Comparison of Mechanical Responses of Asphalt Mixtures under Uniform and Non-Uniform Loads Using Microscale Finite Element Simulation

Continuously increasing traffic volumes necessitate accurate design methods to ensure the optimal service life and efficient use of raw materials. Numerical simulations commonly pursue a simplified approach with homogeneous pavement materials and homogeneous loading. Neither the pavement geometry nor the loading is homogeneous in reality. In this study, the mechanical response of the asphalt mixtures due to homogeneous loads is compared with their mechanical response to inhomogeneous loads. A 3D finite element model was reconstructed with the aid of X-ray computed tomography. Sections of a real tire's pressure distribution were used for the inhomogeneous loads. The evaluation of the material response analyzes the stress distribution within the samples. An inhomogeneous load evokes an increased proportion of high stresses within the sample in every case, particularly at low temperatures. When comparing the two types of loads, the average stresses on the interior (tension and compression) exhibit significant differences. The magnitude of the discrepancies shows that this approach yields results that differ significantly from the common practice of using homogeneous models and can be used to improve pavement design.

A G-Fresnel Optical Device and Image Processing Based Miniature Spectrometer for Mechanoluminescence Sensor Applications

This paper presents a miniature spectrometer fabricated based on a G-Fresnel optical device (i.e., diffraction grating and Fresnel lens) and operated by an image-processing algorithm, with an emphasis on the color space conversion in the range of visible light. The miniature spectrometer will be cost-effective and consists of a compact G-Fresnel optical device, which diffuses mixed visible light into the spectral image and a μ-processor platform embedded with an image-processing algorithm. The RGB color space commonly used in the image signal from a complementary metal-oxide-semiconductor (CMOS)-type image sensor is converted into the HSV color space, which is one of the most common methods to express color as a numeric value using hue (H), saturation (S), and value (V) via the color space conversion algorithm. Because the HSV color space has the advantages of expressing not only the three primary colors of light as the H but also its intensity as the V, it was possible to obtain both the wavelength and intensity information of the visible light from its spectral image. This miniature spectrometer yielded nonlinear sensitivity of hue in terms of wavelength. In this study, we introduce the potential of the G-Fresnel optical device, which is a miniature spectrometer, and demonstrated by measurement of the mechanoluminescence (ML) spectrum as a proof of concept.

Fractal Cracking Patterns in Concretes Exposed to Sulfate Attack

Sulfate attack tests were performed on concrete samples with three water-to-cement ratios, and micro-crack growth patterns on concrete surfaces were recorded. The expansive stress and crack nucleation caused by delayed ettringite formation (DEF) were studied using X-ray diffraction and scanning electron microscopy. By means of a digital image processing technology, fractal dimensions of surface cracking patterns were determined, which monotonously increase during corrosion. Moreover, it is shown that the change of fractal dimensions is directly proportional to accumulation of DEF, and therefore, a simple theoretical model could be proposed to describe the micro-crack evolution in concretes under sulfate attack.

Segmentation of nearly isotropic overlapped tracks in photomicrographs using successive erosions as watershed markers

INTRODUCTION

Procedures for measuring and counting tracks are time-consuming and involve practical problems. The precision of automatic counting methods is not satisfactory yet; the major challenges are distinguishing tracks and material defects, identifying small tracks and defects of similar size, and detecting overlapping tracks.

MATERIALS AND METHODS

Here, we address the overlapping tracks issue using the algorithm Watershed Using Successive Erosions as Markers (WUSEM), which combines the watershed transform, morphological erosions and labeling to separate regions in photomicrographs. We tested this method in two data sets of diallyl phthalate (DAP) photomicrographs and compared the results when counting manually and using the classic watershed and H-watershed transforms.

RESULTS

The mean automatic/manual efficiency counting ratio when using WUSEM in the test data sets is 0.97 ± 0.11.

CONCLUSION

WUSEM shows reliable results when used in photomicrographs presenting almost isotropic objects. Also, diameter and eccentricity criteria may be used to increase the reliability of this method.

Retinal images benchmark for the detection of diabetic retinopathy and clinically significant macular edema (CSME)

Diabetes mellitus is an enduring disease related with significant morbidity and mortality. The main pathogenesis behind this disease is its numerous micro- and macrovascular complications. In developing countries, diabetic retinopathy (DR) is one of the major sources of vision impairment in working age population. DR has been classified into two categories: proliferative diabetic retinopathy (PDR) and non-proliferative diabetic retinopathy (NPDR). NPDR is further classified into mild, moderate and severe, while PDR is further classified into early PDR, high risk PDR and advanced diabetic eye disease. DR is a disease caused due to high blood glucose levels which result in vision loss or permanent blindness. High-level advancements in the field of bio-medical image processing have speeded up the automated process of disease diagnoses and analysis. Much research has been conducted and computerized systems have been designed to detect and analyze retinal diseases through image processing. Similarly, a number of algorithms have been designed to detect and grade DR by analyzing different symptoms including microaneurysms, soft exudates, hard exudates, cotton wool spots, fibrotic bands, neovascularization on disc (NVD), neovascularization elsewhere (NVE), hemorrhages and tractional bands. The visual examination of the retina is a vital test to diagnose DR-related complications. However, all the DR computer-aided diagnostic systems require a standard dataset for the estimation of their efficiency, performance and accuracy. This research presents a benchmark for the evaluation of computer-based DR diagnostic systems. The existing DR benchmarks are small in size and do not cover all the DR stages and categories. The dataset contains 1445 high-quality fundus photographs of retinal images, acquired over 2 years from the records of the patients who presented to the Department of Ophthalmology, Holy Family Hospital, Rawalpindi. This benchmark provides an evaluation platform for medical image analysis researchers. Furthermore, it provides evaluation data for all the stages of DR.

Digital image processing software for diagnosing diabetic retinopathy from fundus photograph

Objective

The aim of this study was to develop automated software for screening and diagnosing diabetic retinopathy (DR) from fundus photograph of patients with diabetes mellitus.

Methods

The extraction of clinically significant features to detect pathologies of DR and the severity classification were performed by using MATLAB R2015a with MATLAB Image Processing Toolbox. In addition, the graphic user interface was developed using the MATLAB GUI Toolbox. The accuracy of software was measured by comparing the obtained results to those of the diagnosis by the ophthalmologist.

Results

A set of 400 fundus images, containing 21 normal fundus images and 379 DR fundus images (162 non-proliferative DR and 217 proliferative DR), was interpreted by the ophthalmologist as a reference standard. The initial result showed that the sensitivity, specificity and accuracy of this software in detection of DR were 98%, 67% and 96.25%, respectively. However, the accuracy of this software in classifying non-proliferative and proliferative diabetic retinopathy was 66.58%. The average time for processing is 7 seconds for one fundus image.

Conclusion

The automated DR screening software was developed by using MATLAB programming and yielded 96.25% accuracy for the detection of DR when compared to that of the diagnosis by the ophthalmologist. It may be a helpful tool for DR screening in the distant rural area where ophthalmologist is not available.

Photo-Rheological Fluid-Based Colorimetric Ultraviolet Light Intensity Sensor

This study presents an introduction to a new type of ultraviolet (UV) light intensity sensor using photo-rheological (PR) fluids whose properties, such as color, can be changed by UV light. When the PR fluids were irradiated by UV light, colorimetric transitions were observed. Effectively, this means that their color changed gradually from yellow to red. The degree of the color change depended on the UV light intensity and was characterized by the hue value of the images acquired with a compact image sensor. We demonstrated that UV light-responsive capabilities can be readily imparted to PR fluids, and that the colorimetric responses to different UV light intensities can be used to measure the UV light intensities.

The Effects of High Dynamic Range on Fingerprint Images Processed by Photoshop

High dynamic range (HDR) imaging is a function that combines five images with different exposures into a single image. This technique may provide fine ridge details of fingerprint images for forensic latent fingerprint examination. Therefore, viewing fingerprints under optimal conditions is of paramount importance. This paper analyzes HDR and non-HDR photos by using the Michelson contrast formula. The Michelson formula will provide a measurement to determine whether better contrast between the background and print can be achieved using the HDR function and if the background color affects the quality of the images. Two hypothesis were tested: (i) the HDR image provides more details of fingerprints with a better tone, greater clarity, and contrast than a normally exposed image regardless of the background color; (ii) the background color does not affect the quality of HDR fingerprint images overall, but the multi-color background may increase the contrast of HDR fingerprint images in some cases.

Refining Convolutional Neural Network Detection of Small-Bowel Obstruction in Conventional Radiography. AJR Am J Roentgenol. 2019;212:342-350. [PMID: 30476452 DOI: 10.2214/ajr.18.20362]

OBJECTIVE

The purpose of this study was to evaluate improvement of convolutional neural network detection of high-grade small-bowel obstruction on conventional radiographs with increased training set size.

MATERIALS AND METHODS

A set of 2210 abdominal radiographs from one institution (image set 1) had been previously classified into obstructive and nonobstructive categories by consensus judgments of three abdominal radiologists. The images were used to fine-tune an initial convolutional neural network classifier (stage 1). An additional set of 13,935 clinical images from the same institution was reduced to 5558 radiographs (image set 2) primarily by retaining only images classified positive for bowel obstruction by the initial classifier. These images were classified into obstructive and nonobstructive categories by an abdominal radiologist. The combined 7768 radiographs were used to train additional classifiers (stage 2 training). The best classifiers from stage 1 and stage 2 training were evaluated on a held-out test set of 1453 abdominal radiographs from image set 1.

RESULTS

The ROC AUC for the neural network trained on image set 1 was 0.803; after stage 2, the ROC AUC of the best model was 0.971. By use of an operating point based on maximizing the validation set Youden J index, the stage 2-trained model had a test set sensitivity of 91.4% and specificity of 91.9%. Classification performance increased with training set size, reaching a plateau with over 200 positive training examples.

CONCLUSION

Accuracy of detection of high-grade small-bowel obstruction with a convolutional neural network improves significantly with the number of positive training radiographs.

Label-free dynamic segmentation and morphological analysis of subcellular optical scatterers

Imaging without fluorescent protein labels or dyes presents significant advantages for studying living cells without confounding staining artifacts and with minimal sample preparation. Here, we combine label-free optical scatter imaging with digital segmentation and processing to create dynamic subcellular masks, which highlight significantly scattering objects within the cells' cytoplasms. The technique is tested by quantifying organelle morphology and redistribution during cell injury induced by calcium overload. Objects within the subcellular mask are first analyzed individually. We show that the objects' aspect ratio and degree of orientation ("orientedness") decrease in response to calcium overload, while they remain unchanged in untreated control cells. These changes are concurrent with mitochondrial fission and rounding observed by fluorescence, and are consistent with our previously published data demonstrating scattering changes associated with mitochondrial rounding during calcium injury. In addition, we show that the magnitude of the textural features associated with the spatial distribution of the masked objects' orientedness values, changes by more than 30% in the calcium-treated cells compared with no change or changes of less than 10% in untreated controls, reflecting dynamic changes in the overall spatial distribution and arrangement of subcellular scatterers in response to injury. Taken together, our results suggest that our method successfully provides label-free morphological signatures associated with cellular injury. Thus, we propose that dynamically segmenting and analyzing the morphology and organizational patterns of subcellular scatterers as a function of time can be utilized to quantify changes in a given cellular condition or state.

Visualization of Concrete Slump Flow Using the Kinect Sensor

Workability is regarded as one of the important parameters of high-performance concrete and monitoring it is essential in concrete quality management at construction sites. The conventional workability test methods are basically based on length and time measured by a ruler and a stopwatch and, as such, inevitably involves human error. In this paper, we propose a 4D slump test method based on digital measurement and data processing as a novel concrete workability test. After acquiring the dynamically changing 3D surface of fresh concrete using a 3D depth sensor during the slump flow test, the stream images are processed with the proposed 4D slump processing algorithm and the results are compressed into a single 4D slump image. This image basically represents the dynamically spreading cross-section of fresh concrete along the time axis. From the 4D slump image, it is possible to determine the slump flow diameter, slump flow time, and slump height at any location simultaneously. The proposed 4D slump test will be able to activate research related to concrete flow simulation and concrete rheology by providing spatiotemporal measurement data of concrete flow.

Diffuse optical tomography using semiautomated coregistered ultrasound measurements

Diffuse optical tomography (DOT) has demonstrated huge potential in breast cancer diagnosis and treatment monitoring. DOT image reconstruction guided by ultrasound (US) improves the diffused light localization and lesion reconstruction accuracy. However, DOT reconstruction depends on tumor geometry provided by coregistered US. Experienced operators can manually measure these lesion parameters; however, training and measurement time are needed. The wide clinical use of this technique depends on its robustness and faster imaging reconstruction capability. This article introduces a semiautomated procedure that automatically extracts lesion information from US images and incorporates it into the optical reconstruction. An adaptive threshold-based image segmentation is used to obtain tumor boundaries. For some US images, posterior shadow can extend to the chest wall and make the detection of deeper lesion boundary difficult. This problem can be solved using a Hough transform. The proposed procedure was validated from data of 20 patients. Optical reconstruction results using the proposed procedure were compared with those reconstructed using extracted tumor information from an experienced user. Mean optical absorption obtained from manual measurement was 0.21±0.06  cm-1 for malignant and 0.12±0.06  cm-1 for benign cases, whereas for the proposed method it was 0.24±0.08  cm-1 and 0.12±0.05  cm-1, respectively.

Quantitative high-content/high-throughput microscopy analysis of lipid droplets in subject-specific adipogenesis models

Neutral lipids packed in lipid droplets (LDs) are essential as a source of fuel for organisms, and specialized storing cells, the adipocytes, provide a buffer for energy variations. Many modern-society-disorders are connected with excess accumulation or deficiency of LDs in adipose tissue. Intracellular LD number and size distribution reflect the tissue conditions, while the associated mechanisms and genes rs are still poorly understood. Large-scale genetic screens using human in vitro differentiated primary adipocytes require cell samples donated from many patients. The heterogeneity appearing between donors highlighted the need for high-throughput methods robust to individual variations. Previous image analysis algorithms failed to handle individual LDs, but focused on averages, hiding population heterogeneity. We present a new high-content analysis (HCA) technique for analysis of fat cell metabolism using data from a large-scale RNAi screen including images of more than 500 k in vitro differentiated adipocytes from three donors. The RNAi-based suppression of Perilipin 1 (PLIN1), a protein involved in the adipocyte lipid metabolism, served as a positive control, while cells treated with randomized RNA served as negative controls. We validate our segmentation by comparing our results to those of previously published methods: We also evaluate the discriminative power of different morphological features describing LD size distribution. Classification of cells as containing few large or many small LDs followed by calculating the percentage of cells in each class proved to discriminate the positive PLIN1-suppressed phenotype from the untreated negative control with an area under the receiver operating characteristic curve of 0.98. The results suggest that this HCA method offers improved segmentation and classification accuracy, and can, thus, be utilized to quantify changes in LD metabolism in response to treatment in many cell models relevant to a variety of diseases. © 2017 International Society for Advancement of Cytometry.

Concrete Crack Identification Using a UAV Incorporating Hybrid Image Processing

Crack assessment is an essential process in the maintenance of concrete structures. In general, concrete cracks are inspected by manual visual observation of the surface, which is intrinsically subjective as it depends on the experience of inspectors. Further, it is time-consuming, expensive, and often unsafe when inaccessible structural members are to be assessed. Unmanned aerial vehicle (UAV) technologies combined with digital image processing have recently been applied to crack assessment to overcome the drawbacks of manual visual inspection. However, identification of crack information in terms of width and length has not been fully explored in the UAV-based applications, because of the absence of distance measurement and tailored image processing. This paper presents a crack identification strategy that combines hybrid image processing with UAV technology. Equipped with a camera, an ultrasonic displacement sensor, and a WiFi module, the system provides the image of cracks and the associated working distance from a target structure on demand. The obtained information is subsequently processed by hybrid image binarization to estimate the crack width accurately while minimizing the loss of the crack length information. The proposed system has shown to successfully measure cracks thicker than 0.1 mm with the maximum length estimation error of 7.3%.

Quantitative analysis of histopathological findings using image processing software

In evaluating pathological changes in drug efficacy and toxicity studies, morphometric analysis can be quite robust. In this experiment, we examined whether morphometric changes of major pathological findings in various tissue specimens stained with hematoxylin and eosin could be recognized and quantified using image processing software. Using Tissue Studio, hypertrophy of hepatocytes and adrenocortical cells could be quantified based on the method of a previous report, but the regions of red pulp, white pulp, and marginal zones in the spleen could not be recognized when using one setting condition. Using Image-Pro Plus, lipid-derived vacuoles in the liver and mucin-derived vacuoles in the intestinal mucosa could be quantified using two criteria (area and/or roundness). Vacuoles derived from phospholipid could not be quantified when small lipid deposition coexisted in the liver and adrenal cortex. Mononuclear inflammatory cell infiltration in the liver could be quantified to some extent, except for specimens with many clustered infiltrating cells. Adipocyte size and the mean linear intercept could be quantified easily and efficiently using morphological processing and the macro tool equipped in Image-Pro Plus. These methodologies are expected to form a base system that can recognize morphometric features and analyze quantitatively pathological findings through the use of information technology.

Digital Image Analysis of Cells and Computational Tools for the Study of Mechanism of RSV Entry to Human Bronchial Epithelium

this paper presents a research proposal which has been developed as a doctoral thesis in the PhD program in Computer Systems Engineering at the Universidad del Norte since August 2015. This research focuses on the analysis of cell images of the human bronchial epithelium infected with the Respiratory Syncytial Virus in order to understand the mechanisms of entry of the virus into the human body. Due to the large amount of information that is processed, it is necessary to use computational tools to finally differentiate between infected and uninfected cells.

Quantitative distinction of the morphological characteristic of erythrocyte precursor cells with texture analysis using gray level co-occurrence matrix

BACKGROUND

Morphological characteristics of blood cells are still qualitatively defined. So a texture analysis (Tx) method using gray level co-occurrence matrices (GLCMs; CM-Tx method) was applied to images of erythrocyte precursor cells (EPCs) for quantitatively distinguishing four types of EPC stages: proerythroblast, basophilic erythroblast, polychromatic erythroblast, and orthochromatic erythroblast.

METHODS

Fifty-five images of four types of EPCs were downloaded from an atlas uploaded by the Blood Cell Morphology Standardization Subcommittee (BCMSS) of the Japanese Society of Laboratory Hematology (JSLH). Using in-house programs, two types of GLCMs-(R: d=1, θ=0°) and (U: d=1, θ=270°)-and nine types of texture distinction index (TDI) were calculated with images removed outer part of cell.

RESULTS

Three binary decision trees were sequentially divided among four types of EPC with the sum average of GLCM (U), the contrast of GLCM (R), and the sum average of GLCM (U). The average concordance rate (sensitivity) of CM-Tx method with the judgments of eleven experts in the BCMSS of the JSLH was 95.8% (87.5-100.0), and the average specificity was 97.6% (92.5-100.0).

CONCLUSIONS

The CM-Tx method is an effective tool for quantitative distinction of EPC with their morphological features.

Algorithm for Analyzing Thermal Images of Laser Irradiated Human Skin

Introduction: Tracking temporal changes of temperature during laser skin treatment plays an important role in improving the process of laser skin treatment itself. There are a number of methods to analyze temperature's temporal dependency during laser skin treatment; some of those methods depend on imaging the skin with thermal cameras. However, the use of thermal cameras exhibits specific problems, including the ability to track laser-skin interaction spot. This paper is dedicated to solve that problem using digital image processing program coded with Matlab. Methods: The measurements were taken for 15 native Syrian subjects of different sex, age and skin tones, the treated ailment was port wine stain. The clinical work (laser exposure) was performed in Damascus University, hospital of dermatology. The treatment was observed by thermal camera and analyzed using the proposed Matlab coded tracking system. Results: For all the subjects, the treatment laser spot was tracked and the curves of skin temperature change with time where calculated by the use of the proposed algorithm, then the active time was calculated for each subject. The algorithm proved practical and robust. Conclusion: The proposed algorithm proved to be efficient and can be used to support future researchers with capability to measure the temperature with high frame rate.

Automatic and quantitative measurement of laryngeal video stroboscopic images

The laryngeal video stroboscope is an important instrument for physicians to analyze abnormalities and diseases in the glottal area. Stroboscope has been widely used around the world. However, without quantized indices, physicians can only make subjective judgment on glottal images. We designed a new laser projection marking module and applied it onto the laryngeal video stroboscope to provide scale conversion reference parameters for glottal imaging and to convert the physiological parameters of glottis. Image processing technology was used to segment the important image regions of interest. Information of the glottis was quantified, and the vocal fold image segmentation system was completed to assist clinical diagnosis and increase accuracy. Regarding image processing, histogram equalization was used to enhance glottis image contrast. The center weighted median filters image noise while retaining the texture of the glottal image. Statistical threshold determination was used for automatic segmentation of a glottal image. As the glottis image contains saliva and light spots, which are classified as the noise of the image, noise was eliminated by erosion, expansion, disconnection, and closure techniques to highlight the vocal area. We also used image processing to automatically identify an image of vocal fold region in order to quantify information from the glottal image, such as glottal area, vocal fold perimeter, vocal fold length, glottal width, and vocal fold angle. The quantized glottis image database was created to assist physicians in diagnosing glottis diseases more objectively.

Characterization and automatic screening of reactive and abnormal neoplastic B lymphoid cells from peripheral blood

INTRODUCTION

The objective was to advance in the automatic, image-based, characterization and recognition of a heterogeneous set of lymphoid cells from peripheral blood, including normal, reactive, and five groups of abnormal lymphocytes: hairy cells, mantle cells, follicular lymphoma, chronic lymphocytic leukemia, and prolymphocytes.

METHODS

A number of 4389 images from 105 patients were selected by pathologists, based on morphologic visual appearance, from patients whose diagnosis was confirmed by all the remaining complementary tests. Besides geometry, new color and texture features were extracted using six alternative color spaces to obtain rich information to characterize the cell groups. The recognition system was designed using support vector machines trained with the whole image set.

RESULTS

In the experimental tests, individual sets of images from 21 new patients were analyzed by the trained recognition system and compared with the true diagnosis. An overall recognition accuracy of 97.67% was achieved when the cell screening was performed into three groups: normal lymphocytes, abnormal lymphoid cells, and reactive lymphocytes. The accuracy of the whole experimental study was 91.23% when considering the further discrimination of the abnormal lymphoid cells into the specific five groups.

CONCLUSION

The excellent automatic screening of the three groups of normal, reactive, and abnormal lymphocytes is useful as it discriminates between malignancy and not malignancy. The discrimination of the five groups of abnormal lymphoid cells is encouraging toward the idea that the system could be an automated image-based screening method to identify blood involvement by a variety of B lymphomas.

LPI Radar Waveform Recognition Based on Time-Frequency Distribution

In this paper, an automatic radar waveform recognition system in a high noise environment is proposed. Signal waveform recognition techniques are widely applied in the field of cognitive radio, spectrum management and radar applications, etc. We devise a system to classify the modulating signals widely used in low probability of intercept (LPI) radar detection systems. The radar signals are divided into eight types of classifications, including linear frequency modulation (LFM), BPSK (Barker code modulation), Costas codes and polyphase codes (comprising Frank, P1, P2, P3 and P4). The classifier is Elman neural network (ENN), and it is a supervised classification based on features extracted from the system. Through the techniques of image filtering, image opening operation, skeleton extraction, principal component analysis (PCA), image binarization algorithm and Pseudo–Zernike moments, etc., the features are extracted from the Choi–Williams time-frequency distribution (CWD) image of the received data. In order to reduce the redundant features and simplify calculation, the features selection algorithm based on mutual information between classes and features vectors are applied. The superiority of the proposed classification system is demonstrated by the simulations and analysis. Simulation results show that the overall ratio of successful recognition (RSR) is 94.7% at signal-to-noise ratio (SNR) of −2 dB.

Semi-Automatic Rating Method for Neutrophil Alkaline Phosphatase Activity

BACKGROUND

The neutrophil alkaline phosphatase (NAP) score is a valuable test for the diagnosis of myeloproliferative neoplasms, but it has still manually rated. Therefore, we developed a semi-automatic rating method using Photoshop^® and Image-J, called NAP-PS-IJ.

METHODS

Neutrophil alkaline phosphatase staining was conducted with Tomonaga's method to films of peripheral blood taken from three healthy volunteers. At least 30 neutrophils with NAP scores from 0 to 5+ were observed and taken their images. From which the outer part of neutrophil was removed away with Image-J. These were binarized with two different procedures (P1 and P2) using Photoshop^® . NAP-positive area (NAP-PA) and granule (NAP-PGC) were measured and counted with Image-J.

RESULTS

The NAP-PA in images binarized with P1 significantly (P < 0.05) differed between images with NAP scores from 0 to 3+ (group 1) and those from 4+ to 5+ (group 2). The original images in group 1 were binarized with P2. NAP-PGC of them significantly (P < 0.05) differed among all four NAP score groups. The mean NAP-PGC with NAP-PS-IJ indicated a good correlation (r = 0.92, P < 0.001) to results by human examiners.

CONCLUSIONS

The sensitivity and specificity of NAP-PS-IJ were 60% and 92%, which might be considered as a prototypic method for the full-automatic rating NAP score.

Enhancement of Corneal Visibility in Optical Coherence Tomography Images Using Corneal Adaptive Compensation

PURPOSE

To improve the contrast of optical coherence tomography (OCT) images of the cornea (post processing).

METHODS

We have recently developed standard compensation (SC) algorithms to remove light attenuation artifacts. A more recent approach, namely adaptive compensation (AC), further limited noise overamplification within deep tissue regions. AC was shown to work efficiently when all A-scan signals were fully attenuated at high depth. But in many imaging applications (e.g., OCT imaging of the cornea), such an assumption is not satisfied, which can result in strong noise overamplification. A corneal adaptive compensation (CAC) algorithm was therefore developed to overcome such limitation. CAC benefited from local A-scan processing (rather than global as in AC) and its performance was compared with that of SC and AC using Fourier-domain OCT images of four human corneas.

RESULTS

CAC provided considerably superior image contrast improvement than SC or AC did, with excellent visibility of the corneal stroma, low noise overamplification, homogeneous signal amplification, and high contrast. Specifically, CAC provided mean interlayer contrasts (a measure of high stromal visibility and low noise) greater than 0.97, while SC and AC provided lower values ranging from 0.38 to 1.00.

CONCLUSION

CAC provided considerable improvement compared with SC and AC by eliminating noise overamplification, while maintaining all benefits of compensation, thus making the corneal endothelium and corneal thickness easily identifiable.

TRANSLATIONAL RELEVANCE

CAC may find wide applicability in clinical practice and could contribute to improved morphometric and biomechanical understanding of the cornea.

Hyperspectral imaging techniques for the characterization of Haematococcus pluvialis (Chlorophyceae)

A hyperspectral imaging camera was combined with a bright-field microscope to investigate the intracellular distribution of pigments in cells of the green microalga Haematococcus pluvialis, a synonym for H. lacustris (Chlorophyceae). We applied multivariate curve resolution to the hyperspectral image data to estimate the pigment contents in culture and revealed that the predicted values were consistent with actual measurements obtained from extracted pigments. Because it was possible to estimate pigment contents in every pixel, the intracellular distribution of the pigments was investigated during various life-cycle stages. Astaxanthin was localized specifically at the eyespot of zoospores in early culture stages. Then, it became widely distributed in cells, but subsequently localized differently than the chl. Integrated with our recently developed image-processing program "HaematoCalMorph," the hyperspectral imaging system was useful for monitoring intracellular distributions of pigments during culture as well as for studying cellular responses under various conditions.

Sequential processing of quantitative phase images for the study of cell behaviour in real-time digital holographic microscopy

Transmitted light holographic microscopy is particularly used for quantitative phase imaging of transparent microscopic objects such as living cells. The study of the cell is based on extraction of the dynamic data on cell behaviour from the time-lapse sequence of the phase images. However, the phase images are affected by the phase aberrations that make the analysis particularly difficult. This is because the phase deformation is prone to change during long-term experiments. Here, we present a novel algorithm for sequential processing of living cells phase images in a time-lapse sequence. The algorithm compensates for the deformation of a phase image using weighted least-squares surface fitting. Moreover, it identifies and segments the individual cells in the phase image. All these procedures are performed automatically and applied immediately after obtaining every single phase image. This property of the algorithm is important for real-time cell quantitative phase imaging and instantaneous control of the course of the experiment by playback of the recorded sequence up to actual time. Such operator's intervention is a forerunner of process automation derived from image analysis. The efficiency of the propounded algorithm is demonstrated on images of rat fibrosarcoma cells using an off-axis holographic microscope.

Design of a test system for the development of advanced video chips and software algorithms

BACKGROUND

Visual deterioration is a crucial point in minimally invasive surgery impeding surgical performance. Modern image processing technologies appear to be promising approaches for further image optimization by digital elimination of disturbing particles. To make them mature for clinical application, an experimental test environment for evaluation of possible image interferences would be most helpful.

METHODS

After a comprehensive review of the literature (MEDLINE, IEEE, Google Scholar), a test bed for generation of artificial surgical smoke and mist was evolved. Smoke was generated by a fog machine and mist produced by a nebulizer. The size of resulting droplets was measured microscopically and compared with biological smoke (electrocautery) and mist (ultrasound dissection) emerging during minimally invasive surgical procedures.

RESULTS

The particles resulting from artificial generation are in the range of the size of biological droplets. For surgical smoke, the droplet dimension produced by the fog machine was 4.19 µm compared with 4.65 µm generated by electrocautery during a surgical procedure. The size of artificial mist produced by the nebulizer ranged between 45.38 and 48.04 µm compared with the range between 30.80 and 56.27 µm that was generated during minimally invasive ultrasonic dissection.

CONCLUSION

A suitable test bed for artificial smoke and mist generation was developed revealing almost identical droplet characteristics as produced during minimally invasive surgical procedures. The possibility to generate image interferences comparable to those occurring during laparoscopy (electrocautery and ultrasound dissection) provides a basis for the future development of image processing technologies for clinical applications.

Special raster scanning for reduction of charging effects in scanning electron microscopy

A special raster scanning (SRS) method for reduction of charging effects is developed for the field of SEM. Both a conventional fast scan (horizontal direction) and an unusual scan (vertical direction) are adopted for acquiring raw data consisting of many sub-images. These data are converted to a proper SEM image using digital image processing techniques. About sharpness of the image and reduction of charging effects, the SRS is compared with the conventional fast scan (with frame-averaging) and the conventional slow scan. Experimental results show the effectiveness of SRS images. By a successful combination of the proposed scanning method and low accelerating voltage (LV)-SEMs, it is expected that higher-quality SEM images can be more easily acquired by the considerable reduction of charging effects, while maintaining the resolution.

Eccentricity error identification and compensation for high-accuracy 3D optical measurement

The circular target has been widely used in various three-dimensional optical measurements, such as camera calibration, photogrammetry and structured light projection measurement system. The identification and compensation of the circular target systematic eccentricity error caused by perspective projection is an important issue for ensuring accurate measurement. This paper introduces a novel approach for identifying and correcting the eccentricity error with the help of a concentric circles target. Compared with previous eccentricity error correction methods, our approach does not require taking care of the geometric parameters of the measurement system regarding target and camera. Therefore, the proposed approach is very flexible in practical applications, and in particular, it is also applicable in the case of only one image with a single target available. The experimental results are presented to prove the efficiency and stability of the proposed approach for eccentricity error compensation.

Computer-aided detection of architectural distortion in prior mammograms of interval cancer

Architectural distortion is an important sign of breast cancer, but because of its subtlety, it is a common cause of false-negative findings on screening mammograms. This paper presents methods for the detection of architectural distortion in mammograms of interval cancer cases taken prior to the detection of breast cancer using Gabor filters, phase portrait analysis, fractal analysis, and texture analysis. The methods were used to detect initial candidates for sites of architectural distortion in prior mammograms of interval cancer and also normal control cases. A total of 4,224 regions of interest (ROIs) were automatically obtained from 106 prior mammograms of 56 interval cancer cases, including 301 ROIs related to architectural distortion, and from 52 prior mammograms of 13 normal cases. For each ROI, the fractal dimension and Haralick's texture features were computed. Feature selection was performed separately using stepwise logistic regression and stepwise regression. The best results achieved, in terms of the area under the receiver operating characteristics curve, with the features selected by stepwise logistic regression are 0.76 with the Bayesian classifier, 0.73 with Fisher linear discriminant analysis, 0.77 with an artificial neural network based on radial basis functions, and 0.77 with a support vector machine. Analysis of the performance of the methods with free-response receiver operating characteristics indicated a sensitivity of 0.80 at 7.6 false positives per image. The methods have good potential in detecting architectural distortion in mammograms of interval cancer cases.

Surface characterization of proteins using multi-fractal property of heat-denatured aggregates

Multi-fractal property of heat-denatured protein aggregates (HDPA) is characteristic of its individual form. The visual similarity between digitally generated microscopic images of HDPA with that of surface-image of its individual X-ray structures in protein databank (PDB) displayed using Visual Molecular Dynamics (VMD) viewer is the basis of the study. We deigned experiments to view the fractal nature of proteins at different aggregate scales. Intensity based multi-fractal dimensions (ILMFD) extracted from various planes of digital microscopic images of protein aggregates were used to characterize HDPA into different classes. Moreover, the ILMFD parameters extracted from aggregates show similar classification pattern to digital images of protein surface displayed by VMD viewer using PDB entry. We discuss the use of irregular patterns of heat-denatured aggregate proteins to understand various surface properties in native proteins.

Verification of prostate treatment setup using computed radiography for portal imaging

A non-film-based system was used to obtain high quality portal film images. Digital portal images were obtained with a computed radiography (CR) system, in which the film is replaced with a photostimulable phosphor plate. Digital processing of portal images enhanced the display contrast using regional histogram equalization. The images were compared to images on radiographic film, exposed in the same cassette. The contrast-enhanced CR images of prostate treatment fields facilitated identification of the entire contour of the ischium, the location of the pubic symphysis, and the ischial tuberosity to determine the anterior and inferior locations of the prostate and bladder. Identifying the coccyx on the processed portal images permits the physician to locate accurately the posterior wall of the rectum. In each case the quality of the CR image was judged by the clinician to be superior to conventional portal film. The identification of these anatomical structures on the portal images is clinically important for verifying 3D conformal therapy of the prostate. With the same CR system one may acquire digital treatment portal and simulation images. This provides a foundation for a picture archival communication system for radiation oncology. Existing software can be used to register these digital portal and simulation images to facilitate verification of treatment setup.

Fractal analyses of osseous healing using tuned aperture computed tomography images

The aim of this study was to evaluate osseous healing in mandibular defects using fractal analyses on conventional radiographs and tuned aperture computed tomography (TACT; OrthoTACT, Instrumentarium Imaging, Helsinki, Finland) images. Eighty test sites on the inferior margins of rabbit mandibles were subject to lesion induction and treated with one of the following: no treatment (controls); osteoblasts only; polymer matrix only; or osteoblast-polymer matrix (OPM) combination. Images were acquired using conventional radiography and TACT, including unprocessed TACT (TACT-U) and iteratively restored TACT (TACT-IR). Healing was followed up over time and images acquired at 3, 6, 9, and 12 weeks post-surgery. Fractal dimension (FD) was computed within regions of interest in the defects using the TACT workbench. Results were analyzed for effects produced by imaging modality, treatment modality, time after surgery and lesion location. Histomorphometric data were available to assess ground truth. Significant differences (p < 0.0001) were noted based on imaging modality with TACT-IR recording the highest mean fractal dimension (MFD), followed by TACT-U and conventional images, in that order. Sites treated with OPM recorded the highest MFDs among all treatment modalities (p < 0.0001). The highest MFD based on time was recorded at 3 weeks and differed significantly with 12 weeks (p < 0.035). Correlation of FD with results of histomorphometric data was high (r = 0.79; p < 0.001). The FD computed on TACT-IR showed the highest correlation with histomorphometric data, thus establishing the fact TACT is a more efficient and accurate imaging modality for quantification of osseous changes within healing bony defects.

Angiographic film subtraction using a laser digitizer and computer processing

Digital subtraction angiography has been accepted as an invaluable clinical tool over the past decade; however, film-screen-based angiography is still performed routinely when high-resolution or large field-of-view angiograms are needed. A technique is presented whereby two films from an angiographic sequence are digitized using a high-resolution laser digitizer, and the digitized images are aligned, subtracted, and displayed using the computer. To accommodate for some types of patient motion, an image warping algorithm is presented and discussed in detail. The warping algorithm is piecewise linear, using triangular regions for warping, resulting in a global nonlinear transform across triangle elements. An algorithm describing optimal triangle selection also is discussed. The results show that subtraction images of excellent quality can be produced by the proposed technique, and suggest that, in some settings, digitized subtraction films may be preferred over conventional film subtraction.

Clinical trial of panoramic dental radiography using a CCD receptor

The objective of this study was to evaluate the perceived clinical efficacy of a charge-coupled device (CCD) detector for panoramic radiography by comparing the images produced to conventional film/screen radiographs using the same machine and patient population. For clinical evaluation, 18 criteria were selected. These included overall assessment of the area of coverage, clarity of dental structures, clarity of bony outlines, specific anatomic details such as the maxillary sinus floor, mandibular canal and mandibular condyle, and region-by-region assessment of the dentition. Observers acted independently using identical optimal viewing conditions. Film and digital radiographs were evaluated separately. A five interval Likert rating scale was used. Digital images were rated superior to the conventional film radiographs for 14 criteria. Film radiographs marginally outperformed digital images for three criteria. For one criterion (periodontal bone status) the two modalities showed no difference in terms of the means ratings. It was concluded that digital images are clinically equivalent to conventional film/screen images for panoramic dental radiography.

Image processing assessment of femoral osteopenia

Visual assessment of femoral osteopenia (the radiographic presentation of osteoporosis) is unreliable. Many of the short-comings of observer grading can be overcome by digital image analysis. Our group has developed algorithms to make automatic assessment of osteopenia from clinical radiographs. Texture Analysis Models (TA) commonly used in image analysis were investigated as measures of osteopenia. Unlike densitometric methods, TA characterizes properties of the structure of the image (ie, trabecular patterns). A group of women were analyzed whose subjects ranged from those at risk of osteoporosis (n = 24) to normal (n = 40). Using an IBM PC, frame-grabber, camera, and light-box, we appraised five statistical TA algorithms for assessment of the femoral neck in standard pelvic radiographs: (1) Fractal Signature (FS) describes the image's fractal nature. (2) Auto-Correlation of unaltered and Sobel Edge Transformed images (ACSE) measures image spatial self-similarity. (3) Co-occurrence Matrices (CM) gives the joint probability of greylevels with distance/direction and describes statistical relationships of image variation. (4) Textural Spectrum (TS) neighborhood pixel relationships measure regional directional and pixel-inversion properties. (5) Eular Numbers (EN) describe texture by properties (such as connectivity) of binary images. Good reproducibility from repeated analysis of radiographs was shown using both paired t-tests and Altman-Bland's methods. We have shown a correlation between femoral neck bone mineral density (BMD-the "gold standard" of osteoporosis assessment) and textural measures for all five algorithms. Significant measures of osteopenia were: ACSE (r = 0.6, P < .001), CM (r = -0.69, P < .001), FS (r = 0.35, P < .01), TS (r = 0.52, P < .001) and EN (r = -0.39, P < .01). Relationships were also found between textural characteristics and age/weight. TA techniques characterize the radiographic changes of bone in osteoporosis. Technology based on these ideas may have a place alongside BMD measurements in the assessment of this condition.

Quantitative measurement of cell motility associated with transformed phenotype

The motility of individual mammalian cells is crucial for many biological processes. This report describes a new technique to quantitate cell motility, momentary alterations of cell shape, based on trace images obtained by video-image analyses and computer techniques. By means of this system, quantitation of cell motility could be automatically done without human observation or subjective judgement. Quantitative data from transformed and nontransformed rodent fibroblasts revealed that the cell motility measured here was related to the expression of such transformed phenotypes as morphological changes and tumorigenicity.