Rangayyan, Rangaraj M.Yilmaz, SerdarSeminowich, Sansira Lyne2017-12-182017-12-182012Seminowich, S. L. (2012). Digital image processing techniques for analysis of images of renal biopsy samples (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/4935http://hdl.handle.net/1880/105936Bibliography: p. 103-113A few pages are in colour.Diagnosis and monitoring of kidney transplant allografts is supported by microscopic analysis of renal biopsy samples. Visual analysis by pathologists allows for incon­sistencies, bias, and inaccuracies; image analysis via digital processing can address these concerns, reduce effort, and potentially provide a second opinion. In this thesis, digital image analysis methods for automatic segmentation of structures in images of renal biopsy samples are presented. 1\/Iethods for accurate segmentation of the effective biopsy area include opening by reconstruction, morphological closing, and erosion. The re ults were compared to contours drawn by an experienced pathologist; the mean distance to the closest point was 5.46 ± 3.92 µm (6 ± 4.31 pixels) and the true-positive fraction was 98.25 ± 1.77%. Methods for automatic segmentation of cell nuclei are also presented, including, automatic thresholding, adaptive threshold­ing, and morphological granulometry. The results were verified against pathologist annotations; true-positive ratios were in the range of 0.80 to 0.93.xv, 113 leaves : ill. ; 30 cm.engUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.master thesis10.11575/PRISM/4935