© IC-SCCE COMPARATIVE EVALUATION OF FEEDFORWARD AND PROBABILISTIC NEURAL NETWORKS FOR THE AUTOMATIC CLASSIFICATION OF BRAIN TUMOURS

Abstract. Brain tumours grading is a crucial step for determining treatment planning and patient management. The grade of a tumour is defined by pathologists after reviewing biopsies under the microscope, a procedure that has been proven highly subjective. In this work, we propose a computer-based system for the automatic classification of astrocytomas that can be used as a second opinion tool for the clinicians contributing to the objectification of the diagnostic process. The system process routine brain tumours biopsies and performs automatic diagnosis of the degree of tumour abnormality (low from high grade tumours) based solely on quantitative information acquired from cell nuclei. We designed the system incorporating two stat-of-art neural network algorithms, namely Perry’s nonmonotone spectral conjugate gradient training algorithm for Multi Layer Perceptrons (MLPs) and Probabilistic NN (PNN). Best performance was obtained using a MLP-NN classifier with 7-hidden neurons topology that discriminating low from high grade tumours with an accuracy of 92.0%. Sensitivity and specificity ranged to 93.1 % and 90.5 % respectively. The PNN classifier resulted in lower rates (83.3 % specificity, 91.5 % sensitivity and 89.9 % overall accuracy). The proposed method is a dynamic new alternative to brain tumour grading since it combines relatively high accuracy rates with daily clinical standards.

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

© IC-EpsMsO DEVELOPMENT OF A REMOTELY ACCOMPLISHED EDGE – DETECTION ALGORITHM ON BREAST ULTRASOUND

The development of an edge detection algorithm, accomplished remotely, utilizing a server and a client in a local network Material and Methods: 150 breast ultrasound (US) images, depicting various aspects of breast lesions, were captured employing a HDI-3000 ATL digital US system with 512x512x8 image resolution. The basic configuration comprised a client and a server. Image processing was distributed, both to the client and the server; the client pre-processed images in order to enhance the lesion boundary, while the server delineated the lesion boundary. Client-side processing involved: (i) median filtering, (ii) adaptive three-level wavelet-based soft-thresholding for de-speckling, and (iii) image histogram thresholding for lesion boundary enhancement. Server-side processing comprised dynamic contour processing for accurate lesion boundary detection. Results (boundary points) were sent back to the client, were superimposed on the original image, and were compared with the free-hand selection performed by the physician (N.D.). Results: Agreement between lesion boundaries selected by the physician and boundaries determined by the proposed algorithm was 92%. Overall processing time was less than 3s (including transfer time from Client to Server and vise-versa), requiring only minimum operator intervention. Conclusion: The proposed edge detection algorithm is fast an

A New Approach to Robust Clustering by Density Estimation in an Autocorrelation Derived Feature Space

Robust clustering techniques aim to classify objects into partitions that have meaning for the particular problem, while dealing with outliers contaminating data. In this paper, we propose a new robust clustering method based on the concept of density estimation in an autocorrelation derived feature space. In that feature space, clusters are better separated than in the original data space, making clustering easier. The autocorrelation features comprise the input to a new Probabilistic Neural Network motivated clustering algorithm. We show that the method can also be applied for outlier detection when only one class of data exists. The proposed method was tested with simulated data and real data collected from bioaffinity assay applications. The algorithm was able to separate the clusters despite of the presence of outliers in every case. In addition, we demonstrate that combinations of traditional robust estimation techniques and clustering algorithms (k-means, fuzzy k-means) failed to detect different populations of data when applied to the same datasets due to existence of outliers

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

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