Center for Biomedical Engineering

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Browsing Center for Biomedical Engineering by Subject "Breast Cancer Detection"

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    Automated Breast Cancer Detection using Computer Aided Diagnosis
    (Addis Ababa University, 2018-05) Bruhtesfa, Mouhabaw; Dawit, Assefa (PhD)
    Breast cancer is the most prevalent invasive cancer in women and stands second for chief cause of cancer deaths in women, next to lung cancer. The occurrence rate is exceeding in the developing countries though the rate of mortality has decreased which can be credited to the advances in diagnosis and treatment. Initial diagnosis involves histological observation (microscopic observation of cells/tissues) of affected breast tissues for structural changes, irregularities in cell shapes, distribution of cells in the tissue and determining the grade of the cancer. As manual interpretation of the tissues is often labor intensive, expensive and prone to errors and inconsistency, computer-based analysis of microscopic histopathology images is used as an alternative to provide a more accurate, automatic, fast and reproducible procedure to assess breast cancers. One important aspect in this regard is the automatic segmentation of breast cancers and several approaches are available in the literature for use in executing such tasks. However, the segmentation of major pathological structures and their subsequent follow– ups are not easy because of various artifacts such as presence of anatomical structures with highly correlated pixels with that of lesion, illumination variability, noise and magnification of the microscope. This thesis attempts to present a new mathematical scheme for analysis of color breast histopathology images acquired through digital microscopy or whole slide imaging. The proposed scheme uses a holistic representation of the color images in the three (trinion) space and applies trinion based Fourier transforms to extract useful imaging features for the purpose of classification and segmentation of histopathology images. A suitable color space transformation and a way of extracting robust higher order features are included in the method. The scheme has been applied in analyzing images acquired from standard histopathology image databases and results have shown that the algorithm achieved commendable results with 91% sensitivity, 92.7% specificity, and 92% overall accuracy.