Accurate early diagnosis is crucial in addressing Age-related Macular Degeneration (AMD), a chronic retinal disease that is a leading cause of blindness among the elderly. Medical imaging, particularly fundus imaging, is essential in facilitating timely detection and intervention. Due to the variability in image sizes within our dataset, this paper introduces the SABiT-MNet model, which effectively discriminates between healthy retinas, dry AMD, and wet AMD. The model integrates a novel scale-adaptive (SA) approach by combining an autoencoder with Big Transfer (BiT) as its backbone. Unlike traditional resizing methods, which often result in the loss of critical diagnostic information, the SA model dynamically adjusts to varying image sizes, preserving key retinal features essential for accurate diagnosis. The primary aim of this architecture is to retain crucial details in fundus images to ensure precise classification. In this study, 648 subjects were recruited through the Comparisons of AMD Treatments Trials study group, sponsored by the University of Pennsylvania. Experimental results demonstrate that the proposed SABiT-MNet model outperforms state-of-the-art approaches, including transformer-based models, achieving superior diagnostic accuracy. The model recorded performance metrics of 94% accuracy, 97% sensitivity, and 93.94% specificity. To further validate the robustness of the system, we tested it on the public ODiR dataset, where it achieved similarly promising results, confirming the effectiveness of our approach.

This paper presents AFMUNet, an Adaptive Filter-Based Frequency Modulation U-Net for Optical Coherence Tomography Angiography (OCTA) segmentation. The model addresses the challenge of segmenting both small blood vessels and larger vascular structures, which exhibit significant variations in scale, contrast, and connectivity. To tackle these issues, AFMUNet achieves an image-sized receptive field while capturing global dependencies, enhancing performance across diverse vessel sizes. Specifically, it incorporates the Fast Fourier Transform (FFT) applied to feature maps across multiple scales of a UNet-like architecture. This component is crucial for identifying critical global frequency patterns, enabling the model to represent the intricate details of small vessels alongside larger ones. Simultaneously, a lightweight attention block is employed to learn adaptive frequency filters from the FFT-derived representations. This mechanism selectively emphasizes transferable frequency components essential for highlighting small and thin vessels while suppressing noise and less informative features that could detract from the segmentation of large vascular structures. Experimental evaluations demonstrate that AFMUNet outperforms state-of-the-art models, achieving mean Dice and mean Intersection over Union (IoU) scores of 91.69% and 84.65%, respectively. These results underscore its robustness and superior ability to accurately segment OCTA images, effectively addressing the dual challenge of capturing both fine-grained and coarse vascular details.

This paper introduces a novel framework, termed RAW (Region Attention-Weighted Network with Inter-Region Information Exchange), specifically developed for Age-related Macular Degeneration (AMD) grading using high-resolution input images. The framework begins with a High-Fidelity Detail Retention (HFDR) block, designed to preserve critical image details essential for accurate analysis. Furthermore, a lightweight network, inspired by ResNet, is incorporated to facilitate efficient feature extraction. This is augmented by a Region Attention-Weighted (RAW) block, which highlights significant regions while mitigating interference from less relevant areas. To ensure the effective propagation of crucial information for precise AMD grading, we developed the Inter-Region Information Exchange (IRIE) block. The efficacy of the proposed RAW framework was evaluated using a dataset comprising 864 Color Retinal Fundus (CRF) images, demonstrating outstanding performance compared to existing methods. It achieved a mean accuracy of 98.08%, a mean F1-score of 98.06%, and a mean Cohen’s Kappa of 97.42%. Statistical analysis further confirms the framework’s significant advantage, particularly in terms of F1-score, underscoring its robustness and exceptional capability for AMD grading.

Breast cancer is a highly complex disease that requires precise molecular subtyping to guide tailored treatment strategies. In this study, we employed a marker-based watershed segmentation technique on a breast cancer dataset, enabling the extraction of essential morphometric parameters. These included area, perimeter, circularity, maximal and minimal calipers, and eccentricity, along with hematoxylin and diaminobenzidine (DAB) staining characteristics for individual cells, nuclei, and cytoplasm. We utilized Support Vector Machine (SVM) and Random Forest (RF) models to classify molecular biomarker response scores to identify their molecular subtypes, leveraging these extracted features as discriminative factors. The dataset comprised whole-slide images (WSI) annotated with Progestin Receptors (PR) molecular biomarker response scores, categorizing cell regions into different classes: "other", "tumor: negative", "tumor: +1", "tumor: +2", and "tumor: +3". These detailed annotations enhanced the AI-driven classification of breast cancer molecular biomarkers response score detection on cell level. Performance evaluation of the proposed framework demonstrated substantial classification accuracy, with SVM achieving over 93% in precision, recall, F1-score, and overall accuracy, while RF exceeded 96% across these metrics. The study's findings highlight the efficacy of integrating marker-based watershed segmentation with morphometric and staining analysis for precise breast cancer molecular biomarkers response score classification. This approach provides deeper insights into tumor heterogeneity, reinforcing the importance of incorporating morphometric and staining parameters in molecular biomarkers response score classification and hence improved personalized treatment and prognosis.

This paper introduces CrossDR, a novel approach unifying 2D and 3D feature representations through a context-aware cross-attention mechanism for diabetic retinopathy (DR) classification in 3D Optical Coherence Tomography (OCT) scans. The model uncovers critical diagnostic cues embedded in 3D-OCT images, which offer rich information for DR detection. The framework comprises three core components: the Lightweight Attention (LA) block, a CNN-based encoder, and the Context-Aware Cross-Attention (CA)2block. The LA block highlights volume-specific features that are crucial for DR diagnosis. It achieves this by applying a series of 1×1 convolutions followed by softmax operations, enabling the extraction of spatially informative DR-specific features. Meanwhile, the CNN-based encoder extracts high-level semantic features from the 3D-OCT slices, offering a robust representation of retinal structures. The (CA)2block further boosts the model’s performance by dynamically capturing and reweighting feature dependencies. This block models relationships between feature maps at different abstraction levels, improving the discriminative power of the extracted features. By integrating these refined features through the (CA)2block, the model achieves accurate DR classification. The CrossDR framework was evaluated on 481 volumetric OCT images, outperforming existing state-of-the-art methods with an accuracy of 94%. Multiple experiments and ablation studies were conducted to assess our model’s performance.

This study proposes a novel deep learning-based automatic segmentation system for accurately delineating pulmonary regions in 3D computed tomography (CT) scans. First, a modified local binary pattern, called cylinder binary pattern (CBP), is introduced, which utilizes concentric cylinders at different radii, to effectively capture intricate textural details at multiple levels. Then, a 3D encoder-decoder deep learning-based network, called VX-Net, is proposed specifically to accurately segment pulmonary regions within 3D CT scans. This network incorporates the hierarchical transformers into its encoder architecture to significantly improve feature extraction process. These transformers replicate Transformer model by integrating 3D convolutions with both larger and smaller kernels. While this network is used for segmenting pulmonary regions, it can also be adapted for various other segmentation tasks. The proposed system is evaluated on 3D CT scans of 26 patients with three different severity levels of COVID-19, using four distinct metrics. These include Dice similarity coefficient (DSC), overlap coefficient, Hausdorff distance (HD), and absolute volume difference (AVD). The proposed system shows remarkable performance, achieving scores of 97.63±0.98%, 95.38±1.86%, 1.99±1.9, and 3.01±1.15, respectively. When compared to various state-of-the-art segmentation methods, the proposed segmentation system showcases its ability to accurately segment both normal and pathological pulmonary regions.

This paper introduces a novel framework for renal segmentation of kidney transplant patients suspected of renal rejection. The framework applies image processing techniques for texture analysis utilizing a modified Pix 2 Pix GAN model to capture the varied kidney shapes in the dataset of 36 subject volumes acquired using BOLD MRI scans. For this problem, we built a framework that analyzes the kidney texture based on four steps: (i) calculate the average CDF for each case to map CDF values to their corresponding intensities for contrast enhancement (ii) extract the region of interest for the kidney to focus on the kidney structure, (iii) calculate the probability maps using the histograms of the contours for the kidney and non-kidney regions, (iv) Create a common-layer across the dataset using the masks by calculating the average of the pixel values of the images to accommodate the shared information within the mask images. Finally, stack the three layers to have the RGB channels contain relevant information about the renal dataset as input for the modified GAN model. The proposed framework achieved an average accuracy and Dice Similarity Coefficient: $90.3 \%$, and $83.1 \%$, respectively. The framework’s primary results underscore its efficiency in providing segmentation for renal diagnosis.

Breast cancer is a prevalent and diverse type of cancer that exhibits unique clinicopathologic characteristics, making the correct identification of its subtype critical to providing targeted treatment and increasing survival rates. This identification process involves testing for the presence of four key molecular biomarkers, namely estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and antigen Ki67. For accurate diagnosis ,the expertise of a pathologist and immunohistochemistry is required. To overcome this diagnostic challenge, we present a novel approach based on a deep learning pipeline for automated classification. Our approach can detect tumor and non-tumoral regions of the HER2 biomarker. Our deep learning framework comprises a Dense Convolutional Network (DenseNet), which process whole slide images (WSIs) of breast tissues, dividing them into patches for input into the network. Moreover, our approach provides both patchwise and pixelwise classification and analyzes ten WSIs of breast cancer histology. Our proposed approach generates an image map that classifies slide images on the pixel-level, detecting the status of hormone HER2 receptor as either positive or negative. The obtained results show that our deep learning-based approach has the potential to enhance the pathologist’s capabilities in diagnosing histopathological images with automated classification.

Prostate cancer (PCa) is a widespread type of cancer that leads to numerous fatalities and a high financial cost. The chance of survival for PCa patients increases when the disease is detected at an early stage. This study discusses the development of a non-invasive computer-aided diagnosis (CAD) system that utilizes intravoxel incoherent motion (IVIM) parameters to detect and diagnose prostate cancer. The study focuses on IVIM, which can separate the diffusion of water molecules in capillaries from the molecular diffusion outside of the vessels, and its diagnostic efficacy in the central and peripheral zones of prostate cancer. The study proposes a two-step segmentation approach for tumor detection, starting with the precise localization of the prostate gland using a robust level-sets technique and then using an Attention U-Net to extract the tumor-containing region of interest (ROI) from the segmented image. The study evaluates the performance of the CAD system, the best classifier and IVIM parameters for differentiation, and the diagnostic value of IVIM parameters compared to ADC. The results of this study contribute to the development of non-invasive methods for early prostate cancer detection and diagnosis. The IVIM (CZ + PZ) parameters that utilized the extra trees classifier (ETC) and were implemented without principal component analysis (PCA) and standardization scaling achieved the best metrics. They produced an accuracy of 84.62%, a balanced accuracy of 82.58%, a precision of 80%, a specificity of 67.86%, a sensitivity of 97.30%, an F1-score of 87.12%, an IoU of 78.26%, a ROC of 83.88%, and a weighted sum metric (WSM) of 82.79%.

A novel unsupervised-based segmentation method is introduced to accurately delineate the lung region in 3D CT images based on appearance and geometric models. First, a probabilistic model that utilized a linear combination of Gaussian (LCG) tuned by a modified expectation maximization (EM) algorithm, is employed to model the density distribution of 3D CT chest volume. Subsequently, the initial labeling of the 3D CT chest volume is mapped to a probability distribution based on a 3D Markov Gibbs random field (MGRF) for refining. Finally, a geometric model is employed to refine the proposed segmentation by interpolating/connecting two points on its boundary with high curvature. The effectiveness of the proposed approach on 3D computed tomography (CT) chest scans of 26 patients diagnosed with different severity of coronavirus disease 2019 (COVID-19) is evaluated using four different metrics: overlap coefficient, Dice similarity coefficient (DSC), absolute lung volume difference (ALVD), and 95th-percentile bidirectional Hausdorff distance (95thHD). The proposed method achieved 94.89%±2.39%, 97.36%±1.27%, 1.79±1.89, and 4.75±2.3, respectively. Compared to three state-of-the-art methods based on deep learning approaches, the proposed method achieved superior performance in segmenting pathological lung tissues, demonstrating the promising of the proposed segmentation system.

In this study, we propose a Computer-Aided Diagnostic (CAD) system to diagnose and understand autism spectrum disorder (ASD) using structural MRI (sMRI). Starting with identifying morphological anomalies within the cortical regions of ASD subjects. Every cortical feature receives a score corresponding to their contribution in diagnosing a subject to be ASD or typically developed (TD). Scores are determined by hyper-optimized machine learning (ML) classifiers. An early personalized diagnosis of ASD becomes possible by the proposed CAD system. The proposed framework implements multiple stages including cerebral cortex extraction from structure MRI (sMRI). Moreover, the proposed framework identify the altered brain regions. We can summarize this framework in the following procedures: i) Cerebral cortex segmentation, ii) Parcellation of the cortex to Desikan-Killiany (DK) atlas; iii) Annotating brain regions which are associated with ASD; iv) Blocking for the confounding effect of both age and sex; v) Tailoring ASD neuro-atlases; vi) Classifying ASD using neural networks (NN). We uti-lized Autism Brain Imaging Data Exchange (ABIDE I) dataset to test the proposed framework. The proposed achieved a balanced accuracy score of 97% ± 2%. In this study, we demonstrate the ability to describe specific developmental patterns of the brain in autism using tailored neuro-atlases, as well as, developing an objective CAD system using morphological features extracted from sMRI scans.

One thing that assists in automatic environmental monitoring is leaf segmentation. By segmenting a leaf, image-based leaf health assessment can be performed which is crucial in maintaining the effectiveness of the environmental balance. This paper presents a technique that serves an accurate framework for diseased leaf segmentation from Coloured imaged. In other words, this method works to use information generated from RGB images that we have stored in our data base to represent the current input image. To achieve such technique, four main steps were constructed: 1) Using contrast variations to characterize the region of interest (ROI) of a given leaf which enhances the accuracy of the segmentation using minimal time. 2) using linear combination of discrete Gaussians (LCDG) to represent the visual appearance of the input image and to assume the marginal probability distributions of the three regions of interest classes. 3) Using information generated from RGB images that we have stored in our data base to calculate the probabilities of the three classes on a pixel basis in step two. 4) Lastly, clarifying the labels with Gauss-Markov random field model (GGMRF) to maintain the continuity. After all these steps, the experimental validation promised high accuracy.

Analyzing cerebrovascular changes using Time-of-Flight Magnetic Resonance Angiography (ToF-MRA) images can detect the presence of serious diseases and track their progress, e.g., hypertension. Such analysis requires accurate segmentation of the vasculature from the surroundings, which motivated us to propose a fully automated cerebral vasculature segmentation approach based on extracting both prior and current appearance features that capture the appearance of macro and micro-vessels. The appearance prior is modeled with a novel translation and rotation invariant Markov-Gibbs Random Field (MGRF) of voxel intensities with pairwise interaction analytically identified from a set of training data sets, while the current appearance is represented with a marginal probability distribution of voxel intensities by using a Linear Combination of Discrete Gaussians (LCDG) whose parameters are estimated by a modified Expectation-Maximization (EM) algorithm. The proposed approach was validated on 190 data sets using three metrics, which revealed high accuracy compared to existing approaches.

The ultimate goal of this paper is to develop a novel personalized comprehensive computer aided diagnostic (CAD) system for precise diagnosis of autism spectrum disorder (ASD) based on the 3D shape analysis of the cerebral cortex (Cx), To achieve the main goal of the proposed system, we used structural MRI modality (sMRI) to be able to extract the shape features of the brain cortex. After segmenting the brain cortex from sMRI, we used a spherical harmonics analysis to measure the surface complexity, in addition to studying surface curvatures. Finally, a multi-stage deep network based on several autoencoders and softmax classifiers is constructed to provide the final global diagnosis. The presented CAD system was tested on several datasets, achieving an average accuracy of 92.15%. In addition to its global diagnostic accuracy, the local diagnostic accuracies of the most significant areas also demonstrated the ability of the proposed system to construct very promising local maps of ASD-related brain abnormalities, which can be considered an important step towards personalized medicine for autistic individuals.

Faces in the wild have recently captured the focus of researchers for all facial analysis problems. Partial occlusion is a major problem for analyzing faces captured in unconstrained non-cooperative conditions. Even detecting the faces in such conditions is a challenging problem that needs to be solved before any further analysis of such faces can be done. In this paper, we propose modelling the face as a collection of parts that can be selected from the visible regular facial features and some other objects that can possibly occlude faces such as sunglasses, caps and hands. The proposed algorithm is more toward scene understanding in the sense that it is not only detecting faces but it also suggests the visible parts of these faces and even some of the occluding objects which can help in any further analysis. Experimental results show a state of the art performance on the challenging FDDB database with a thorough analysis of the performance with different types of partial occlusion.

Recently, several approaches for pedestrian detection have been investigated using discriminatively trained part based models with which Histogram of Oriented Gradients (HOG) showed to be a robust feature. In this paper, we propose a new feature based on HOG to be used with the discriminatively trained part framework for pedestrian detection. Our method is based on computing the image mixed partial derivatives to be used to redefine the gradients of some pixels and to reweigh the vote at all pixels with respect to the original HOG. Our approach was tested on the PASCAL2007 and INRIA person dataset and showed to have an outstanding performance.

In this paper we propose an educational robotic system for teaching kids at nursery schools how to write and draw simple shapes. Our system uses the humanoid robot NAO to draw shapes appearing on a computer screen. The system uses light polarization property for fast detection of the screen despite of its content. We also propose a mapping from the image domain to the robot space.

Polarization imaging can give information about surface shape, and roughness. Polarization has been used for shape recovery, but with convex/concave reconstruction ambiguity. In this paper, we present a direct method to shape recovery using both polarization and shading that resolves this ambiguity, without the need for nonlinear optimization routines. Several experiments on synthetic and real datasets are reported to evaluate the proposed method. The method consistently outperforms some well-known methods based on polarization information alone.