Shape Recognition Using Multiscale Morphological Image Processing: A Computational Approach to Analysis and Pattern Detection

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This book focuses on advanced methods for hemorrhagic stroke detection and segmentation using multiscale morphological image processing integrated with deep learning techniques. It addresses key challenges in medical imaging such as low contrast, complex anatomical structures, and limited labeled data. The proposed framework utilizes MRI modalities (T1, T2, and FLAIR) and introduces novel models including a Multiscale-Morphological Neural Network (MMNN) for accurate shape recognition and a Multiresolution Morphological U-Net (MMU-Net) for precise lesion segmentation. The approach combines preprocessing, contour discrimination, and morphological operations to enhance feature extraction and classification. Comparative analysis with existing techniques such as CNN, ResNet, and U-Net demonstrates improved performance in terms of accuracy, precision, sensitivity, and specificity. The book provides a comprehensive resource for researchers and practitioners in biomedical imaging, artificial intelligence, and healthcare technology, emphasizing practical applications in clinical diagnostics and decision support systems.

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Pagina's: 148, Paperback, LAP LAMBERT Academic Publishing