Alkhafaji, Ali Fawzi Mohammed Ali
(2024)
Acltshe-Amts: A New Adaptive Brain Tumour Enhancement And Segmentation Approaches.
PhD thesis, Perpustakaan Hamzah Sendut.
Abstract
Brain tumor subregion segmentation from multimodal Magnetic Resonance (MR) images is of great interest for better tumor diagnosis. Multilevel thresholding is one of the prominent approaches used for brain image segmentation. Currently, when applying multilevel thresholding for brain tumor segmentation, two important problems must be carefully addressed. First, the MR brain images suffer from sensitivity to intensity inhomogeneity, poor contrast, and hidden details, which corrupt the original MR image during capturing. Second, the conventional multilevel thresholding approaches, including optimization-based thresholding approaches, have several main issues, such as manual adjustment of multilevel thresholds, dedicating single thresholding criteria as objective functions, leading to the bias of the thresholding towards a specific type of MR image, and the requirement to tune a large number of control parameters. In this thesis, a two-stage approach is proposed to address these issues. In the first stage, a new image enhancement approach called Adaptive Clip Limit Tile Size Histogram Equalization (ACLTSHE) is proposed to improve contrast, highlight the hidden details, and achieve homogenized intensity distribution of MR images. The ACLTSHE integrates Contrast-Limited Adaptive Histogram Equalization, Multi-Objective Whale Optimization Algorithm, Discrete Entropy (DE), Peak Signal-to-Noise Ratio (PSNR), and Structure Similarity Index (SSI) to improve the quality of MR images while preserving the original structure of the MR images. In the second stage, a new approach called Adaptive Multilevel Thresholding Segmentation (AMTS) is proposed for unsupervised brain tumor
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subregion segmentation from normal brain tissue. The AMTS approach segments and extracts the whole tumor, core tumor, and enhanced tumor regions from the brain MR images, integrating the Multi-Objective Grasshopper Optimization algorithm, Kapur Entropy, Cross-Entropy, and Localized active contour.
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