Automated Breast Cancer Detection using Computer Aided Diagnosis
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Date
2018-05
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Addis Ababa University
Abstract
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.
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Keywords
Computer Aided Diagnosis, Breast Cancer Detection