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Detection of Breast Cancer in Mammogram Images Using Texture Analysis Methods

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dc.contributor.author Omer, Ashgan Mohamed
dc.contributor.author Supervisor, - Alnazier O. Hamza
dc.date.accessioned 2020-03-12T09:12:56Z
dc.date.available 2020-03-12T09:12:56Z
dc.date.issued 2019-02-10
dc.identifier.citation Omer, Ashgan Mohamed . Detection of Breast Cancer in Mammogram Images Using Texture Analysis Methods / Ashgan Mohamed Omer ; Alnazier O. Hamza .- Khartoum: Sudan University of Science and Technology, college of Engineering, 2019 .- 199p. :ill. ;28cm .- M.Sc. en_US
dc.identifier.uri http://repository.sustech.edu/handle/123456789/24845
dc.description Thesis en_US
dc.description.abstract The early detection of breast cancer is of the outmost importance in order to increase survival rates. Mammography is considered as an effective tool to detect breast cancer, but due to the complexity of breast tissue texture and the observer variability effect, misdiagnosis of breast cancer frequently occurs. This research develops a simple Computer-aided detection (CAD) system in order to improve the accuracy and efficiency of mammogram interpretation. The first step of the proposed algorithm is mammographic image preprocessing which removes unwanted regions from the breast image and enhances image contrast without losing the image information. A region of interest (ROI) was then segmented using thresholding, and quantization based techniques. In mammography, the breast tissue type influences the performance of the detection. Thus, the proposed algorithm automatically characterizes the tissue type as fatty, dense or glandular. The last step of the proposed CAD system, aims at distinguishing normal from abnormal breast tissue, and subsequently differentiates benign from malignant lesions. In this thesis work, the classification tasks were based on texture features and supported vector machine (SVM). Features were extracted by using conventional well-known features (Haralick features) as well as new, less-known features (segmented fractal texture analysis SFTA). To obtain good classification performances, optimal features were selected and redundant features were removed. With the aim of showing the robustness of our approach, tests were performed using the well-known Mammographic Image Analysis Society (MIAS) database which contains annotations provided by radiologists. For tissue characterization, three approaches of features extractions were investigated; the best accuracy for distinguishing fatty tissue from non-fatty was obtained using combined Haralick and SFTA features (87% accuracy). Moreover, the best accuracy for differentiating between dense and glandular tissue was obtained using SFTA features (78 % accuracy). The SFTA features proved their superiority for the classification of normal vs abnormal lesion, and benign vs malignant lesion, with accuracies of 75.5% and 74.5% respectively. en_US
dc.description.sponsorship Sudan University of Science and Technology en_US
dc.language.iso en en_US
dc.publisher Sudan University of Science and Technology en_US
dc.subject Biomedical Engineering en_US
dc.subject Texture Analysis Methods en_US
dc.subject Detection of Breast Cancer en_US
dc.subject Mammogram Images en_US
dc.title Detection of Breast Cancer in Mammogram Images Using Texture Analysis Methods en_US
dc.title.alternative الكشف عن سرطان الثدي في صور الأشعة باستخدام التحليل النسجي en_US
dc.type Thesis en_US


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