Three-dimensional brain tumor MRI segmentation is a challenging task in the field of medical image analysis. Recently, deep learning methods, particularly CNN-based architectures, have significantly improved segmentation by learning complex spatial features. However, challenges such as preserving fine details, texture variation, handling class imbalance, and ensuring generalization persist. Enhancing deep learning models with domain-specific knowledge, such as texture-aware filters, can further improve segmentation accuracy and robustness. This paper presents a novel model that incorporates Gabor convolution into a U-Net architecture to enhance texture analysis and minimize feature loss. The model processes 3D brain tumor MRIs slice by slice, utilizing multi-view inputs to preserve spatial details while maintaining a lightweight design. Furthermore, it investigates the optimal kernel sizes for the Gabor filter, marking the first study to address this crucial aspect of integrating textural analysis with deep learning techniques. Experimental results show that the proposed framework improved segmentation accuracy by using a 7×7 Gabor kernel size and achieving Dice coefficients of 89.78% for WT, 85.60% for TC, and 83.55% for ET, with a mean Dice score of 86.31%. The proposed model demonstrated consistent improvements over the standard U-Net and outperformed several existing state-of-the-art methods.

Melanoma is the most lethal form of skin cancer, but early detection can lead to effective treatment. Subsequently, the main concern of the health management community is to create efficient systems to detect melanoma earlier by utilizing computer vision systems since the traditional screening methods are manual, time-consuming, and inaccurate in some cases. These systems use measurable visual components describing the shape, color, and texture. These features are extracted based on rules invented by dermatologists to determine the malignancy of skin lesions. In this paper, we propose a novel approach to melanoma detection based on the “ugly duckling” concept, which suggests that nevi in the same individual usually resemble each other, and malignant melanomas often do not follow this pattern. Our method uses a convolutional neural network architecture to extract feature vectors from dermatoscopic images of skin lesions. Then, out-liers are detected by applying principal component analysis. The outliers are indicative of potential melanoma lesions. We evaluate the performance of our method using a dataset of dermatoscopic images. Our proposed method has shown the potential to improve melanoma detection rates.

To develop an efficient device that helps dermatologists to early evaluate and inspect a specific kind of skin disease, computer vision systems have been intensively studied. These systems replace the traditional screening ways which are manual and time-consuming. These systems use some measurable visual component describing the shape, color, and texture of skin diseases to recognize them and to specify their malignancy. This article will be concentrated on the importance of using some statistical features and extracting the most relevant features of texture-colored images by calculating their degree of characterization. Using these highly-rated static textural features, non-fatal skin disease and melanoma classification results are presented and discussed.

The document image segmentation is an indispensable task in the document layout analysis system. This paper presents an accurate segmentation approach based on fuzzy classification for the administrative document image. The texture-based analysis works for this kind of document image are rare. And the research works on specific tasks are limited. Moreover, the texture-based segmentation methods are desired because they do not rely strongly on a priori knowledge surrounding the document. In addition, the robustness of these methods for degraded documents has been proven. For these purposes, the texture is explored in the analysis for our image type, using a fuzzy classification. The Fisher score determinate the most discriminative texture features for our segmentation: mean and variance. Our approach achieves encouraging and promising results for the detection of document zones: text, image and background. Qualitative and quantitative experiments are presented to determinate our approach performance.