Biomedical Engineering

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Browsing Biomedical Engineering by Subject "Breast Tumor"

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    S-transform Based Detection and Classification of Breast Cancer in Ultrasound Images
    (Addis Ababa University, 2019-09) Mekdes, Seyoum; Dawit, Assefa (PhD); Mengistu, Kifle (PhD)
    Breast cancer is the second leading cause of death for women all over the world. Since the cause of breast cancer remains unknown, early detection and diagnosis is the key for control. In that regard, Breast Ultrasound Imaging (BUS) has become important modality for breast cancer detection due to its noninvasive, cost effective nature and suitability for screening and diagnosing in low resource settings. Ultrasound imaging is one of the most frequently used diagnosis tools to detect and classify abnormalities in breast. A known drawback of the technology is that it has high amount of speckle noise which results in poor image quality. This makes it difficult to use the imaging technology for accurate detection of malignant tumors. The procedure is traditionally carried out by visual assessment of the images which is often a time taking process prone to observer variability issues. In this regard, computer aided detection techniques have been developed in various literatures showing promises with their merits and demerits. Nevertheless, image based accurate detection of breast cancer is still a topic of interest with many ongoing researches in the area. In the current work, S-transform based breast cancer detection and classification method is developed. The proposed system consists of four stages: preprocessing, segmentation, feature extraction and classification. Image enhancement and speckle noise reduction were implemented during preprocessing. Region of interest (ROI) is then accurately determined on preprocessed images by employing canny edge detection. The ultrasound images were then classified based on different features like mean, variance, standard deviation (STD), entropy and contrast metrics. The results of the classification stage were compared against available ground truth images acquired from research image database. Accordingly, the classification procedure implemented using artificial neural network offered 90% detection accuracy.