Self Organization Map based Texture Feature Extraction for Efficient Medical Image Categorization
Texture is one of the most important properties of visual surface that helps in discriminating one object from another or an object from background. The self-organizing map (SOM) is an excellent tool in exploratory phase of data mining. It projects its input space on prototypes of a low-dimensional regular grid that can be effectively utilized to visualize and explore properties of the data. This paper proposes an enhancement extraction method for accurate extracting features for efficient image representation it based on SOM neural network. In this approach, we apply three different partitioning approaches as a region of interested (ROI) selection methods for extracting different accurate textural features from medical image as a primary step of our extraction method. Fisherfaces feature selection is used, for selecting discriminated features form extracted textural features. Experimental result showed the high accuracy of medical image categorization with our proposed extraction method. Experiments held on Mammographic Image Analysis Society (MIAS) dataset.
Code (0)
등록된 구현이 없습니다.
Tasks
feature selectionImage CategorizationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Analyzing Regional Organization of the Human Hippocampus in 3D-PLI Using Contrastive Learning and Geometric Unfolding
Understanding the cortical organization of the human brain requires interpretable descriptors for distinct structural and functional imaging data. 3D polarized light imaging (3D-PLI) is an imaging modality for visualizin…
Contrastive LearningHippocampusGood Deep Features to Track: Self-Supervised Feature Extraction and Tracking in Visual Odometry
Visual-based localization has made significant progress, yet its performance often drops in large-scale, outdoor, and long-term settings due to factors like lighting changes, dynamic scenes, and low-texture areas. These …
Self-Supervised LearningVisual OdometrySTPGANsFusion: Structure and Texture Preserving Generative Adversarial Networks for Multi-modal Medical Image Fusion
Medical images from various modalities carry diverse information. The features from these source images are combined into a single image, constituting more information content, beneficial for subsequent medical applicati…
DiagnosticAlignVTOFF: Texture-Spatial Feature Alignment for High-Fidelity Virtual Try-Off
Virtual Try-Off (VTOFF) is a challenging multimodal image generation task that aims to synthesize high-fidelity flat-lay garments under complex geometric deformation and rich high-frequency textures. Existing methods oft…
Image GenerationVirtual Try-OffTexture Based Image Segmentation of Chili Pepper X-Ray Images Using Gabor Filter
Texture segmentation is the process of partitioning an image into regions with different textures containing a similar group of pixels. Detecting the discontinuity of the filter's output and their statistical properties …
Image SegmentationSegmentationSemantic SegmentationTexture Classification