الفهرس 
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Abstract
Breast cancer is one of the most common types of cancer and early detection can significantly decrease the associated mortality rate. Different kinds of segmentation methods were applied to extract regions of interest from breast cancer images that are necessary to improve the classification. In this thesis, a segmentation method for breast cancer from thermal images is introduced based on a proposed Chaotic Salp Swarm Algorithm (CSSA). Although the Salp Swarm Algorithm (SSA) shows superiority in singleobjective optimization problems, it suffers from a low convergence rate and local optima stagnation. In the proposed method, a segmentation algorithm is formulated using the quick-shift method for superpixels extraction whose parameters are optimized by Chaotic Salp Swarm Algorithm (CSSA). The quick-shift method generates compact and nearly uniform superpixels by clustering the breast thermal image pixels. Chaotic Salp Swarm Algorithm (CSSA) is developed based on ten chaotic maps to enhance the original Salp Swarm Algorithm (SSA) convergence rate while accuracy could be improved by controlling the balance between exploration and exploitation. The proposed algorithm is applied to real world thermal images for the breast area. The results demonstrate that the proposed Chaotic Salp Swarm Algorithm (CSSA) achieves fast convergence for the unimodal benchmark functions and outperforms the original (Salp Swarm Algorithm) SSA. Moreover, a Dataset from Mastology Research with Infrared Image (DMR-IR) is used to test the performance of the proposed algorithm. In experiments, the proposed optimized segmentation algorithm extracts the breast area from the background accurately where the region of interest is focused on the breast area and removes the unwanted area such as underarms and stomach which intern can enhance the results of cancer detection. Furthermore, the proposed algorithms achieve robustness for the segmentation of different healthy and unhealthy cases images compared to the state-of-the-art methods. The thesis contributions are: - • Chaotic Salp Swarm Algorithm (CSSA) is proposed to optimize the quickshift method parameters by enhancing the original Salp Swarm Algorithm (SSA) accuracy. • A breast cancer segmentation algorithm is proposed using the optimized quick-shift and Otsu methods. • The proposed segmentation algorithm is applied to thermal images for the problem of breast cancer. • In experiments, we used online database which called Dataset from Mastology Research with Infrared Image (DMR-IR) that scontains different healthy and unhealthy cases are used to show the performance of the proposed algorithms. • The proposed algorithms can be generalized to different applications in the biomedical imaging diagnoses such as thermal image.