Myxofibrosarcoma of the Chest Wall Detected on 99mTc-MDP Whole-Body Bone Scan

Myxofibrosarcoma is a type of soft tissue sarcoma, predominantly characterized by a high propensity for local recurrence, albeit demonstrating a relatively diminished risk for distant metastasis. Its prevalence is notably higher in elderly patients. Here, we present a case of a 73-year-old woman diagnosed with Myxofibrosarcoma. She was subjected to a whole-body bone scan using 99mTc-methylene diphosphonate (MDP) to survey potential bony metastasis. It revealed marked MDP accumulation with peripheral soft tissue uptake in the right lateral chest region of this patient. This imaging phenotype could potentially be attributed to the augmented vascularity within the tumor, a finding that was prominently displayed in this particular case.

Immune Response Related to Lymphadenopathy Post COVID-19 Vaccination

Mass vaccination against coronavirus disease 2019 (COVID-19) is a global health strategy to control the COVID-19 pandemic. With the increasing number of vaccinations, COVID-19 vaccine-associated lymphadenopathy (C19-VAL) has been frequently reported. Current findings emphasize the characteristics of C19-VAL. The mechanism of C19-VAL is complicated to explore. Accumulated reports separately show that C19-VAL incidence is associated with receiver age and gender, reactive change within lymph nodes (LN), etc. We constructed a systematic review to evaluate the associated elements of C19-VAL and provide the mechanism of C19-VAL. Articles were searched from PubMed, Web of Science and EMBASE by using the processing of PRISMA. The search terms included combinations of the COVID-19 vaccine, COVID-19 vaccination and lymphadenopathy. Finally, sixty-two articles have been included in this study. Our results show that days post-vaccination and B cell germinal center response are negatively correlated with C19-VAL incidence. The reactive change within LN is highly related to C19-VAL development. The study results suggested that strong vaccine immune response may contribute to the C19-VAL development and perhaps through the B cell germinal center response post vaccination. From the perspective of imaging interpretation, it is important to carefully distinguish reactive lymph nodes from metastatic lymph node enlargement through medical history collection or evaluation, especially in patients with underlying malignancy.

Reversing "Flip-Flop" Phenomenon of 131 I and Glucose Avidity in RET-Fusion Positive Radioiodine-Refractory Thyroid Cancer Lesions After Treatment of Pralsetinib

ABSTRACT

A 61-year-old man presented with papillary thyroid cancer in radioiodine-refractory status after high-activity 131 I treatments following thyroidectomy. FDG-avid neck and pulmonary metastases but without 131 I-uptake were detected. CCDC6-RET fusion was identified from the tumor lesion. He was treated with pralsetinib, a RET inhibitor, followed by another high-activity 131 I therapy. Posttherapeutic scan displayed restoration of 131 I avidity at those lesions only shown on previous FDG PET/CT. Reduced FDG avidity of those lesions and decreased serum antithyroglobulin antibody titer were also noticed. This case illustrated successfully reinduced 131 I avidity in papillary thyroid cancer through redifferentiation with target therapy to suppress tumor RET overexpression.

A Rare Adverse Effect of the COVID-19 Vaccine on Autoimmune Encephalitis

Since countries commenced COVID-19 vaccination around the world, many vaccine-related adverse effects have been reported. Among them, short-term memory loss with autoimmune encephalitis (AE) was reported as a rare adverse effect. Since case numbers are limited, this brief report may draw the attention of the medical community to this uncommon adverse effect and serve as a reference for future vaccine improvement. However, given the high risk of adverse outcomes when infected with SARS-CoV-2 and the clearly favorable safety/tolerability profile of existing vaccines, vaccination is still recommended.

Hydrogels: Properties and Applications in Biomedicine

Hydrogels are crosslinked polymer chains with three-dimensional (3D) network structures, which can absorb relatively large amounts of fluid. Because of the high water content, soft structure, and porosity of hydrogels, they closely resemble living tissues. Research in recent years shows that hydrogels have been applied in various fields, such as agriculture, biomaterials, the food industry, drug delivery, tissue engineering, and regenerative medicine. Along with the underlying technology improvements of hydrogel development, hydrogels can be expected to be applied in more fields. Although not all hydrogels have good biodegradability and biocompatibility, such as synthetic hydrogels (polyvinyl alcohol, polyacrylamide, polyethylene glycol hydrogels, etc.), their biodegradability and biocompatibility can be adjusted by modification of their functional group or incorporation of natural polymers. Hence, scientists are still interested in the biomedical applications of hydrogels due to their creative adjustability for different uses. In this review, we first introduce the basic information of hydrogels, such as structure, classification, and synthesis. Then, we further describe the recent applications of hydrogels in 3D cell cultures, drug delivery, wound dressing, and tissue engineering.

Incidental Finding of Moderna COVID-19 Vaccination-Related Axillary Lymphadenopathy on 201Tl Myocardial Perfusion Imaging

ABSTRACT

We presented here a 71-year-old man with a history of thyroid cancer post total thyroidectomy and 131I ablation and right renal cell carcinoma post right partial nephrectomy. He reported persistent chest tightness and pain after the first dose of the Moderna COVID-19 (mRNA-1273) vaccine. Thus, coronary heart disease was suspected, and the patient was referred for MPI (myocardial perfusion imaging). Focal 201Tl uptake in the left axillary region was found incidentally on MPI, and SPECT/CT revealed enlarged benign-looking lymph nodes. The diagnostic is in favor of reactive hyperplasia after the intramuscular injection of vaccine into left deltoid muscle.

The Effects of Heterologous Immunization with Prime-Boost COVID-19 Vaccination against SARS-CoV-2

Coronavirus Disease 2019 (COVID-19) pandemic, which is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has become the global challenge. Reaching global herd immunity will help end the COVID-19 pandemic. However, vaccine shortage and vaccine hesitancy are the obstacles to achieve global herd immunity against SARS-CoV-2. The current homologous vaccine regimen is experimentally switching to heterologous vaccination at several study sites. However, the reactogenicity of heterologous ChAdOx1-S and mRNA vaccination against SARS-CoV-2 is still unclear. We have conducted a systematic review to summarize the current findings on the safety and immunogenicity of this heterologous vaccination and elucidate their implications against SARS-CoV-2. This systematic review was conducted by the guidelines of PRISMA. Articles were searched from PubMed and other sources (MedRixv and Google scholar) starting from 1 January to 5 September 2021. The search term was heterologous ChAdOx1-S and BNT162b2 or mRNA-1273 vaccination. Our review found that participants with ChAdOx1/BNT162b2, ChAdOx1-S/mRNA-1273 or BNT162b2/ChAdOx1-S did not have the serious adverse events seen with homologous vaccination. Participants with the heterologous regimen (ChAdOx1/BNT162b2, ChAdOx1-S/mRNA-1273 or BNT162b2/ChAdOx1-S), compared with those with two doses of ChAdOx1-S, have shown a more robust immune responses against SARS-CoV-2, such as higher levels of responsive antibodies or increased numbers of spike-specific T-cells. Nevertheless, these immune responses were slightly diminished in the recipients of BNT162b2/ChAdOx1-S. Also, the safety study of heterologous ChAdOx1-S/mRNA vaccination was based on small populations. Further studies to enclose diverse categories, such as race/ethnicity or geography, may be necessary. Overall, the heterologous immunization with ChAdOX1-S and the mRNA vaccine may improve the vaccine shortage related slow pace of reaching herd immunity, especially using the heterologous immunization with ChAdOx1-S/BNT162b2.

Standardization in Quantitative Imaging: A Multicenter Comparison of Radiomic Features from Different Software Packages on Digital Reference Objects and Patient Data Sets

Radiomic features are being increasingly studied for clinical applications. We aimed to assess the agreement among radiomic features when computed by several groups by using different software packages under very tightly controlled conditions, which included standardized feature definitions and common image data sets. Ten sites (9 from the NCI's Quantitative Imaging Network] positron emission tomography–computed tomography working group plus one site from outside that group) participated in this project. Nine common quantitative imaging features were selected for comparison including features that describe morphology, intensity, shape, and texture. The common image data sets were: three 3D digital reference objects (DROs) and 10 patient image scans from the Lung Image Database Consortium data set using a specific lesion in each scan. Each object (DRO or lesion) was accompanied by an already-defined volume of interest, from which the features were calculated. Feature values for each object (DRO or lesion) were reported. The coefficient of variation (CV), expressed as a percentage, was calculated across software packages for each feature on each object. Thirteen sets of results were obtained for the DROs and patient data sets. Five of the 9 features showed excellent agreement with CV < 1%; 1 feature had moderate agreement (CV < 10%), and 3 features had larger variations (CV ≥ 10%) even after attempts at harmonization of feature calculations. This work highlights the value of feature definition standardization as well as the need to further clarify definitions for some features.

Evaluation of the Image Feature Analysis Techniques by Image Strength Fluctuation for Alzheimer’s Disease

This article compares the effectiveness of both image strength fluctuation (ISF) and statistical parametric mapping (SPM), which is used for analyzing cerebral blood flow traced by single photon emission computed tomography (SPECT) in patients with Alzheimer’s disease (AD). This is a retrospective study that uses the built-in SPECT template of the SPM software to analyze the differences in cerebral blood flow (CBF) between two groups after using the ISF and SPM software to normalize and smooth the data. Using Z score analysis, the ISF method revealed unusual fluctuations in the range of image strength. The hot and cold areas were considered to be the difference between normal and abnormal images and the analysis of the gray intensities of cold and hot areas was used to show that cold and hot were in characteristic locations that correlate with brain functional areas. The results of the SPM analysis revealed significantly reduced blood flow in the bilateral inferior parietal lobule, the middle or superior gyrus of the left temporal lobe, the superior gyrus and the sub-lobar insula of the right temporal lobe. The results from SPM were similar to those obtained with the ISF and indicated the same regions (83.3%).

Incidental Finding of Bilateral Thalamic Glioma on 99mTc TRODAT-1 SPECT/CT

ABSTRACT

99mTc-TRODAT-1 SPECT/CT has been used to evaluate parkinsonian disorders. We present an interesting case of a 63-year-old woman with progressive tremulousness over the left side of the body for 6 months. Bilateral thalamic glioma with reduced uptake was shown on 99mTc-TRODAT-1 SPECT/CT. Secondary parkinsonism was impressed in this case.

Comparison of irregular flux viewer system with BONENAVI version for identification of Tc-99m MDP whole body bone scan metastasis images

The Tc-99m methylene diphosphonate (MDP) whole body bone scan (WBBS) has been widely accepted as a method of choice for the initial diagnosis of bone and joint changes in patients with oncologic diseases. The WBBS has shown high sensitivity but relatively low specificity due to bone variation. This study aims to use the self-developing irregular flux viewer (IFV) system to predict possible bone lesions in planar WBBS. The study uses gradient vector flow (GVF) and self-organizing map (SOM) methods to analyze the blood fluid-dynamics and evaluate hot points. The evaluation includes a selection of 368 patients with bone metastasis from prostate cancer, lung cancer and breast cancer. Finally, we compare IFV values with BONENAVI version data. BONENAVI is a computer-assisted diagnosis system that analyzes bone scintigraphy automatically. The analysis shows that the IFV system achieves sensitivities of 93% for prostate cancer, 91% for breast cancer, and 83% for lung cancer, respectively. On the other hand, our proposed approach achieves a higher sensitivity than the results of BONEVAVI version 2.0.5 for prostate cancer (88%), breast cancer (86%) and lung cancer (82%), respectively. The study results demonstrate that the high sensitivity and specificity of the IFV system can provide assistance for image interpretation and generate prediction values for WBBS.

Comparison of FDG-PET/CT for Cancer Detection in Populations With Different Risks of Underlying Malignancy

Background/Aim: Whole-body positron-emission tomography/computed tomography with the glucose analog 2-[ 18 F]fluoro-2-deoxy-D-glucose (FDG-PET/CT) has been used to screen examinees for underlying malignancy in many countries. The aim of this study was to compare the potential value of FDG-PET/CT application in asymptomatic individuals with those with suspected malignancy. Patients and Methods: A total of 9,408 examinees underwent whole-body FDG-PET/CT at our hospital from July 2006 to August 2013. Three thousand and seven hundred asymptomatic individuals and 848 individuals with laboratory and clinical/radiologicaI suspicion of malignancy who had undergone FDG-PET/CT for cancer screening were recruited. The final confirmation of cancer and outcomes were based on a pathological report and continuous follow-up. Results: Forty-five out of 3,700 asymptomatic individuals (1.2%) had proven malignancy, and 42 of them (93.3%) were found by FDG-PET/CT. Two hundred and twelve out of 848 with suspected malignancy (25%) had proven malignancy, and 196 of them (92.5%) were detected by FDG-PET/CT. Most of these cancers in asymptomatic individuals were clinically at an early stage. The discovery rate in asymptomatic individuals and those with suspected malignancy was 1.1% and 23.1%, respectively. The overall survival of patients with cancer diagnosed with PET/CT was higher than those with suspected malignancy (78.6% vs. 48.5%, p<0.001). Patients with a resectable lesion, early-stage disease, and lower maximal standardized uptake value had significantly better survival than those without. Conclusion: FDG-PET/CT is useful in the early diagnosis of cancer and thus might improve the survival rates of these patients. Considering the costs and risk of radiation exposure, it would be better used as a priority in patients with laboratory and clinical/radiologic suspicion of malignancy.

Spontaneous orbital subcutaneous emphysema mimicking lacrimal duct obstruction after sneezing: A case report

Introduction:

Orbital subcutaneous emphysema after trauma has been carefully reported, but its development in the absence of trauma is rare.

Case presentation:

We report on a 70-year-old patient who developed unilateral orbital subcutaneous emphysema, mimicking lacrimal duct occlusion, after this man sneezed, and presented with right crepitant eyelid swelling and progressive ptosis. Orbital subcutaneous emphysema develops when air can get into the periorbital soft tissue, which presents as a result of facial bone trauma, iatrogenic procedures, and gas-forming infectious microorganisms, as seen in many published articles. It is very uncommon to see this kind of case report after sneezing; however, in our case, spontaneous orbital subcutaneous emphysema occurred after sneezing that resolved slowly after a few weeks without surgical intervention. Our patient denied any painful sensation over the right orbital area, including no visual problems with the right eye but right eyeball limited movements. It can be stressful to patients due to its symptoms but is not a true emergency.

Conclusion:

In this article, we should bring awareness to physicians of the possibility of a spontaneously orbital subcutaneous emphysema with complications after sneezing, yielding relevant information for patients to be informed about avoid excessive nose blowing or occluding the nose, creating controlled symptoms.

Posterior reversible encephalopathy syndrome in a postpartum hemorrhagic woman without hypertension: A case report

RATIONALE

Posterior reversible encephalopathy syndrome (PRES), which diagnosis is based on clinical symptoms and radiological features, is a neurotoxic disease characterized by a set of clinical manifestations, such as seizure, headache, visual, and/or consciousness disturbance. It is the first case of PRES followed by postpartum hemorrhage (PPH) without underlying disease.

PATIENT CONCERNS

A 37-year-old healthy woman had PPH after caesarean section. Six days after delivery, headache occurred suddenly, followed by episodes of clonus seizure.

DIAGNOSES

Brain computed tomography showed ischemic stroke. However, magnetic resonance imaging revealed characteristics consistent with PRES.

INTERVENTIONS

The patient received phenytoin for seizure control.

OUTCOMES

Seizure was under good control over the following days. Three months later, repeated magnetic resonance imaging showed complete remission.

LESSONS

PRES may be triggered by PPH and is not necessarily secondary to typical predisposing factors such as hypertension or pre/eclampsia. Hormone fluctuation, increased blood pressure variation, and massive blood transfusion may be contributed to the development of PRES in our case. Also, it is necessary to rule out those life-threatening diseases, such as cavernoma hemorrhage, cerebral venous thrombosis, and ischemic stroke before the diagnosis of PRES.

Application of whole-body FDG-PET for cancer screening in a cohort of hospital employees

Whole-body positron emission tomography/computed tomography with the glucose analog 2-[F]fluoro-2-deoxy-D-glucose (FDG-PET/CT) has been extensively used to screen for underlying malignancies in asymptomatic individuals. We were able to survey a cohort of hospital employees using FDG-PET/CT and to report the results herein.A total of 116 hospital employees older than 55 years old were offered whole-body FDG-PET in our hospital. Ninety-seven employees (83.6%) completed the assessment from February 2014 to August 2014 in our PET center. The final confirmation of cancer was based on pathologic examination and follow-up after more than 1 year.Among the 97 participants, 92 were asymptomatic and 5 presented with previously diagnosed cancers. Six of the 92 asymptomatic participants (6.6%) with significant nodular lesions were referred for histological or cytological evaluation of the possibility of malignancy, and 1 case was considered clinically important and required surgical resection. The cancer discovery rate was 3.3% (3/92) with positive predictive value of 50% (3/6). In the 5 participants with previously identified cancers, no recurrence or metastasis was detected.The offer of whole-body FDG-PET for cancer screening was welcomed with enthusiasm by most of the hospital employees. PET/CT combines the merits of PET and CT and can be administered to and provide benefits to a select group of hospital employees.

Added value of using a cocktail of F-18 sodium fluoride and F-18 fluorodeoxyglucose in positron emission tomography/computed tomography for detecting bony metastasis: a case report

Current nuclear imaging of the skeletal system is achieved using technetium-99m (Tc-99m) methylene diphosphonate (MDP), F-18 sodium fluoride (NaF), or F-18 fluorodeoxyglucose (FDG). However, comparisons of these are rare in the literature. We present a case of a 51-year-old female with suspicious lung cancer due to main symptoms of dyspnea, nonproductive cough, and pleural pain. Tc-99m MDP whole-body bone scan (WBBS) showed multiple bony metastases. Five days later, positron emission tomography/computed tomography (PET/CT) images using both F-18 NaF and a cocktail of F-18 NaF and F-18 FDG were obtained on the same day 2 hours apart. The former showed more foci and precisely showed bony lesions compared to those obtained using Tc-99m MDP WBBS. However, the latter demonstrated more extensive radiotracer uptake, especially in osteolytic lesions, and additional soft tissue lesions in the left axillary and surpraclavicular nodes as well as the left pleura. Surgical biopsy was performed in left axillary nodes, and the metastatic carcinoma was found to be of breast origin. This case demonstrated that a cocktail of F-18 NaF and F-18 FDG could be useful in PET/CT for not only detecting more skeletal lesions but also guiding biopsies accurately to the affected tissue.

Polymer planar waveguide device using inverted channel structure with upper liquid crystal cladding

We propose a composite waveguide configuration based on an inverted polymer channel structure with upper nematic liquid crystal cladding. This configuration can achieve a more homogenous liquid crystal molecular alignment between the core and the liquid crystal material by minimizing the rubbing damage during preparation of the alignment layer. We demonstrated our idea with a variable optical attenuator which exhibited a 24 dB of attenuation range over a tuning peak voltage of 10 V at 1550 nm.

Chondrodermatitis nodularis chronica helicis in monozygotic twins

Chondrodermatitis nodularis chronica helicis (CNCH) is a benign inflammatory nodule of the helix. Patients report severe tenderness upon pressure. Commonly seen in middle-aged men, there are no reports of this disease in twins. We report middle-aged male monozygotic twins who simultaneously developed CNCH. This suggests, but does not prove, the possibility of a hereditary factor in the pathogenesis of CNCH.

Skin decontamination of glyphosate from human skin in vitro

This study compared three model decontaminant solutions (tap water, isotonic saline, and hypertonic saline) for their ability to remove a model herbicide (glyphosate) from an in vitro human skin model. Human cadaver skin was dosed (approximately 375microg) of [14C]-glyphosate on 3cm2 per skin. After each exposure time (1, 3, and 30min post-dosing, respectively), the surface skin was washed three times (4ml per time) with each solution. After washing, the skin was stripped twice with tape discs. Lastly, the wash solutions, strippings, receptor fluid, and remainder of skin were liquid scintillation analyzer counted to determine the amount of glyphosate. There were no statistical differences among these groups at any time points. The total mass balance recovery at three time exposure points was between 94.8% and 102.4%. The wash off rates (glyphosate in wash solutions) at three different exposure times is 79-101.2%. Thus the three tested decontaminants possess similar effectiveness in removing glyphosate from skin. This in vitro model is not only economic and rapid, but also provides quantitative data that may aid screening for optimal decontaminants.

Effects of antioxidants on pyridinoline cross-link formation in culture supernatants of fibroblasts from normal skin and hypertrophic scars

Free radicals are normally generated in many metabolic pathways. They are closely associated with inflammatory diseases. The aim of this study was to investigate the effects of free radicals and their antioxidants on the formation of pyridinoline using human fibroblasts from normal skin and hypertrophic scars. The significance of the increase in pyridinoline cross-links is that large quantities have been found in hypertrophic scars formed post-burn than in normal skin, and that catalase was effective in reducing the pyridinoline cross-link formation in hypertrophic scars. The pyridinoline cross-link concentration expressed in nM/ micro g hydroxyl proline was found to be higher in the culture supernatants of the fibroblasts from hypertrophic scars (9.04 +/- 2.74) than that of normal skin (7.55 +/- 2.1). When the human fibroblasts from normal skin and hypertrophic scar were subject to hydroxyl radicals generated by the Fenton reaction, there was no significant increase in pyridinoline cross-link concentration (nm/ micro g hydroxyl proline) in the supernatants compared with the control. When the controls plus various treatments with free radicals were subject to different antioxidants, including superoxide dismutase, catalase, glutathione peroxidase, and desferrioxamine, it was found that catalase alone was most effective in scavenging hydroxyl radicals as determined by the decrease in pyridinoline cross-links.

Chronic exercise improves endothelial calcium signaling and vasodilatation in hypercholesterolemic rabbit femoral artery

OBJECTIVE

This study was to investigate the effects of chronic exercise on vasodilatation and endothelial intracellular calcium (EC [Ca2+]i) signaling in atherosclerotic animals.

METHODS AND RESULTS

For 8 weeks, male New Zealand White rabbits were fed rabbit chow with or without the addition of 2% cholesterol. They were further divided into control and exercise groups. Animals in the exercise groups ran on a leveled treadmill at 0.88 km/h for 10 to 60 minutes gradually for 5 days per week for a total of 8 weeks. At the end of experiments, femoral arteries were dissected, loaded with fura 2-AM, and mounted in a tissue flow chamber. PE-precontracted vessel specimens were exposed to acetylcholine (ACh). The EC [Ca2+]i elevation and vasorelaxation were determined simultaneously under an epifluorescence microscope equipped with a ratio-imaging capability. Our results showed the following: (1) high cholesterol diet feeding caused lipid deposition on vascular surface, reduced the ACh-evoked EC [Ca2+]i elevation, and impaired endothelium-dependent and endothelium-independent vascular responses, but chronic exercise had the opposite effects; (2) ACh-induced vasorelaxation was associated with EC [Ca2+]i elevation in all groups; and (3) vasorelaxation at high levels of EC [Ca2+]i elevation decreased in hypercholesterolemia.

CONCLUSIONS

Our data suggest that hypercholesterolemia induces vascular structural changes and impairs EC [Ca2+]i signaling and vasodilatation, whereas chronic exercise partially reverses these adverse effects.

Acute exercise enhances vasorelaxation by modulating endothelial calcium signaling in rat aortas

The role of endothelial calcium signaling in exercise-enhanced ACh-induced vasorelaxation was examined using male Wistar rats (8~10 wk old) that were divided into control and exercise groups. The exercised animals ran on a treadmill with progressive increments of speed until exhaustion. After decapitation, aortic rings were dissected and loaded with fura 2-AM. After being mounted on a tissue flow chamber, vessels were precontracted with phenylephrine, and ACh-induced endothelial calcium elevation and vasorelaxation were determined simultaneously under an epifluorescence microscope equipped with ratio imaging capability. Our results showed that 1) there was logarithmic correlation between endothelial calcium elevation and vasorelaxation; 2) acute exercise enhanced ACh-induced endothelial calcium elevation and vasorelaxation without altering their relationship; 3) pretreatment with N(omega)-nitro-L-arginine markedly reduced ACh-induced vasorelaxation in both groups but suppressed the calcium response only in the exercise group; and 4) the exercise effect on endothelial calcium elevation was abolished by Ca2+-free buffer or gadolinium. In conclusion, acute exercise increases ACh-induced vasorelaxation by increasing the endothelial calcium influx and the calcium-dependent nitric oxide release.

Computer-aided characterization of mammographic masses: accuracy of mass segmentation and its effects on characterization

Mass segmentation is used as the first step in many computer-aided diagnosis (CAD) systems for classification of breast masses as malignant or benign. The goal of this paper was to study the accuracy of an automated mass segmentation method developed in our laboratory, and to investigate the effect of the segmentation stage on the overall classification accuracy. The automated segmentation method was quantitatively compared with manual segmentation by two expert radiologists (R1 and R2) using three similarity or distance measures on a data set of 100 masses. The area overlap measures between R1 and R2, the computer and R1, and the computer and R2 were 0.76 +/- 0.13, 0.74 +/- 0.11, and 0.74 +/- 0.13, respectively. The interobserver difference in these measures between the two radiologists was compared with the corresponding differences between the computer and the radiologists. Using three similarity measures and data from two radiologists, a total of six statistical tests were performed. The difference between the computer and the radiologist segmentation was significantly larger than the interobserver variability in only one test. Two sets of texture, morphological, and spiculation features, one based on the computer segmentation, and the other based on radiologist segmentation, were extracted from a data set of 249 films from 102 patients. A classifier based on stepwise feature selection and linear discriminant analysis was trained and tested using the two feature sets. The leave-one-case-out method was used for data sampling. For case-based classification, the area Az under the receiver operating characteristic (ROC) curve was 0.89 and 0.88 for the feature sets based on the radiologist segmentation and computer segmentation, respectively. The difference between the two ROC curves was not statistically significant.

Analysis of temporal changes of mammographic features: computer-aided classification of malignant and benign breast masses

A new classification scheme was developed to classify mammographic masses as malignant and benign by using interval change information. The masses on both the current and the prior mammograms were automatically segmented using an active contour method. From each mass, 20 run length statistics (RLS) texture features, 3 speculation features, and 12 morphological features were extracted. Additionally, 20 difference RLS features were obtained by subtracting the prior RLS features from the corresponding current RLS features. The feature space consisted of the current RLS features, the difference RLS features, the current and prior speculation features, and the current and prior mass sizes. Stepwise feature selection and linear discriminant analysis classification were used to select and merge the most useful features. A leave-one-case-out resampling scheme was used to train and test the classifier using 140 temporal image pairs (85 malignant, 55 benign) obtained from 57 biopsy-proven masses (33 malignant, 24 benign) in 56 patients. An average of 10 features were selected from the 56 training subsets: 4 difference RLS features, 4 RLS features, and 1 speculation feature from the current image, and 1 speculation feature from the prior, were most often chosen. The classifier achieved an average training Az of 0.92 and a test Az of 0.88. For comparison, a classifier was trained and tested using features extracted from the 120 current single images. This classifier achieved an average training Az of 0.90 and a test Az of 0.82. The information on the prior image significantly (p = 0.015) improved the accuracy for classification of the masses.

Selection of an optimal neural network architecture for computer-aided detection of microcalcifications--comparison of automated optimization techniques

Many computer-aided diagnosis (CAD) systems use neural networks (NNs) for either detection or classification of abnormalities. Currently, most NNs are "optimized" by manual search in a very limited parameter space. In this work, we evaluated the use of automated optimization methods for selecting an optimal convolution neural network (CNN) architecture. Three automated methods, the steepest descent (SD), the simulated annealing (SA), and the genetic algorithm (GA), were compared. We used as an example the CNN that classifies true and false microcalcifications detected on digitized mammograms by a prescreening algorithm. Four parameters of the CNN architecture were considered for optimization, the numbers of node groups and the filter kernel sizes in the first and second hidden layers, resulting in a search space of 432 possible architectures. The area Az under the receiver operating characteristic (ROC) curve was used to design a cost function. The SA experiments were conducted with four different annealing schedules. Three different parent selection methods were compared for the GA experiments. An available data set was split into two groups with approximately equal number of samples. By using the two groups alternately for training and testing, two different cost surfaces were evaluated. For the first cost surface, the SD method was trapped in a local minimum 91% (392/432) of the time. The SA using the Boltzman schedule selected the best architecture after evaluating, on average, 167 architectures. The GA achieved its best performance with linearly scaled roulette-wheel parent selection; however, it evaluated 391 different architectures, on average, to find the best one. The second cost surface contained no local minimum. For this surface, a simple SD algorithm could quickly find the global minimum, but the SA with the very fast reannealing schedule was still the most efficient. The same SA scheme, however, was trapped in a local minimum on the first cost surface. Our CNN study demonstrated that, if optimization is to be performed on a cost surface whose characteristics are not known a priori, it is advisable that a moderately fast algorithm such as a SA using a Boltzman cooling schedule be used to conduct an efficient and thorough search, which may offer a better chance of reaching the global minimum.

Improvement of mammographic mass characterization using spiculation meausures and morphological features

We are developing new computer vision techniques for characterization of breast masses on mammograms. We had previously developed a characterization method based on texture features. The goal of the present work was to improve our characterization method by making use of morphological features. Toward this goal, we have developed a fully automated, three-stage segmentation method that includes clustering, active contour, and spiculation detection stages. After segmentation, morphological features describing the shape of the mass were extracted. Texture features were also extracted from a band of pixels surrounding the mass. Stepwise feature selection and linear discriminant analysis were employed in the morphological, texture, and combined feature spaces for classifier design. The classification accuracy was evaluated using the area Az under the receiver operating characteristic curve. A data set containing 249 films from 102 patients was used. When the leave-one-case-out method was applied to partition the data set into trainers and testers, the average test Az for the task of classifying the mass on a single mammographic view was 0.83 +/- 0.02, 0.84 +/- 0.02, and 0.87 +/- 0.02 in the morphological, texture, and combined feature spaces, respectively. The improvement obtained by supplementing texture features with morphological features in classification was statistically significant (p = 0.04). For classifying a mass as malignant or benign, we combined the leave-one-case-out discriminant scores from different views of a mass to obtain a summary score. In this task, the test Az value using the combined feature space was 0.91 +/- 0.02. Our results indicate that combining texture features with morphological features extracted from automatically segmented mass boundaries will be an effective approach for computer-aided characterization of mammographic masses.

Analysis of uncertainties in estimates of components of variance in multivariate ROC analysis

RATIONALE AND OBJECTIVES

Solutions have previously been presented to the problem of estimating the components of variance in the general linear model used for multivariate receiver operating characteristic (ROC) analysis. The case where the variance components do not change across the modalities under comparison was first treated, followed by the case where they are permitted to change. No analysis of uncertainties in these estimates has been presented previously.

MATERIALS AND METHODS

For the case where the variance components do not change across modalities, the "jackknife-after-bootstrap" resampling procedure can be used together with conventional linear propagation of variance to solve for the uncertainties in estimates of the components. For the case where the components are permitted to change across modalities, a slight elaboration of this procedure is presented.

RESULTS

The approach was validated by Monte Carlo simulations, where uncertainties in estimates of the variance components calculated by the jackknife-after-bootstrap procedure were found to converge in the mean to the Monte Carlo results over many independent trials. The method is exemplified with data from a study of readers-with and without the aid of a computer-assist modality-given the task of discriminating benign from malignant masses in mammography.

CONCLUSION

The present approach is relevant to a broad class of problems where estimates of multiple contributions to the variance observed in ROC assessment of diagnostic modalities are desired, in particular, for the assessment of multiple-reader studies of computer-aided diagnosis in radiology where the variance components may change across reading modalities (eg, unaided vs computer-aided reading).

Automated registration of breast lesions in temporal pairs of mammograms for interval change analysis--local affine transformation for improved localization

Analysis of interval change is important for mammographic interpretation. The aim of this study is to evaluate the use of an automated registration technique for computer-aided interval change analysis in mammography. Previously we developed a regional registration technique for identifying masses on temporal pairs of mammograms. In the current study, we improved lesion registration by including a local alignment step. Initially, the lesion position on the prior mammogram was estimated based on the breast geometry. An initial fan-shaped search region was then defined on the prior mammogram. In the second stage, the location of the fan-shaped region on the prior mammogram was refined by warping, based on an affine transformation and simplex optimization in a local region. In the third stage, a search for the best match between the lesion template from the current mammogram and a structure on the prior mammogram was carried out within the search region. This technique was evaluated on 124 temporal pairs of mammograms containing biopsyproven masses. Eighty-seven percent of the estimated lesion locations resulted in an area overlap of at least 50% with the true lesion locations and an average distance of 2.4 +/- 2.1 mm between their centroids. The average distance between the estimated and the true centroid of the lesions on the prior mammogram over all 124 temporal pairs was 4.2 +/- 5.7 mm. The registration accuracy was improved in comparison with our previous study that used a data set of 74 temporal pairs of mammograms. This improvement in accuracy resulted from the improved geometry estimation and the local affine transformation.

Computerized image analysis: estimation of breast density on mammograms

An automated image analysis tool is being developed for the estimation of mammographic breast density. This tool may be useful for risk estimation or for monitoring breast density change in prevention or intervention programs. In this preliminary study, a data set of 4-view mammograms from 65 patients was used to evaluate our approach. Breast density analysis was performed on the digitized mammograms in three stages. First, the breast region was segmented from the surrounding background by an automated breast boundary-tracking algorithm. Second, an adaptive dynamic range compression technique was applied to the breast image to reduce the range of the gray level distribution in the low frequency background and to enhance the differences in the characteristic features of the gray level histogram for breasts of different densities. Third, rule-based classification was used to classify the breast images into four classes according to the characteristic features of their gray level histogram. For each image, a gray level threshold was automatically determined to segment the dense tissue from the breast region. The area of segmented dense tissue as a percentage of the breast area was then estimated. To evaluate the performance of the algorithm, the computer segmentation results were compared to manual segmentation with interactive thresholding by five radiologists. A "true" percent dense area for each mammogram was obtained by averaging the manually segmented areas of the radiologists. We found that the histograms of 6% (8 CC and 8 MLO views) of the breast regions were misclassified by the computer, resulting in poor segmentation of the dense region. For the images with correct classification, the correlation between the computer-estimated percent dense area and the "truth" was 0.94 and 0.91, respectively, for CC and MLO views, with a mean bias of less than 2%. The mean biases of the five radiologists' visual estimates for the same images ranged from 0.1% to 11%. The results demonstrate the feasibility of estimating mammographic breast density using computer vision techniques and its potential to improve the accuracy and reproducibility of breast density estimation in comparison with the subjective visual assessment by radiologists.

Digital mammography: observer performance study of the effects of pixel size on the characterization of malignant and benign microcalcifications

RATIONALE AND OBJECTIVES

The authors performed this study to evaluate the effects of pixel size on the characterization of mammographic microcalcifications by radiologists.

MATERIALS AND METHODS

Two-view mammograms of 112 microcalcification clusters were digitized with a laser scanner at a pixel size of 35 microm. Images with pixel sizes of 70, 105, and 140 microm were derived from the 35-microm-pixel size images by averaging neighboring pixels. The malignancy or benignity of the microcalcifications had been determined with findings at biopsy or 2-year follow-up. Region-of-interest images containing the microcalcifications were printed with a laser imager. Seven radiologists participated in a receiver operating characteristic (ROC) study to estimate the likelihood of malignancy. The classification accuracy was quantified with the area under the ROC curve (Az). The statistical significance of the differences in the Az values for different pixel sizes was estimated with the Dorfman-Berbaum-Metz method and the Student paired t test. The variance components were analyzed with a bootstrap method.

RESULTS

The higher-resolution images did not result in better classification; the average Az with a pixel size of 35 microm was lower than that with pixel sizes of 70 and 105 microm. The differences in Az between different pixel sizes did not achieve statistical significance.

CONCLUSION

Pixel sizes in the range studied do not have a strong effect on radiologists' accuracy in the characterization of microcalcifications. The low specificity of the image features of microcalcifications and the large interobserver and intraobserver variabilities may have prevented small advantages in image resolution from being observed.

Phototimer setup for CR imaging

A study was performed to investigate the feasibility of using the standard deviation (sigma) of the pixel values in a computed radiography (CR) image and a measure of the median incident exposure on the imaging plate (IP) as parameters for setting up phototimers in a CR system. Slabs of Lucite 4-, 6-, and 8-in.-thick were imaged with a CR system at 70, 90, and 125 kVp at various mA s values both with grid and without grid. Incident IP exposures were measured with an ionization chamber. Images were analyzed on a workstation. The sigma's in the "flat field" images were found to be approximately related to the mean incident exposure E by the relationship: sigma is proportional to E(-1/2), indicating the quantum-noise-limited operation of the system. Derived relationships between the reading sensitivity of the (IP) reader (S number) and sigma can be used to obtain images with a specific noise level. At our institution, where a 400 speed screen-film system is used for general radiography and 200 speed for chest radiography, radiologists generally find CR image quality acceptable when sigma < or = 11 (S< or =400) for general radiography (50-90 kVp), and sigma < or =8 (S< or =200) for chest radiography (125 kVp). However, factors other than the amount of x-ray quanta that form the useful image, such as the image processing mode and the amount of collimation, may affect both the sensitivity value and the image quality.

Adverse effects of increased body weight on quantitative measures of mammographic image quality

OBJECTIVE

The purpose of our study was to show that compressed breast thickness on mammograms in overweight and obese women exceeds the thickness in normal-weight women and that increased thickness results in image degradation.

SUBJECTS AND METHODS

Three hundred consecutive routine mammograms were reviewed. Patients were categorized according to body mass index. Compression thickness, compressive force, kilovoltage, and milliampere-seconds were recorded. Geometric unsharpness and contrast degradation were calculated for each body mass index category.

RESULTS

Body mass index categories were lean (3%), normal (36%), overweight (36%), and obese (25%). Body mass index was directly correlated with compressed thickness. In the mediolateral oblique view, the mean thickness of the obese category exceeded normal thickness by 18 mm (p < 0.01), corresponding to a 32% increase in geometric unsharpness. Mean obese thickness exceeded lean thickness by 33 mm (p < 0.01), corresponding to a 79% increase in unsharpness. Similar trends were observed for the craniocaudal view. In the mediolateral oblique projection, there was an increase of 1.0 kVp (p < 0.01) for obese compared with normal and 1.7 kVp (p < 0.01) between lean and obese, corresponding, respectively, to a 16% and a 25% decrease in image contrast because of scatter and kilovoltage changes. Milliampere-seconds increased by 47% on the mediolateral oblique images in the obese category compared with normal body mass index.

CONCLUSION

An increased body mass index was associated with greater compressed breast thickness, resulting in increased geometric unsharpness, decreased image contrast, and greater potential for motion unsharpness.

Feature selection and classifier performance in computer-aided diagnosis: the effect of finite sample size

In computer-aided diagnosis (CAD), a frequently used approach for distinguishing normal and abnormal cases is first to extract potentially useful features for the classification task. Effective features are then selected from this entire pool of available features. Finally, a classifier is designed using the selected features. In this study, we investigated the effect of finite sample size on classification accuracy when classifier design involves stepwise feature selection in linear discriminant analysis, which is the most commonly used feature selection algorithm for linear classifiers. The feature selection and the classifier coefficient estimation steps were considered to be cascading stages in the classifier design process. We compared the performance of the classifier when feature selection was performed on the design samples alone and on the entire set of available samples, which consisted of design and test samples. The area Az under the receiver operating characteristic curve was used as our performance measure. After linear classifier coefficient estimation using the design samples, we studied the hold-out and resubstitution performance estimates. The two classes were assumed to have multidimensional Gaussian distributions, with a large number of features available for feature selection. We investigated the dependence of feature selection performance on the covariance matrices and means for the two classes, and examined the effects of sample size, number of available features, and parameters of stepwise feature selection on classifier bias. Our results indicated that the resubstitution estimate was always optimistically biased, except in cases where the parameters of stepwise feature selection were chosen such that too few features were selected by the stepwise procedure. When feature selection was performed using only the design samples, the hold-out estimate was always pessimistically biased. When feature selection was performed using the entire finite sample space, the hold-out estimates could be pessimistically or optimistically biased, depending on the number of features available for selection, the number of available samples, and their statistical distribution. For our simulation conditions, these estimates were always pessimistically (conservatively) biased if the ratio of the total number of available samples per class to the number of available features was greater than five.

Stereomammography: evaluation of depth perception using a virtual 3D cursor

We are evaluating the usefulness of stereomammography in improving breast cancer diagnosis. One area that we are investigating is whether the improved depth perception associated with stereomammography might be significantly enhanced with the use of a virtual 3D cursor. A study was performed to evaluate the accuracy of absolute depth measurements made in stereomammograms with such a cursor. A biopsy unit was used to produce digital stereo images of a phantom containing 50 low contrast fibrils (0.5 mm diam monofilaments) at depths ranging from 1 to 11 mm, with a minimum spacing of 2 mm. Half of the fibrils were oriented perpendicular (vertical) and half parallel (horizontal) to the stereo shift direction. The depth and orientation of each fibril were randomized, and the horizontal and vertical fibrils crossed, simulating overlapping structures in a breast image. Left and right eye images were generated by shifting the x-ray tube from +2.5 degrees to -2.5 degrees relative to the image receptor. Three observers viewed these images on a computer display with stereo glasses and adjusted the position of a cross-shaped virtual cursor to best match the perceived location of each fibril. The x, y, and z positions of the cursor were indicated on the display. The z (depth) coordinate was separately calibrated using known positions of fibrils in the phantom. The observers analyzed images of two configurations of the phantom. Thus, each observer made 50 vertical filament depth measurements and 50 horizontal filament depth measurements. These measurements were compared with the true depths. The correlation coefficients between the measured and true depths of the vertically oriented fibrils for the three observers were 0.99, 0.97, and 0.89 with standard errors of the estimates of 0.39 mm, 0.83 mm, and 1.33 mm, respectively. Corresponding values for the horizontally oriented fibrils were 0.91, 0.28, and 0.08, and 1.87 mm, 4.19 mm, and 3.13 mm. All observers could estimate the absolute depths of vertically oriented objects fairly accurately in digital stereomammograms; however, only one observer was able to accurately estimate the depths of horizontally oriented objects. This may relate to different aptitudes for stereoscopic visualization. The orientations of most objects in actual mammograms are combinations of horizontal and vertical. Further studies are planned to evaluate absolute depth measurements of fibrils oriented at various intermediate angles and of objects of different shapes. The effects of the shape and contrast of the virtual cursor and the stereo shift angle on the accuracy of the depth measurements will also be investigated.

Classifier design for computer-aided diagnosis: effects of finite sample size on the mean performance of classical and neural network classifiers

Classifier design is one of the key steps in the development of computer-aided diagnosis (CAD) algorithms. A classifier is designed with case samples drawn from the patient population. Generally, the sample size available for classifier design is limited, which introduces variance and bias into the performance of the trained classifier, relative to that obtained with an infinite sample size. For CAD applications, a commonly used performance index for a classifier is the area, Az, under the receiver operating characteristic (ROC) curve. We have conducted a computer simulation study to investigate the dependence of the mean performance, in terms of Az, on design sample size for a linear discriminant and two nonlinear classifiers, the quadratic discriminant and the backpropagation neural network (ANN). The performances of the classifiers were compared for four types of class distributions that have specific properties: multivariate normal distributions with equal covariance matrices and unequal means, unequal covariance matrices and unequal means, and unequal covariance matrices and equal means, and a feature space where the two classes were uniformly distributed in disjoint checkerboard regions. We evaluated the performances of the classifiers in feature spaces of dimensionality ranging from 3 to 15, and design sample sizes from 20 to 800 per class. The dependence of the resubstitution and hold-out performance on design (training) sample size (Nt) was investigated. For multivariate normal class distributions with equal covariance matrices, the linear discriminant is the optimal classifier. It was found that its Az-versus-1/Nt curves can be closely approximated by linear dependences over the range of sample sizes studied. In the feature spaces with unequal covariance matrices where the quadratic discriminant is optimal, the linear discriminant is inferior to the quadratic discriminant or the ANN when the design sample size is large. However, when the design sample is small, a relatively simple classifier, such as the linear discriminant or an ANN with very few hidden nodes, may be preferred because performance bias increases with the complexity of the classifier. In the regime where the classifier performance is dominated by the 1/Nt term, the performance in the limit of infinite sample size can be estimated as the intercept (1/Nt= 0) of a linear regression of Az versus 1/Nt. The understanding of the performance of the classifiers under the constraint of a finite design sample size is expected to facilitate the selection of a proper classifier for a given classification task and the design of an efficient resampling scheme.

Classification of malignant and benign masses based on hybrid ART2LDA approach

A new type of classifier combining an unsupervised and a supervised model was designed and applied to classification of malignant and benign masses on mammograms. The unsupervised model was based on an adaptive resonance theory (ART2) network which clustered the masses into a number of separate classes. The classes were divided into two types: one containing only malignant masses and the other containing a mix of malignant and benign masses. The masses from the malignant classes were classified by ART2. The masses from the mixed classes were input to a supervised linear discriminant classifier (LDA). In this way, some malignant masses were separated and classified by ART2 and the less distinguishable benign and malignant masses were classified by LDA. For the evaluation of classifier performance, 348 regions of interest (ROI's) containing biopsy proven masses (169 benign and 179 malignant) were used. Ten different partitions of training and test groups were randomly generated using an average of 73% of ROI's for training and 27% for testing. Classifier design, including feature selection and weight optimization, was performed with the training group. The test group was kept independent of the training group. The performance of the hybrid classifier was compared to that of an LDA classifier alone and a backpropagation neural network (BPN). Receiver operating characteristics (ROC) analysis was used to evaluate the accuracy of the classifiers. The average area under the ROC curve (A(z)) for the hybrid classifier was 0.81 as compared to 0.78 for the LDA and 0.80 for the BPN. The partial areas above a true positive fraction of 0.9 were 0.34, 0.27 and 0.31 for the hybrid, the LDA and the BPN classifier, respectively. These results indicate that the hybrid classifier is a promising approach for improving the accuracy of classification in CAD applications.

A regional registration technique for automated interval change analysis of breast lesions on mammograms

Analysis of interval change is a useful technique for detection of abnormalities in mammographic interpretation. Interval change analysis is routinely used by radiologists and its importance is well-established in clinical practice. As a first step to develop a computerized method for interval change analysis on mammograms, we are developing an automated regional registration technique to identify corresponding lesions on temporal pairs of mammograms. In this technique, the breast is first segmented from the background on the current and previous mammograms. The breast edges are then aligned using a global alignment procedure based on the mutual information between the breast regions in the two images. Using the nipple location and the breast centroid estimated independently on both mammograms, a polar coordinate system is defined for each image. The polar coordinate of the centroid of a lesion detected on the most recent mammogram is used to obtain an initial estimate of its location on the previous mammogram and to define a fan-shaped search region. A search for a matching structure to the lesion is then performed in the fan-shaped region on the previous mammogram to obtain a final estimate of its location. In this study, a quantitative evaluation of registration accuracy has been performed with a data set of 74 temporal pairs of mammograms and ground-truth correspondence information provided by an experienced radiologist. The most recent mammogram of each temporal pair exhibited a biopsy-proven mass. We have investigated the usefulness of correlation and mutual information as search criteria for determining corresponding regions on mammograms for the biopsy-proven masses. In 85% of the cases (63/74 temporal pairs) the region on the previous mammogram that corresponded to the mass on the current mammogram was correctly identified. The region centroid identified by the registration technique had an average distance of 2.8+/-1.9 mm from the centroid of the radiologist-identified region. These results indicate that our new registration technique may be useful for establishing correspondence between structures on current and previous mammograms. Once such a correspondence is established an interval change analysis could be performed to aid in both detection as well as classification of abnormal breast densities.

Improvement of radiologists' characterization of mammographic masses by using computer-aided diagnosis: an ROC study

PURPOSE

To evaluate the effects of computer-aided diagnosis (CAD) on radiologists' classification of malignant and benign masses seen on mammograms.

MATERIALS AND METHODS

The authors previously developed an automated computer program for estimation of the relative malignancy rating of masses. In the present study, the authors conducted observer performance experiments with receiver operating characteristic (ROC) methodology to evaluate the effects of computer estimates on radiologists' confidence ratings. Six radiologists assessed biopsy-proved masses with and without CAD. Two experiments, one with a single view and the other with two views, were conducted. The classification accuracy was quantified by using the area under the ROC curve, Az.

RESULTS

For the reading of 238 images, the Az value for the computer classifier was 0.92. The radiologists' Az values ranged from 0.79 to 0.92 without CAD and improved to 0.87-0.96 with CAD. For the reading of a subset of 76 paired views, the radiologists' Az values ranged from 0.88 to 0.95 without CAD and improved to 0.93-0.97 with CAD. Improvements in the reading of the two sets of images were statistically significant (P = .022 and .007, respectively). An improved positive predictive value as a function of the false-negative fraction was predicted from the improved ROC curves.

CONCLUSION

CAD may be useful for assisting radiologists in classification of masses and thereby potentially help reduce unnecessary biopsies.

Design and evaluation of an external filter technique for exposure equalization in mammography

We are developing an external filter method for equalizing x-ray exposure in the peripheral region of the breast. This method requires the use of only a limited number of custom-built filters for different breast shapes in a given view. This paper describes the design methodology for these external filters. The filter effectiveness was evaluated through a simulation study on 171 mediolateral and 196 craniocaudal view digitized mammograms and through imaging of a breast phantom. The degree of match between the simulated filter and the individual 3-D exposure profiles at the breast periphery was quantified. An analysis was performed to investigate the effect of filter misalignment. The simulation study indicates that the filter is effective in equalizing exposures for more than 80% of the breast images in our database. The tolerance in filter misalignment was estimated to be about +/- 2 mm for the CC view and +/- 1 mm for the MLO view at the image plane. Some misalignment artifacts were demonstrated with simulated filtered mammograms.

Combined adaptive enhancement and region-growing segmentation of breast masses on digitized mammograms

As an ongoing effort to develop a computer aid for detection of masses on mammograms, we recently designed an object-based region-growing technique to improve mass segmentation. This segmentation method utilizes the density-weighted contrast enhancement (DWCE) filter as a pre-processing step. The DWCE filter adaptively enhances the contrast between the breast structures and the background. Object-based region growing was then applied to each of the identified structures. The region-growing technique uses gray-scale and gradient information to adjust the initial object borders and to reduce merging between adjacent or overlapping structures. Each object is then classified as a breast mass or normal tissue based on extracted morphological and texture features. In this study we evaluated the sensitivity of this combined segmentation scheme and its ability to reduce false positive (FP) detections on a data set of 253 digitized mammograms, each of which contained a biopsy-proven breast mass. It was found that the segmentation scheme detected 98% of the 253 biopsy-proven breast masses in our data set. After final FP reduction, the detection resulted in 4.2 FP per image at a 90% true positive (TP) fraction and 2.0 FPs per image at an 80% TP fraction. The combined DWCE and object-based region growing technique increased the initial detection sensitivity, reduced merging between neighboring structures, and reduced the number of FP detections in our automated breast mass detection scheme.

Computer-aided diagnosis in mammography: classification of mass and normal tissue by texture analysis

Computer-aided diagnosis schemes are being developed to assist radiologists in mammographic interpretation. In this study, we investigated whether texture features could be used to distinguish between mass and non-mass regions in clinical mammograms. Forty-five regions of interest (ROIs) containing true masses with various degrees of visibility and 135 ROIs containing normal breast parenchyma were extracted manually from digitized mammograms as case samples. Spatial-grey-level-dependence (SGLD) matrices of each ROI were calculated and eight texture features were calculated from the SGLD matrices. The correlation and class-distance properties of extracted texture features were analysed. Selected texture features were input into a modified decision-tree classification scheme. The performance of the classifier was evaluated for different feature combinations and orders of features on the tree. A classification accuracy of about 89% sensitivity and 76% specificity was obtained for ordered features, sum average, correlation, and energy, during the training procedure. With a leave-one-out method, the test result was about 76% sensitivity and 64% specificity. The results of this preliminary study demonstrate the feasibility of using texture information for classification of mass and normal breast tissue, which will be likely to be useful for classifying true and false detections in computer-aided diagnosis programmes.

Computerized analysis of mammographic microcalcifications in morphological and texture feature spaces

We are developing computerized feature extraction and classification methods to analyze malignant and benign microcalcifications on digitized mammograms. Morphological features that described the size, contrast, and shape of microcalcifications and their variations within a cluster were designed to characterize microcalcifications segmented from the mammographic background. Texture features were derived from the spatial gray-level dependence (SGLD) matrices constructed at multiple distances and directions from tissue regions containing microcalcifications. A genetic algorithm (GA) based feature selection technique was used to select the best feature subset from the multi-dimensional feature spaces. The GA-based method was compared to the commonly used feature selection method based on the stepwise linear discriminant analysis (LDA) procedure. Linear discriminant classifiers using the selected features as input predictor variables were formulated for the classification task. The discriminant scores output from the classifiers were analyzed by receiver operating characteristic (ROC) methodology and the classification accuracy was quantified by the area, Az, under the ROC curve. We analyzed a data set of 145 mammographic microcalcification clusters in this study. It was found that the feature subsets selected by the GA-based method are comparable to or slightly better than those selected by the stepwise LDA method. The texture features (Az = 0.84) were more effective than morphological features (Az = 0.79) in distinguishing malignant and benign microcalcifications. The highest classification accuracy (Az = 0.89) was obtained in the combined texture and morphological feature space. The improvement was statistically significant in comparison to classification in either the morphological (p = 0.002) or the texture (p = 0.04) feature space alone. The classifier using the best feature subset from the combined feature space and an appropriate decision threshold could correctly identify 35% of the benign clusters without missing a malignant cluster. When the average discriminant score from all views of the same cluster was used for classification, the Az value increased to 0.93 and the classifier could identify 50% of the benign clusters at 100% sensitivity for malignancy. Alternatively, if the minimum discriminant score from all views of the same cluster was used, the Az value would be 0.90 and a specificity of 32% would be obtained at 100% sensitivity. The results of this study indicate the potential of using combined morphological and texture features for computer-aided classification of microcalcifications.

Design of a high-sensitivity classifier based on a genetic algorithm: application to computer-aided diagnosis

A genetic algorithm (GA) based feature selection method was developed for the design of high-sensitivity classifiers, which were tailored to yield high sensitivity with high specificity. The fitness function of the GA was based on the receiver operating characteristic (ROC) partial area index, which is defined as the average specificity above a given sensitivity threshold. The designed GA evolved towards the selection of feature combinations which yielded high specificity in the high-sensitivity region of the ROC curve, regardless of the performance at low sensitivity. This is a desirable quality of a classifier used for breast lesion characterization, since the focus in breast lesion characterization is to diagnose correctly as many benign lesions as possible without missing malignancies. The high-sensitivity classifier, formulated as the Fisher's linear discriminant using GA-selected feature variables, was employed to classify 255 biopsy-proven mammographic masses as malignant or benign. The mammograms were digitized at a pixel size of 0.1 mm x 0.1 mm, and regions of interest (ROIs) containing the biopsied masses were extracted by an experienced radiologist. A recently developed image transformation technique, referred to as the rubber-band straightening transform, was applied to the ROIs. Texture features extracted from the spatial grey-level dependence and run-length statistics matrices of the transformed ROIs were used to distinguish malignant and benign masses. The classification accuracy of the high-sensitivity classifier was compared with that of linear discriminant analysis with stepwise feature selection (LDAsfs). With proper GA training, the ROC partial area of the high-sensitivity classifier above a true-positive fraction of 0.95 was significantly larger than that of LDAsfs, although the latter provided a higher total area (Az) under the ROC curve. By setting an appropriate decision threshold, the high-sensitivity classifier and LDAsfs correctly identified 61% and 34% of the benign masses respectively without missing any malignant masses. Our results show that the choice of the feature selection technique is important in computer-aided diagnosis, and that the GA may be a useful tool for designing classifiers for lesion characterization.

Classification of compressed breast shapes for the design of equalization filters in x-ray mammography

We are developing an external filter method for equalizing the x-ray exposure in mammography. Each filter is specially designed to match the shape of the compressed breast border and to preferentially attenuate the x-ray beam in the peripheral region of the breast. To be practical, this method should require the use of only a limited number of custom built filters. It is hypothesized that this would be possible if compressed breasts can be classified into a finite number of shapes. A study was performed to determine the number of shapes. Based on the parabolic appearance of the outer borders of compressed breasts in mammograms, the borders were fit with the polynomial equations y = ax2 + bx3 and y = ax2 + bx3 + cx4. The goodness-of-fit of these equations was compared. The a,b and a,b,c coefficients were employed in a K-Means clustering procedure to classify 470 CC-view and 484 MLO-view borders into 2-10 clusters. The mean coefficients of the borders within a given cluster defined the "filter" shape, and the individual borders were translated and rotated to best match that filter shape. The average rms differences between the individual borders and the "filter" were computed as were the standard deviations of those differences. The optimally shifted and rotated borders were refit with the above polynomial equations, and plotted for visual evaluation of clustering success. Both polynomial fits were adequate with rms errors of about 2 mm for the 2-coefficient equation, and about 1 mm for the 3-coefficient equation. Although the fits to the original borders were superior for the 3-coefficient equation, the matches to the "filter" borders determined by clustering were not significantly improved. A variety of modified clustering methods were developed and utilized, but none produced major improvements in clustering. Results indicate that 3 or 4 filter shapes may be adequate for each mammographic projection (CC- and MLO-view). To account for the wide variations in exposures observed at the peripheral regions of breasts classified to be of a particular shape, it may be necessary to employ different filters for thin, medium and thick breasts. Even with this added requirement, it should be possible to use a small number of filters as desired.

Computerized characterization of masses on mammograms: the rubber band straightening transform and texture analysis

A new rubber band straightening transform (RBST) is introduced for characterization of mammographic masses as malignant or benign. The RBST transforms a band of pixels surrounding a segmented mass onto the Cartesian plane (the RBST image). The border of a mammographic mass appears approximately as a horizontal line, and possible speculations resemble vertical lines in the RBST image. In this study, the effectiveness of a set of directional textures extracted from the images before the RBST. A database of 168 mammograms containing biopsy-proven malignant and benign breast masses was digitized at a pixel size of 100 microns x 100 microns. Regions of interest (ROIs) containing the biopsied mass were extracted from each mammogram by an experienced radiologist. A clustering algorithm was employed for automated segmentation of each ROI into a mass object and background tissue. Texture features extracted from spatial gray-level dependence matrices and run-length statistics matrices were evaluated for three different regions and representations: (i) the entire ROI; (ii) a band of pixels surrounding the segmented mass object in the ROI; and (iii) the RBST image. Linear discriminant analysis was used for classification, and receiver operating characteristic (ROC) analysis was used to evaluate the classification accuracy. Using the ROC curves as the performance measure, features extracted from the RBST images were found to be significantly more effective than those extracted from the original images. Features extracted from the RBST images yielded an area (Az) of 0.94 under the ROC curve for classification of mammographic masses as malignant and benign.