Computer-Aided Discrimination of Glaucoma Patients from Healthy Subjects Using the RETeval Portable Device.

Glaucoma is a chronic, progressive eye disease affecting the optic nerve, which may cause visual damage and blindness. In this study, we present a machine-learning investigation to classify patients with glaucoma (case group) with respect to normal participants (control group). We examined 172 eyes at the Ophthalmology Clinic of the "Elpis" General Hospital of Athens between October 2022 and September 2023. In addition, we investigated the glaucoma classification in terms of the following: (a) eye selection and (b) gender. Our methodology was based on the features extracted via two diagnostic optical systems: (i) conventional optical coherence tomography (OCT) and (ii) a modern RETeval portable device. The machine-learning approach comprised three different classifiers: the Bayesian, the Probabilistic Neural Network (PNN), and Support Vectors Machines (SVMs). For all cases examined, classification accuracy was found to be significantly higher when using the RETeval device with respect to the OCT system, as follows: 14.7% for all participants, 13.4% and 29.3% for eye selection (right and left, respectively), and 25.6% and 22.6% for gender (male and female, respectively). The most efficient classifier was found to be the SVM compared to the PNN and Bayesian classifiers. In summary, all aforementioned comparisons demonstrate that the RETeval device has the advantage over the OCT system for the classification of glaucoma patients by using the machine-learning approach.

An Experimental Platform for Tomographic Reconstruction of Tissue Images in Brightfield Microscopy.

(1) Background: Reviewing biological material under the microscope is a demanding and time-consuming process, prone to diagnostic pitfalls. In this study, a methodology for tomographic imaging of tissue sections is presented, relying on the idea that each tissue sample has a finite thickness and, therefore, it is possible to create images at different levels within the sample, revealing details that would probably not be seen otherwise. (2) Methods: Optical slicing was possible by developing a custom-made microscopy stage controlled by an ARDUINO. The custom-made stage, besides the normal sample movements that it should provide along the x-, y-, and z- axes, may additionally rotate the sample around the horizontal axis of the microscope slide. This rotation allows the conversion of the optical microscope into a CT geometry, enabling optical slicing of the sample using projection-based tomographic reconstruction algorithms. (3) Results: The resulting images were of satisfactory quality, but they exhibited some artifacts, which are particularly evident in the axial plane images. (4) Conclusions: Using classical tomographic reconstruction algorithms at limited angles, it is possible to investigate the sample at any desired optical plane, revealing information that would be difficult to identify when focusing only on the conventional 2D images.

Radiomics Texture Analysis of Bone Marrow Alterations in MRI Knee Examinations.

Accurate diagnosis and timely intervention are key to addressing common knee conditions effectively. In this work, we aim to identify textural changes in knee lesions based on bone marrow edema (BME), injury (INJ), and osteoarthritis (OST). One hundred and twenty-one MRI knee examinations were selected. Cases were divided into three groups based on radiological findings: forty-one in the BME, thirty-seven in the INJ, and forty-three in the OST groups. From each ROI, eighty-one radiomic descriptors were calculated, encoding texture information. The results suggested differences in the texture characteristics of regions of interest (ROIs) extracted from PD-FSE and STIR sequences. We observed that the ROIs associated with BME exhibited greater local contrast and a wider range of structural diversity compared to the ROIs corresponding to OST. When it comes to STIR sequences, the ROIs related to BME showed higher uniformity in terms of both signal intensity and the variability of local structures compared to the INJ ROIs. A combined radiomic descriptor managed to achieve a high separation ability, with AUC of 0.93 ± 0.02 in the test set. Radiomics analysis may provide a non-invasive and quantitative means to assess the spatial distribution and heterogeneity of bone marrow edema, aiding in its early detection and characterization.

The Biomedical Engineering Labor Market in Greece: A Survey Investigating Job Outlook, Satisfaction and Placement.

In this study, we have evaluated the real-world conditions, the job outlook and the job satisfaction in the Biomedical Engineering (BME) sector in Greece on the basis of the experience of about 12% of the graduates of the BME Department of the University of West Attica, Greece. An anonymous online questionnaire, implemented on the Microsoft Forms platform using multiple choice questions, short text answers and Likert-based scales, became publicly available to the graduates of the BME department. About 12% of the department's graduates responded to the survey. Results show that the time to first employment is very fast for both men and women. About 51.4% of men and 69.4% of women find their first job employment in the BME sector even before their graduation. The internship is considered important for first job placement by more than 50.6% of participants. BME jobs are perceived as most interesting (73.6%), in a good environment (71.9%), with satisfactory career prospects (45.9%), with satisfactory monthly net salary (44.2%) and satisfactory working hours (52.8%). Men are mostly employed in Service (40.5%), whereas women are mostly employed in Sales (33.3%). Most graduates with BSc degree are employed in Service (39.1%) and Sales (21.8%), most graduates with MSc degree are employed in Service (34.6%) and Hospitals/Health care centers (21.2%), and most graduates with PhD degree are employed in Academia and R&D (62.5%). Most well-paid participants (>1500 euros net salary) were PhD holders (71.5%), followed by MSc holders (25%) and BSc holders (16.2%). Maximum monthly salaries were found for those with more than 10 years of experience. In terms of BME sector, most well-paid participants (>1500 euros monthly net salary) are those working with R&D (86.7%), Sales (86.7%) and Management (60%). There is a high demand for biomedical engineers in the labor market in Greece, despite the continuing economic recession that the country is suffering from the past 12 years.

Three-dimensional tissue volume generation in conventional brightfield microscopy.

The purpose of the study is to develop and automate a series of steps for enabling digital 3D tissue volume generation in conventional Brightfield microscopy for histopathology applications. Tissue samples were retrieved from the General Hospital of Athens "Hippocration", Greece. Samples were placed on a microtome that produced consecutive 2 µm sections. Each section was stained using Hematoxylin and Eosin and placed on microscope slides. A histopathologist specified the region of interest (ROI) on each slide. A 2D image was created from each ROI using a LEICA DM2500 microscope with a LEICA DFC 420C camera. Τhe 3D volume was created by stacking consecutive 2D images using a deep learning image interpolation method. The reconstructed 3D tissue volumes were evaluated by an expert histopathologist. Results showed that the 3D volumes might reveal information that is not clearly visible or even undetectable in the conventional 2D Brightfield images. In contrast to other 3D tissue imaging technologies, the proposed method (a) does not depend on the distance of the sample from the objectives producing 3D tissue volumes at any desired magnification, (b) does not require a special instrument, it may be implemented with any conventional Brightfield microscope, and (c) can be used for any given routine application, not only for some specialized clinical studies. The proposed study provides the basis for a feasible, cost-less and time-less upgrade of any standard 2D microscope into a 3D imaging instrument that may enhance the quality of diagnostic assessments in histopathology. HIGHLIGHTS: A method for 3D tissue volume generation. 3D volumes reveal information not clearly visible or even undetectable in 2D images. A method for feasible, cost-less and time-less upgrade of any Brightfield 2D microscope into a 3D imaging instrument.

Employing machine learning and microscopy images of AIB1-stained biopsy material to assess the 5-year survival of patients with colorectal cancer.

Our purpose was to employ microscopy images of amplified in breast cancer 1 (AIB1)-stained biopsy material of patients with colorectal cancer (CRC) to: (a) find statistically significant differences (SSDs) in the texture and color of the epithelial gland tissue, between 5-year survivors and non-survivors after the first diagnosis and (b) employ machine learning (ML) methods for predicting the CRC-patient 5-year survival. We collected biopsy material from 54 patients with diagnosed CRC from the archives of the University Hospital of Patras, Greece. Twenty-six of the patients had survived 5 years after the first diagnosis. We selected regions of interest containing the epithelial gland at different microscope lens magnifications. We computed 69 textural and color features. Furthermore, we identified features with SSDs between the two groups of patients and we designed a supervised ML system for predicting the CRC-patient 5-year survival. Additionally, we employed the VGG16 pretrained convolution neural network to extract deep learning (DL) features, the support vector machines classifier, and the bootstrap cross-validation method for boosting the accuracy of predicting 5-year survival. Fourteen features sustained SSDs between the two groups of patients. The supervised ML system achieved 87% accuracy in predicting 5-year survival. In comparison, the DL system, using images from all magnifications, gave 97% classification accuracy. Glandular texture in 5-year non-survivors appeared to be of lower contrast, coarseness, roughness, local pixel correlation, and lower AIB1 variation, all indicating loss of textural definition. The supervised ML system revealed useful information regarding features that discriminate between 5-year survivors and non-survivors while the DL system displayed superior accuracy by employing DL features.

Assessment of HPV Risk Type in H&E-stained Biopsy Specimens of the Cervix by Microscopy Image Analysis.

OBJECTIVES: The objective of this study was (a) to identify, by computer processing of digitized images of hematoxylin and eosin (H&E)-stained biopsy material of the cervix, differences in the structure of nuclei between high-risk (HR) and low-risk (LR) human papillomavirus virus (HPV) types and (b) to assess the HPV risk type by designing a decision-support system (DSS). MATERIALS AND METHODS: Clinical material comprised H&E-stained biopsies from squamous intraepithelial lesions of 55 patients with polymerase chain reaction-verified HR-HPV (26 patients) or LR-HPV (29 patients) infection. From each patient's biopsy specimen, we digitized 1 region of interest, guided by the expert physician. After the segmentation of nuclei, we quantified from each nucleus 77 textural and morphologic features. We represented each patient by a 77-feature vector, the feature means of all nuclei, and we created 2 classes for HR-HPV and LR-HPV types. We carried out (a) a statistical analysis to determine features with statistically significant differences between the 2 classes and (b) a discriminant analysis, by designing a DSS, to estimate the HPV risk type. RESULTS: Statistical analysis revealed 40 features with between-classes statistically significant differences and discriminant analysis showed that the best DSS design achieved a high accuracy of about 93% in identifying the HPV risk type on data not used in the design of the DSS. CONCLUSIONS: Nuclei of HR-HPV types were of higher intensity, contained larger structures, had higher edges, were coarser, rougher, had higher contrast, were larger, and attained more irregular shapes. The proposed DSS indicates that discrimination of HPV risk type from images of H&E-stained biopsy material of the cervix is promising.

GPU-enabled design of an adaptable pattern recognition system for discriminating squamous intraepithelial lesions of the cervix.

The aim of the present study was to design an adaptable pattern recognition (PR) system to discriminate low- from high-grade squamous intraepithelial lesions (LSIL and HSIL, respectively) of the cervix using microscopy images of hematoxylin and eosin (H&E)-stained biopsy material from two different medical centers. Clinical material comprised H&E-stained biopsies of 66 patients diagnosed with LSIL (34 cases) or HSIL (32 cases). Regions of interest were selected from each patient's digitized microscopy images. Seventy-seven features were generated, regarding the texture, morphology and spatial distribution of nuclei. The probabilistic neural network (PNN) classifier, the exhaustive search feature selection method, the leave-one-out (LOO) and the bootstrap validation methods were used to design the PR system and to assess its precision. Optimal PR system design and evaluation were made feasible by the employment of graphics processing unit (GPU) and Compute Unified Device Architecture (CUDA) technologies. The accuracy of the PR-system was 93% and 88.6% when using the LOO and bootstrap validation methods, respectively. The proposed PR system for discriminating LSIL from HSIL of the cervix was designed to operate in a clinical environment, having the capability of being redesigned when new verified cases are added to its repository and when data from other medical centers are included, following similar biopsy material preparation procedures.

Correlating Changes in the Epithelial Gland Tissue With Advancing Colorectal Cancer Histologic Grade, Using IHC Stained for AIB1 Expression Biopsy Material.

OBJECTIVE: The objective of this study was to study the textural and color changes occurring in the epithelial gland tissue with advancing colorectal cancer (CRC), utilizing immunohistochemical stain for AIB1 expression biopsy material. MATERIAL AND METHODS: Clinical material comprised biopsy specimens of 67 patients with a diagnosis of CRC. Two experienced pathologists used H&E-stained material for grading CRC lesions and immunohistochemical (IHC) stain for AIB1 expression. Twenty six patients were diagnosed with grade I, 28 with grade II, and 13 with grade III CRC. Guided by pathologists, we selected the regions of interest from AIB1-digitized images of each patient, encompassing the epithelial gland, and we computed 69 features, quantifying textural and color properties of the AIB1-stained lesions. We evaluated the statistical differences between grades by means of the Wilcoxon statistical test for each feature, and we assessed changes in feature values with advancing tumor grade by means of the Point Biserial Correlation. RESULTS: Statistical analysis revealed 14 single features, quantifying textural and color properties of the epithelial gland, which sustained statistically significant differences between LG-CRC and HG-CRC cases. These features were drawn from the gray-level image histogram, the cooccurrence matrix, the run length matrix, the discrete wavelet transform, the Tamura method, and the L*a*b color transform. CONCLUSIONS: A systematic statistical analysis of AIB1-stained biopsy material showed that high-grade CRC lesions contain higher intensity levels, appear coarser, are more homogeneous with smooth variation across the image, have lower contrast that is slowly varying across the image, have lower AIB1 staining, and have lower edges. A combination of textural and color attributes, evaluating image gray-tone distribution, textural roughness, inhomogeneity, AIB1 staining, and image coarseness should be considered in evaluating AIB1-stained CRC lesions.

Multifeature Quantification of Nuclear Properties from Images of H&E-Stained Biopsy Material for Investigating Changes in Nuclear Structure with Advancing CIN Grade.

Background: Cervical dysplasia is a precancerous condition, and if left untreated, it may lead to cervical cancer, which is the second most common cancer in women. The purpose of this study was to investigate differences in nuclear properties of the H&E-stained biopsy material between low CIN and high CIN cases and associate those properties with the CIN grade. Methods: The clinical material comprised hematoxylin and eosin- (H&E-) stained biopsy specimens from lesions of 44 patients diagnosed with cervical intraepithelial neoplasia (CIN). Four or five nonoverlapping microscopy images were digitized from each patient's H&E specimens, from regions indicated by the expert physician. Sixty-three textural and morphological nuclear features were generated for each patient's images. The Wilcoxon statistical test and the point biserial correlation were used to estimate each feature's discriminatory power between low CIN and high CIN cases and its correlation with the advancing CIN grade, respectively. Results: Statistical analysis showed 19 features that quantify nuclear shape, size, and texture and sustain statistically significant differences between low CIN and high CIN cases. These findings revealed that nuclei in high CIN cases, as compared to nuclei in low CIN cases, have more irregular shape, are larger in size, are coarser in texture, contain higher edges, have higher local contrast, are more inhomogeneous, and comprise structures of different intensities. Conclusion: A systematic statistical analysis of nucleus features, quantified from the H&E-stained biopsy material, showed that there are significant differences in the shape, size, and texture of nuclei between low CIN and high CIN cases.

Image quilting and wavelet fusion for creation of synthetic microscopy nuclei images.

BACKGROUND AND OBJECTIVE: In this study a texture simulation methodology is proposed for composing synthetic tissue microscopy images that could serve as a quantitative gold standard for the evaluation of the reliability, accuracy and performance of segmentation algorithms in computer-aided diagnosis. METHODS: A library of background and nuclei regions was generated using pre-segmented Haematoxylin and Eosin images of brain tumours. Background image samples were used as input to an image quilting algorithm that produced the synthetic background image. Randomly selected pre-segmented nuclei were randomly fused on the synthetic background using a wavelet-based fusion approach. To investigate whether the produced synthetic images are meaningful and similar to real world images, two different tests were performed, one qualitative by an experienced histopathologist and one quantitative using the normalized mutual information and the Kullback-Leibler tests. To illustrate the challenges that synthetic images may pose to object recognition algorithms, two segmentation methodologies were utilized for nuclei detection, one based on the Otsu thresholding and another based on the seeded region growing approach. RESULTS: Results showed a satisfactory to good resemblance of the synthetic with the real world images according to both qualitative and quantitative tests. The segmentation accuracy was slightly higher for the seeded region growing algorithm (87.2%) than the Otsu's algorithm (86.3%). CONCLUSIONS: Since we know the exact coordinates of the regions of interest within the synthesised images, these images could then serve as a 'gold standard' for evaluation of segmentation algorithms in computer-aided diagnosis in tissue microscopy.

Adaptable pattern recognition system for discriminating Melanocytic Nevi from Malignant Melanomas using plain photography images from different image databases.

OBJECTIVE: The aim of this study was to propose features that evaluate pictorial differences between melanocytic nevus (mole) and melanoma lesions by computer-based analysis of plain photography images and to design a cross-platform, tunable, decision support system to discriminate with high accuracy moles from melanomas in different publicly available image databases. MATERIAL AND METHODS: Digital plain photography images of verified mole and melanoma lesions were downloaded from (i) Edinburgh University Hospital, UK, (Dermofit, 330moles/70 melanomas, under signed agreement), from 5 different centers (Multicenter, 63moles/25 melanomas, publicly available), and from the Groningen University, Netherlands (Groningen, 100moles/70 melanomas, publicly available). Images were processed for outlining the lesion-border and isolating the lesion from the surrounding background. Fourteen features were generated from each lesion evaluating texture (4), structure (5), shape (4) and color (1). Features were subjected to statistical analysis for determining differences in pictorial properties between moles and melanomas. The Probabilistic Neural Network (PNN) classifier, the exhaustive search features selection, the leave-one-out (LOO), and the external cross-validation (ECV) methods were used to design the PR-system for discriminating between moles and melanomas. RESULTS: Statistical analysis revealed that melanomas as compared to moles were of lower intensity, of less homogenous surface, had more dark pixels with intensities spanning larger spectra of gray-values, contained more objects of different sizes and gray-levels, had more asymmetrical shapes and irregular outlines, had abrupt intensity transitions from lesion to background tissue, and had more distinct colors. The PR-system designed by the Dermofit images scored on the Dermofit images, using the ECV, 94.1%, 82.9%, 96.5% for overall accuracy, sensitivity, specificity, on the Multicenter Images 92.0%, 88%, 93.7% and on the Groningen Images 76.2%, 73.9%, 77.8% respectively. CONCLUSION: The PR-system as designed by the Dermofit image database could be fine-tuned to classify with good accuracy plain photography moles/melanomas images of other databases employing different image capturing equipment and protocols.

Development of a Reference Image Collection Library for Histopathology Image Processing, Analysis and Decision Support Systems Research.

Histopathology image processing, analysis and computer-aided diagnosis have been shown as effective assisting tools towards reliable and intra-/inter-observer invariant decisions in traditional pathology. Especially for cancer patients, decisions need to be as accurate as possible in order to increase the probability of optimal treatment planning. In this study, we propose a new image collection library (HICL-Histology Image Collection Library) comprising 3831 histological images of three different diseases, for fostering research in histopathology image processing, analysis and computer-aided diagnosis. Raw data comprised 93, 116 and 55 cases of brain, breast and laryngeal cancer respectively collected from the archives of the University Hospital of Patras, Greece. The 3831 images were generated from the most representative regions of the pathology, specified by an experienced histopathologist. The HICL Image Collection is free for access under an academic license at http://medisp.bme.teiath.gr/hicl/ . Potential exploitations of the proposed library may span over a board spectrum, such as in image processing to improve visualization, in segmentation for nuclei detection, in decision support systems for second opinion consultations, in statistical analysis for investigation of potential correlations between clinical annotations and imaging findings and, generally, in fostering research on histopathology image processing and analysis. To the best of our knowledge, the HICL constitutes the first attempt towards creation of a reference image collection library in the field of traditional histopathology, publicly and freely available to the scientific community.

Computer-based automated estimation of breast vascularity and correlation with breast cancer in DCE-MRI images.

Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) with gadolinium constitutes one of the most promising protocols for boosting up the sensitivity in breast cancer detection. The aim of this study was twofold: first to design an image processing methodology to estimate the vascularity of the breast region in DCE-MRI images and second to investigate whether the differences in the composition/texture and vascularity of normal, benign and malignant breasts may serve as potential indicators regarding the presence of the disease. Clinical material comprised thirty nine cases examined on a 3.0-T MRI system (SIGNA HDx; GE Healthcare). Vessel segmentation was performed using a custom made modification of the Seeded Region Growing algorithm that was designed in order to identify pixels belonging to the breast vascular network. Two families of features were extracted: first, morphological and textural features from segmented images in order to quantify the extent and the properties of the vascular network; second, textural features from the whole breast region in order to investigate whether the nature of the disease causes statistically important changes in the texture of affected breasts. Results have indicated that: (a) the texture of vessels presents statistically significant differences (p<0.001) between normal, benign and malignant cases, (b) the texture of the whole breast region for malignant and non-malignant breasts, produced statistically significant differences (p<0.001), (c) the relative ratios of the texture between the two breasts may be used for the discrimination of non-malignant from malignant patients, and (d) an area under the receiver operating characteristic curve of 0.908 (AUC) was found when features were combined in a logistic regression prediction rule according to ROC analysis.

Computer-based image analysis system designed to differentiate between low-grade and high-grade laryngeal cancer cases.

OBJECTIVE: To design a pattern recognition (PR) system for discriminating between low- and high-grade laryngeal cancer cases, employing immunohistochemically stained, for p63 expression, histopathology images. STUDY DESIGN: The PR system was designed to assist in the physician's diagnosis for improving patient survival. The material comprised 55 verified cases of laryngeal cancer, 21 of low-grade and 34 of high-grade malignancy. Histopathology images were first processed for automatically segmenting p63 expressed nuclei. Fifty-two features were next extracted from the segmented nuclei, concerning nuclei texture, shape, and physical topology in the image. Those features and the Probabilistic Neural Network classifier were used to design the PR system on the multiprocessors of the Nvidia 580 GTX graphics processing unit (GPU) card using the Compute Unified Device Architecture parallel programming model and C++ programming language. RESULTS: PR system performance in classifying laryngeal cancer cases as low grade and high grade was 85.7% and 94.1%, respectively. The system's overall accuracy was 90.9%, using 7 features, and its estimated accuracy to "unseen" by the system cases was 80%. CONCLUSION: Optimum system design was feasible after employing parallel processing techniques and GPU technology. The proposed system was structured so as to function in a clinical environment, as a research tool, and with the capability of being redesigned on site when new verified cases are added to its repository.

Texture analysis of articular cartilage traumatic changes in the knee calculated from morphological 3.0T MR imaging.

OBJECTIVES: In the present work, we aim to identify changes in the cartilage texture of the injured knee in young, physically active, patients by computer analysis of MRI images based on 3.0T morphological sequences. METHODS: Fifty-three young patients with training injury or trauma in one knee underwent MRI and arthroscopy. Textural features were computed from the MRI images of the knee-cartilages and two classes were formed of 28 normal and 16 with pathology only in the medial femoral condyle (MFC) cartilage. RESULTS: Textural features with statistically significant differences between the two classes were found only at the MFC and the medial tibial condyle (MTC) areas. Three features-combinations, at the MFC or the MTC, maximized the between classes separation, thus, rendering alterations in cartilage texture due to injury more evident. The MFC cartilage in the pathology class was found more inhomogeneous in the distribution of gray-levels and of lower texture anisotropy and the opposed MTC cartilage, though normal on MRI and arthroscopy, was found to have lower texture anisotropy than cartilage in the normal class. CONCLUSION: Texture analysis may be used as an adjunct to morphological MR imaging for improving the detection of subtle cartilage changes and contributes to early therapeutic approach.

Multimodality GPU-based computer-assisted diagnosis of breast cancer using ultrasound and digital mammography images.

PURPOSE: To improve the computer-aided diagnosis of breast lesions, by designing a pattern recognition system (PR-system) on commercial graphics processing unit (GPU) cards using parallel programming and textural information from multimodality imaging. MATERIAL AND METHODS: Patients with histologically verified breast lesions underwent both ultrasound (US) and digital mammography (DM), lesions were outlined on the images by an experienced radiologist, and textural features were calculated. The PR-system was designed to provide highest possible precision by programming in parallel the multiprocessors of the NVIDIA's GPU cards, GeForce 8800GT or 580GTX, and using the CUDA programming framework and C++. The PR-system was built around the probabilistic neural network classifier, and its performance was evaluated by a re-substitution method, for estimating the system's highest accuracy, and by the external cross-validation method, for assessing the PR-system's unbiased accuracy to new, "unseen" by the system, data. RESULTS: Classification accuracies for discriminating malignant from benign lesions were as follows: 85.5 % using US-features alone, 82.3 % employing DM features alone, and 93.5 % combining US and DM features. Mean accuracy to new "unseen" data for the combined US and DM features was 81 %. Those classification accuracies were about 10 % higher than accuracies achieved on a single CPU, using sequential programming methods, and 150-fold faster. CONCLUSION: The proposed PR-system improves breast-lesion discrimination accuracy, it may be redesigned on site when new verified data are incorporated in its depository, and it may serve as a second opinion tool in a clinical environment.

Pattern-recognition system, designed on GPU, for discriminating between injured normal and pathological knee cartilage.

The aim was to design a pattern-recognition (PR) system for discriminating between normal and pathological knee articular cartilage of the medial femoral (MFC) and tibial condyles (MTC). The data set comprised segmented regions of interest (ROIs) from coronal and sagittal 3-T magnetic resonance images of the MFC and MTC cartilage of young patients, 28 with abnormality-free knee and 16 with pathological findings. The PR system was designed employing the probabilistic neural network classifier, textural features from the segmented ROIs and the leave-one-out evaluation method, while the PR system's precision to "unseen" data was assessed by employing the external cross-validation method. Optimal system design was accomplished on a consumer graphics processing unit (GPU) using Compute Unified Device Architecture parallel programming. PR system design on the GPU required about 3.5 min against 15 h on a CPU-based system. Highest classification accuracies for the MFC and MTC cartilages were 93.2% and 95.5%, and accuracies to "unseen" data were 89% and 86%, respectively. The proposed PR system is housed in a PC, equipped with a consumer GPU, and it may be easily retrained when new verified data are incorporated in its repository and may be of value as a second-opinion tool in a clinical environment.

A wavelet-based Markov random field segmentation model in segmenting microarray experiments.

In the present study, an adaptation of the Markov Random Field (MRF) segmentation model, by means of the stationary wavelet transform (SWT), applied to complementary DNA (cDNA) microarray images is proposed (WMRF). A 3-level decomposition scheme of the initial microarray image was performed, followed by a soft thresholding filtering technique. With the inverse process, a Denoised image was created. In addition, by using the Amplitudes of the filtered wavelet Horizontal and Vertical images at each level, three different Magnitudes were formed. These images were combined with the Denoised one to create the proposed SMRF segmentation model. For numerical evaluation of the segmentation accuracy, the segmentation matching factor (SMF), the Coefficient of Determination (r(2)), and the concordance correlation (p(c)) were calculated on the simulated images. In addition, the SMRF performance was contrasted to the Fuzzy C Means (FCM), Gaussian Mixture Models (GMM), Fuzzy GMM (FGMM), and the conventional MRF techniques. Indirect accuracy performances were also tested on the experimental images by means of the Mean Absolute Error (MAE) and the Coefficient of Variation (CV). In the latter case, SPOT and SCANALYZE software results were also tested. In the former case, SMRF attained the best SMF, r(2), and p(c) (92.66%, 0.923, and 0.88, respectively) scores, whereas, in the latter case scored MAE and CV, 497 and 0.88, respectively. The results and support the performance superiority of the SMRF algorithm in segmenting cDNA images.

Quantitative combination of volumetric MR imaging and MR spectroscopy data for the discrimination of meningiomas from metastatic brain tumors by means of pattern recognition.

The analysis of information derived from magnetic resonance imaging (MRI) and spectroscopy (MRS) has been identified as an important indicator for discriminating among different brain pathologies. The purpose of this study was to investigate the efficiency of the combination of textural MRI features and MRS metabolite ratios by means of a pattern recognition system in the task of discriminating between meningiomas and metastatic brain tumors. The data set consisted of 40 brain MR image series and their corresponding spectral data obtained from patients with verified tumors. The pattern recognition system was designed employing the support vector machines classifier with radial basis function kernel; the system was evaluated using an external cross validation process to render results indicative of the generalization performance to "unknown" cases. The combination of MR textural and spectroscopic features resulted in 92.15% overall accuracy in discriminating meningiomas from metastatic brain tumors. The fusion of the information derived from MRI and MRS data might be helpful in providing clinicians a useful second opinion tool for accurate characterization of brain tumors.