Research on the effect of process parameters on the performance of femtosecond laser-bonded skin microstructure

In this paper, the effect of the femtosecond laser process parameters on the texture characteristics of the microstructure was analyzed with the response surface method. The correspondence between the temperature of skin during laser bonding and microscopic tissue texture characteristics parameters was explored. The results show that the three process parameters of laser power, scanning speed, and scanning times and the interaction between the parameters have different patterns of influence on the four texture characteristics parameters of skin microstructure angular second-order moments, entropy, contrast, and relevance. Angular second-order moments and relevance of skin microstructure textures increase with increasing temperature, while entropy values and contrast decrease. It provides another way to evaluate the performance of femtosecond laser-bonded skin with microstructure.

Texture-based speciation of otitis media-related bacterial biofilms from optical coherence tomography images using supervised classification

Otitis media (OM) is primarily a bacterial middle-ear infection prevalent among children worldwide. In recurrent and/or chronic OM cases, antibiotic-resistant bacterial biofilms can develop in the middle ear. A biofilm related to OM typically contains one or multiple bacterial strains, the most common include Haemophilus influenzae, Streptococcus pneumoniae, Moraxella catarrhalis, Pseudomonas aeruginosa, and Staphylococcus aureus. Optical coherence tomography (OCT) has been used clinically to visualize the presence of bacterial biofilms in the middle ear. This study used OCT to compare microstructural image texture features from primary bacterial biofilms in vitro and in vivo. The proposed method applied supervised machine-learning-based frameworks (SVM, random forest (RF), and XGBoost) to classify and speciate multiclass bacterial biofilms from the texture features extracted from OCT B-Scan images obtained from in vitro cultures and from clinically-obtained in vivo images from human subjects. Our findings show that optimized SVM-RBF and XGBoost classifiers can help distinguish bacterial biofilms by incorporating clinical knowledge into classification decisions. Furthermore, both classifiers achieved more than 95% of AUC (area under receiver operating curve), detecting each biofilm class. These results demonstrate the potential for differentiating OM-causing bacterial biofilms through texture analysis of OCT images and a machine-learning framework, which could provide additional clinically relevant data during real-time in vivo characterization of ear infections.

Identification of EIMD Level Differences Between Long- and Short Head of Biceps Brachii Using Echo Intensity and GLCM Texture Features

Purpose: This study aimed to compare the time-course changes of exercise-induced muscle damage (EIMD) levels in the long head of biceps brachii (LHB) and short head of the biceps brachii (SHB) using echo intensity (EI) and to determine the efficiency of the gray level co-occurrence matrix (GLCM) texture parameters. Methods: The participants performed 30 maximal eccentric contractions of the elbow flexor. Along with muscle damage indicators, including circumference, range of motion, muscle soreness, and maximal voluntary isometric contraction (MVIC), the EI and GLCM texture features of the LHB and SHB was also assessed using B-mode ultrasonography. All measurements were assessed pre- and immediately post-exercise and after 24, 48, 72, and 96 h. Results: The muscle damage indicators indicated significant changes after the eccentric contractions (p < 0.01 for circumference, range of motion, muscle soreness, and MVIC). The EI of LHB significantly increased following the contractions (p < 0.01), but that of SHB did not (p > 0.05). In contrast, for the GLCM texture parameters, there were significant changes in the SHB (p < 0.01 for homogeneity, energy, and entropy). Conclusion: Thus, this study demonstrated that EIMD severity is different between LHB and SHB even within the same muscle. In the GLCM features, the time course of SHB after eccentric contraction revealed different patterns compared with those of LHB. Therefore, even if there are no changes in EI within a target muscle following muscle contractions, new information on muscle quality can be obtained through GLCM analysis.

A survey of testicular texture in canine ultrasound images

Introduction

Computer-based texture analysis provides objective data that can be extracted from medical images, including ultrasound images. One popular methodology involves the generation of a gray-level co-occurrence matrix (GLCM) from the image, and from that matrix, texture fractures can be extracted.

Methods

We performed texture analysis on 280 ultrasound testicular images obtained from 70 dogs and explored the resulting texture data, by means of principal component analysis (PCA).

Results

Various abnormal lesions were identified subjectively in 35 of the 280 cropped images. In 16 images, pinpoint-to-small, well-defined, hyperechoic foci were identified without acoustic shadowing. These latter images were classified as having "microliths." The remaining 19 images with other lesions and areas of non-homogeneous testicular parenchyma were classified as "other." In the PCA scores plot, most of the images with lesions were clustered. These clustered images represented by those scores had higher values for the texture features entropy, dissimilarity, and contrast, and lower values for the angular second moment and energy in the first principal component. Other data relating to the dogs, including age and history of treatment for prostatomegaly or chemical castration, did not show clustering on the PCA.

Discussion

This study illustrates that objective texture analysis in testicular ultrasound correlates to some of the visual features used in subjective interpretation and provides quantitative data for parameters that are highly subjective by human observer analysis. The study demonstrated a potential for texture analysis in prediction models in dogs with testicular abnormalities.

Distinct characteristics and severity of brain magnetic resonance imaging lesions in women and men with multiple sclerosis assessed using verified texture analysis measures

Background and goal

In vivo characterization of brain lesion types in multiple sclerosis (MS) has been an ongoing challenge. Based on verified texture analysis measures from clinical magnetic resonance imaging (MRI), this study aimed to develop a method to identify two extremes of brain MS lesions that were approximately severely demyelinated (sDEM) and highly remyelinated (hREM), and compare them in terms of common clinical variables.

Method

Texture analysis used an optimized gray-level co-occurrence matrix (GLCM) method based on FLAIR MRI from 200 relapsing-remitting MS participants. Two top-performing metrics were calculated: texture contrast and dissimilarity. Lesion identification applied a percentile approach according to texture values calculated: ≤ 25 percentile for hREM and ≥75 percentile for sDEM.

Results

The sDEM had a greater total normalized volume yet smaller average size, and worse MRI texture than hREM. In lesion distribution mapping, the two lesion types appeared to overlap largely in location and were present the most in the corpus callosum and periventricular regions. Further, in sDEM, the normalized volume was greater and in hREM, the average size was smaller in men than women. There were no other significant results in clinical variable-associated analyses.

Conclusion

Percentile statistics of competitive MRI texture measures may be a promising method for probing select types of brain MS lesion pathology. Associated findings can provide another useful dimension for improved measurement and monitoring of disease activity in MS. The different characteristics of sDEM and hREM between men and women likely adds new information to the literature, deserving further confirmation.

Machine learning based gray-level co-occurrence matrix early warning system enables accurate detection of colorectal cancer pelvic bone metastases on MRI

Objective

The mortality of colorectal cancer patients with pelvic bone metastasis is imminent, and timely diagnosis and intervention to improve the prognosis is particularly important. Therefore, this study aimed to build a bone metastasis prediction model based on Gray level Co-occurrence Matrix (GLCM) - based Score to guide clinical diagnosis and treatment.

Methods

We retrospectively included 614 patients with colorectal cancer who underwent pelvic multiparameter magnetic resonance image(MRI) from January 2015 to January 2022 in the gastrointestinal surgery department of Gezhouba Central Hospital of Sinopharm. GLCM-based Score and Machine learning algorithm, that is,artificial neural net7work model(ANNM), random forest model(RFM), decision tree model(DTM) and support vector machine model(SVMM) were used to build prediction model of bone metastasis in colorectal cancer patients. The effectiveness evaluation of each model mainly included decision curve analysis(DCA), area under the receiver operating characteristic (AUROC) curve and clinical influence curve(CIC).

Results

We captured fourteen categories of radiomics data based on GLCM for variable screening of bone metastasis prediction models. Among them, Haralick_90, IV_0, IG_90, Haralick_30, CSV, Entropy and Haralick_45 were significantly related to the risk of bone metastasis, and were listed as candidate variables of machine learning prediction models. Among them, the prediction efficiency of RFM in combination with Haralick_90, Haralick_all, IV_0, IG_90, IG_0, Haralick_30, CSV, Entropy and Haralick_45 in training set and internal verification set was [AUC: 0.926,95% CI: 0.873-0.979] and [AUC: 0.919,95% CI: 0.868-0.970] respectively. The prediction efficiency of the other four types of prediction models was between [AUC: 0.716,95% CI: 0.663-0.769] and [AUC: 0.912,95% CI: 0.859-0.965].

Conclusion

The automatic segmentation model based on diffusion-weighted imaging(DWI) using depth learning method can accurately segment the pelvic bone structure, and the subsequently established radiomics model can effectively detect bone metastases within the pelvic scope, especially the RFM algorithm, which can provide a new method for automatically evaluating the pelvic bone turnover of colorectal cancer patients.

Quantitative Evaluation of the Effectiveness of Chemical Peelings in Reducing Acne Lesions Based on Gray-Level Co-Occurrence Matrix (GLCM)

Purpose

Acne vulgaris is a chronic, inflammatory disease accompanied by lesions affecting the structure of the skin. Chemical peels are one of the methods of reducing acne vulgaris. There is still a lack of quantitative methods of assessing impact of cosmetic procedure on the skin. Skin condition depends on skin texture characterization; therefore, the analysis that provides data about the textures can be helpful in assessing the effectiveness of cosmetic treatments.

Patients and Methods

The study involved 24 volunteers with acne lesions. Each participant underwent 4 treatments using chemical peels at two-week intervals. Before, during and after procedure clinical photography were made. To assess effectiveness of chemical peeling in acne lesion reduction, we were used gray-level co-occurrence matrix (GLCM) analysis. Qualitative assessment of acne severity was made by 12 experts in dermatology.

Results

After a series of treatments, the GLCM contrast value decreased in each area of the face, and the GLCM homogeneity value increased, which means that the number of acne lesions was reduced. Expert assessment according to the IGA scale confirms the effectiveness of therapy with both salicylic and glycolic acid and pyruvic acid.

Conclusion

The results of this study prove that GLCM analysis is a useful tool for assessing the effectiveness of chemical peel treatments. It can also be used for quantitative assessment of skin texture.

Radiomic Immunophenotyping of GSEA-Assessed Immunophenotypes of Glioblastoma and Its Implications for Prognosis: A Feasibility Study

Simple Summary

Characterization of immunophenotypes in GBM is important for therapeutic stratification and helps predict treatment response and prognosis. However, identifying immunophenotypes of patients with GBM requires multiple laboratory experiments and is time consuming. We developed a non-invasive method to evaluate enrichment levels of CTL, aDC, Treg, and MDSC immune cells to classify immunophenotypes of GBM tumor microenvironment with radiomic features of MR imaging. Five immunophenotypes (G1–G5) of GBM can be classified with specific gene set enrichment analysis. G2 had the worst prognosis and comprised highly enriched MDSCs and lowly enriched CTLs. G3 had the best prognosis and comprised lowly enriched MDSCs and Tregs and highly enriched CTLs. Moreover, the developed radiomics models can successfully identified these two groups by immune cell subsets enriched levels prediction. Therefore, it is possible to characterize immunophenotypes of GBM and predict patient prognosis with radiomics methods.

Abstract

Characterization of immunophenotypes in glioblastoma (GBM) is important for therapeutic stratification and helps predict treatment response and prognosis. Radiomics can be used to predict molecular subtypes and gene expression levels. However, whether radiomics aids immunophenotyping prediction is still unknown. In this study, to classify immunophenotypes in patients with GBM, we developed machine learning-based magnetic resonance (MR) radiomic models to evaluate the enrichment levels of four immune subsets: Cytotoxic T lymphocytes (CTLs), activated dendritic cells, regulatory T cells (Tregs), and myeloid-derived suppressor cells (MDSCs). Independent testing data and the leave-one-out cross-validation method were used to evaluate model effectiveness and model performance, respectively. We identified five immunophenotypes (G1 to G5) based on the enrichment level for the four immune subsets. G2 had the worst prognosis and comprised highly enriched MDSCs and lowly enriched CTLs. G3 had the best prognosis and comprised lowly enriched MDSCs and Tregs and highly enriched CTLs. The average accuracy of T1-weighted contrasted MR radiomics models of the enrichment level for the four immune subsets reached 79% and predicted G2, G3, and the “immune-cold” phenotype (G1) according to our radiomics models. Our radiomic immunophenotyping models feasibly characterize the immunophenotypes of GBM and can predict patient prognosis.

Identification of Human Ovarian Adenocarcinoma Cells with Cisplatin-resistance by Feature Extraction of Gray Level Co-occurrence Matrix Using Optical Images

Ovarian cancer is the most malignant of all gynecological cancers. A challenge that deteriorates with ovarian adenocarcinoma in neoplastic disease patients has been associated with the chemoresistance of cancer cells. Cisplatin (CP) belongs to the first-line chemotherapeutic agents and it would be beneficial to identify chemoresistance for ovarian adenocarcinoma cells, especially CP-resistance. Gray level co-occurrence matrix (GLCM) was characterized imaging from a numeric matrix and find its texture features. Serous type (OVCAR-4 and A2780), and clear cell type (IGROV1) ovarian carcinoma cell lines with CP-resistance were used to demonstrate GLCM texture feature extraction of images. Cells were cultured with cell density of 6 × 10⁵ in a glass-bottom dish to form a uniform coverage of the glass slide to get the optical images by microscope and DVC camera. CP-resistant cells included OVCAR-4, A2780 and IGROV and had the higher contrast and entropy, lower energy, and homogeneity. Signal to noise ratio was used to evaluate the degree for chemoresistance of cell images based on GLCM texture feature extraction. The difference between wile type and CP-resistant cells was statistically significant in every case (p < 0.001). It is a promising model to achieve a rapid method with a more reliable diagnostic performance for identification of ovarian adenocarcinoma cells with CP-resistance by feature extraction of GLCM in vitro or ex vivo.

Discriminant analysis and interpretation of nuclear chromatin distribution and coarseness using gray-level co-occurrence matrix features for lobular endocervical glandular hyperplasia

BACKGROUND

Lobular endocervical glandular hyperplasia (LEGH) is a disease considered to be the origin of tumorigenesis of minimal deviation adenocarcinoma, which has characteristic expression in the gastric pyloric mucosa. It is difficult to diagnose by nuclear findings because of lower nuclear atypia. In this study, nuclei of endocervical (EC) and LEGH cells were digitized, and nuclear information was quantified from nuclear images and objectively evaluated using a computer. We examined whether it is possible to distinguish between EC and LEGH cells, which is difficult by human eyes.

METHODS

Signal intensity, morphological features, Otsu thresholding technique and gray-level co-occurrence matrix (GLCM) features were calculated from nuclei of EC and LEGH cells on cytology microscopic images. Then, discriminant analysis was performed using the significant difference test and linear support vector machine (LSVM).

RESULTS

GLCM features in LEGH cells were higher than those in EC cells. The nuclei of LEGH cells had a higher frequency of signal value pairs with a larger signal value difference than that of EC cells. Therefore, LEGH cell nuclei are thought to have more chromatin granules, and the chromatin is coarse and granular. Moreover, in the LSVM discriminant analysis, the accuracy of GLCM calculated using these features was 85.4%.

CONCLUSION

In this study, GLCM accurately demonstrated the nuclear chromatin distribution and coarseness. Discriminant analysis of EC and LEGH cells using GLCM features is useful.

Factors associated with obesity alter matrix remodeling in breast cancer tissues

Obesity is associated with a higher risk of developing breast cancer and with worse disease outcomes for women of all ages. The composition, density, and organization of the breast tissue stroma are also known to play an important role in the development and progression of the disease. However, the connections between obesity and stromal remodeling are not well understood. We sought to characterize detailed organization features of the collagen matrix within healthy and cancerous breast tissues acquired from mice exposed to either a normal or high fat (obesity inducing) diet. We performed second-harmonic generation and spectral two-photon excited fluorescence imaging, and we extracted the level of collagen-associated fluorescence (CAF) along with metrics of collagen content, three-dimensional, and two-dimensional organization. There were significant differences in the CAF intensity and overall collagen organization between normal and tumor tissues; however, obesity-enhanced changes in these metrics, especially when three-dimensional organization metrics were considered. Thus, our studies indicate that obesity impacts significantly collagen organization and structure and the related pathways of communication may be important future therapeutic targets.

Application of Gray Level Co-Occurrence Matrix Analysis as a New Method for Enzyme Histochemistry Quantification

Enzyme histochemistry is a valuable histological method which provides a connection between morphology, activity, and spatial localization of investigated enzymes. Even though the method relies purely on arbitrary evaluations performed by the human eye, it is still wildly accepted and used in histo(patho)logy. Texture analysis emerged as an excellent tool for image quantification of subtle differences reflected in both spatial discrepancies and gray level values of pixels. The current study of texture analysis utilizes the gray-level co-occurrence matrix as a method for quantification of differences between ecto-5'-nucleotidase activities in healthy hippocampal tissue and tissue with marked neurodegeneration. We used the angular second moment, contrast (CON), correlation, inverse difference moment (INV), and entropy for texture analysis and receiver operating characteristic analysis with immunoblot and qualitative assessment of enzyme histochemistry as a validation. Our results strongly argue that co-occurrence matrix analysis could be used for the determination of fine differences in the enzyme activities with the possibility to ascribe those differences to regions or specific cell types. In addition, it emerged that INV and CON are especially useful parameters for this type of enzyme histochemistry analysis. We concluded that texture analysis is a reliable method for quantification of this descriptive technique, thus removing biases and adding it a quantitative dimension.

Spatial Bayesian modeling of GLCM with application to malignant lesion characterization

The emerging field of cancer radiomics endeavors to characterize intrinsic patterns of tumor phenotypes and surrogate markers of response by transforming medical images into objects that yield quantifiable summary statistics to which regression and machine learning algorithms may be applied for statistical interrogation. Recent literature has identified clinicopathological association based on textural features deriving from gray-level co-occurrence matrices (GLCM) which facilitate evaluations of gray-level spatial dependence within a delineated region of interest. GLCM-derived features, however, tend to contribute highly redundant information. Moreover, when reporting selected feature sets, investigators often fail to adjust for multiplicities and commonly fail to convey the predictive power of their findings. This article presents a Bayesian probabilistic modeling framework for the GLCM as a multivariate object as well as describes its application within a cancer detection context based on computed tomography. The methodology, which circumvents processing steps and avoids evaluations of reductive and highly correlated feature sets, uses latent Gaussian Markov random field structure to characterize spatial dependencies among GLCM cells and facilitates classification via predictive probability. Correctly predicting the underlying pathology of 81% of the adrenal lesions in our case study, the proposed method outperformed current practices which achieved a maximum accuracy of only 59%. Simulations and theory are presented to further elucidate this comparison as well as ascertain the utility of applying multivariate Gaussian spatial processes to GLCM objects.

Blood oxygen level-dependent magnetic resonance imaging for detecting pathological patterns in patients with lupus nephritis: a preliminary study using gray-level co-occurrence matrix analysis

Objective

Blood oxygen level-dependent magnetic resonance imaging (BOLD MRI) is a noninvasive technique useful in patients with renal disease. The current study was performed to determine whether BOLD MRI can contribute to the diagnosis of renal pathological patterns.

Methods

BOLD MRI was used to obtain functional magnetic resonance parameter R2* values. Gray-level co-occurrence matrixes (GLCMs) were generated for gray-scale maps. Several GLCM parameters were calculated and used to construct algorithmic models for renal pathological patterns.

Results

Histopathology and BOLD MRI were used to examine 12 patients. Two GLCM parameters, including correlation and energy, revealed differences among four groups of renal pathological patterns. Four Fisher’s linear discriminant formulas were constructed using two variables, including the correlation at 45° and correlation at 90°. A cross-validation test showed that the formulas correctly predicted 28 of 36 samples, and the rate of correct prediction was 77.8%.

Conclusions

Differences in the texture characteristics of BOLD MRI in patients with lupus nephritis may be detected by GLCM analysis. Discriminant formulas constructed using GLCM parameters may facilitate prediction of renal pathological patterns.

Image texture features predict renal function decline in patients with autosomal dominant polycystic kidney disease

Magnetic resonance imaging (MRI) examinations provide high-resolution information about the anatomic structure of the kidneys and are used to measure total kidney volume (TKV) in patients with Autosomal Dominant Polycystic Kidney Disease (ADPKD). Height-adjusted TKV (HtTKV) has become the gold-standard imaging biomarker for ADPKD progression at early stages of the disease when estimated glomerular filtration rate (eGFR) is still normal. However, HtTKV does not take advantage of the wealth of information provided by MRI. Here we tested whether image texture features provide additional insights into the ADPKD kidney that may be used as complementary information to existing biomarkers. A retrospective cohort of 122 patients from the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease (CRISP) study was identified who had T2-weighted MRIs and eGFR values over 70 mL/min/1.73m² at the time of their baseline scan. We computed nine distinct image texture features for each patient. The ability of each feature to predict subsequent progression to CKD stage 3A, 3B, and 30% reduction in eGFR at eight-year follow-up was assessed. A multiple linear regression model was developed incorporating age, baseline eGFR, HtTKV, and three image texture features identified by stability feature selection (Entropy, Correlation, and Energy). Including texture in a multiple linear regression model (predicting percent change in eGFR) improved Pearson correlation coefficient from -0.51 (using age, eGFR, and HtTKV) to -0.70 (adding texture). Thus, texture analysis offers an approach to refine ADPKD prognosis and should be further explored for its utility in individualized clinical decision making and outcome prediction.

An Investigation of Bayes Algorithm and Neural Networks for Identifying the Breast Cancer

Context

Breast cancer is a biggest threat to women. X-ray mammography is the most effective method for early detection and screening of breast cancer. It is a tough challenge for the radiologist in reading mammography since it does not provide consistent result every time.

Aim

To improve the primary sign of this disease, computer-aided diagnosis schemes have been developed. Using monitor, digital images of mammography are displayed and they can be lightened or darkened before they are printed on the film. Time factor is important to identify the abnormality in body such as breast cancer and lung cancer. Hence, to detect the tissues and treatment stages, image-processing techniques are improved in several medical areas. In this project, using low-level preprocessing techniques and image segmentation, the breast cancer detection is done.

Methods

With the help of Bayes algorithm and neural networks (NNs), the type of the mammogram and stages is identified. For segmentation process, region-growing algorithm is used, which helps to find the affected portion, i.e., region of interest. Gray-level co-occurrence matrix (GLCM) and texture feature are used for feature extraction.

Results

Bayes algorithm is used for probability of identification, whereas NNs is used to reduce the probability level from 0-1000 to 0-1 in case of classification.

Quantitative Sonographic Texture Analysis in Preterm Neonates With White Matter Injury: Correlation of Texture Features With White Matter Injury Severity

OBJECTIVES

To analyze the texture features on cranial sonography in preterm neonates with white matter injury quantitatively and to correlate these features with magnetic resonance imaging (MRI).

METHODS

The study included 33 preterm neonates treated in our neonatal intensive care unit who underwent serial cranial sonography and brain MRI near term. Patients were subdivided into 3 groups according to the presence and severity of white matter injury as revealed by MRI: normal (group 1; n = 20), mild (group 2; n = 5), and severe (group 3; n = 8). The periventricular echogenicity on sonography was evaluated quantitatively with second-order gray-level statistics (gray-level co-occurrence matrix [GLCM] method). Four GLCM texture features representing homogeneity were extracted in 12 directions: (1) angular second moment (ASM), (2) inverse differential moment (IDM), (3) contrast, and (4) entropy.

RESULTS

Thirty of 48 features showed a statistically significant difference between groups 1 and 3 (ASM in 9 directions, IDM in 6 directions, contrast in 3 directions, and entropy in all 12 directions). There were no significant differences observed between groups 1 and 2 or groups 2 and 3. The mean contrast and entropy values were generally lower in group 1 than group 3, whereas the mean ASM and IDM values were higher in group 1.

CONCLUSIONS

Severe white matter injury could be identified by using GLCM texture analysis, whereas mild white matter injury observed on MRI could not be evaluated by GLCM analysis. Quantitative texture analysis using the GLCM may serve as a complementary tool for quantitative assessment of periventricular echogenicity.

Gray-Level Co-Occurrence Matrix Texture Analysis of Breast Tumor Images in Prognosis of Distant Metastasis Risk

Owing to exceptional heterogeneity in the outcome of invasive breast cancer it is essential to develop highly accurate prognostic tools for effective therapeutic management. Based on this pressing need, we aimed to improve breast cancer prognosis by exploring the prognostic value of tumor histology image analysis. Patient group (n=78) selection was based on invasive breast cancer diagnosis without systemic treatment with a median follow-up of 147 months. Gray-level co-occurrence matrix texture analysis was performed retrospectively on primary tumor tissue section digital images stained either nonspecifically with hematoxylin and eosin or specifically with a pan-cytokeratin antibody cocktail for epithelial malignant cells. Univariate analysis revealed stronger association with metastasis risk by texture analysis when compared with clinicopathological parameters. The combination of individual clinicopathological and texture variables into composite scores resulted in further powerful enhancement of prognostic performance, with an accuracy of up to 90%, discrimination efficiency by the area under the curve [95% confidence interval (CI)] of 0.94 (0.87-0.99) and hazard ratio (95% CI) of 20.1 (7.5-109.4). Internal validation was successfully performed by bootstrap and split-sample cross-validation, suggesting that the models are generalizable. Whereas further validation is needed on an external set of patients, this preliminary study indicates the potential use of primary breast tumor histology texture as a highly accurate, simple, and cost-effective prognostic indicator of distant metastasis risk.

Volumetric Characterization of Human Patellar Cartilage Matrix on Phase Contrast X-Ray Computed Tomography

Phase contrast X-ray computed tomography (PCI-CT) has recently emerged as a novel imaging technique that allows visualization of cartilage soft tissue, subsequent examination of chondrocyte patterns, and their correlation to osteoarthritis. Previous studies have shown that 2D texture features are effective at distinguishing between healthy and osteoarthritic regions of interest annotated in the radial zone of cartilage matrix on PCI-CT images. In this study, we further extend the texture analysis to 3D and investigate the ability of volumetric texture features at characterizing chondrocyte patterns in the cartilage matrix for purposes of classification. Here, we extracted volumetric texture features derived from Minkowski Functionals and gray-level co-occurrence matrices (GLCM) from 496 volumes of interest (VOI) annotated on PCI-CT images of human patellar cartilage specimens. The extracted features were then used in a machine-learning task involving support vector regression to classify ROIs as healthy or osteoarthritic. Classification performance was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC). The best classification performance was observed with GLCM features correlation (AUC = 0.83 ± 0.06) and homogeneity (AUC = 0.82 ± 0.07), which significantly outperformed all Minkowski Functionals (p < 0.05). These results suggest that such quantitative analysis of chondrocyte patterns in human patellar cartilage matrix involving GLCM-derived statistical features can distinguish between healthy and osteoarthritic tissue with high accuracy.

Adaptive road crack detection system by pavement classification

This paper presents a road distress detection system involving the phases needed to properly deal with fully automatic road distress assessment. A vehicle equipped with line scan cameras, laser illumination and acquisition HW-SW is used to storage the digital images that will be further processed to identify road cracks. Pre-processing is firstly carried out to both smooth the texture and enhance the linear features. Non-crack features detection is then applied to mask areas of the images with joints, sealed cracks and white painting, that usually generate false positive cracking. A seed-based approach is proposed to deal with road crack detection, combining Multiple Directional Non-Minimum Suppression (MDNMS) with a symmetry check. Seeds are linked by computing the paths with the lowest cost that meet the symmetry restrictions. The whole detection process involves the use of several parameters. A correct setting becomes essential to get optimal results without manual intervention. A fully automatic approach by means of a linear SVM-based classifier ensemble able to distinguish between up to 10 different types of pavement that appear in the Spanish roads is proposed. The optimal feature vector includes different texture-based features. The parameters are then tuned depending on the output provided by the classifier. Regarding non-crack features detection, results show that the introduction of such module reduces the impact of false positives due to non-crack features up to a factor of 2. In addition, the observed performance of the crack detection system is significantly boosted by adapting the parameters to the type of pavement.

Remote-sensing image classification based on an improved probabilistic neural network

This paper proposes a hybrid classifier for polarimetric SAR images. The feature sets consist of span image, the H/A/α decomposition, and the GLCM-based texture features. Then, a probabilistic neural network (PNN) was adopted for classification, and a novel algorithm proposed to enhance its performance. Principle component analysis (PCA) was chosen to reduce feature dimensions, random division to reduce the number of neurons, and Brent’s search (BS) to find the optimal bias values. The results on San Francisco and Flevoland sites are compared to that using a 3-layer BPNN to demonstrate the validity of our algorithm in terms of confusion matrix and overall accuracy. In addition, the importance of each improvement of the algorithm was proven.