In clinical practice, histopathological analysis of biopsied tissue is the main method for bladder cancer diagnosis and prognosis. The diagnosis is performed by a pathologist based on the morphological features in the image of a hematoxylin and eosin (HE) stained tissue sample. This manuscript proposes algorithms to perform morphometric analysis on the HE images, quantify the features in the images, and discriminate bladder cancers with different grades, i.e. high grade and low grade. The nuclei are separated from the background and other types of cells such as red blood cells (RBCs) and immune cells using manual outlining, color deconvolution and image segmentation. A mask of nuclei is generated for each image for quantitative morphometric analysis. The features of the nuclei in the mask image including size, shape, orientation, and their spatial distributions are measured. To quantify local clustering and alignment of nuclei, we propose a 1-nearest-neighbor (1-NN) algorithm which measures nearest neighbor distance and nearest neighbor parallelism. The global distributions of the features are measured using statistics of the proposed parameters. A linear support vector machine (SVM) algorithm is used to classify the high grade and low grade bladder cancers. The results show using a particular group of nuclei such as large ones, and combining multiple parameters can achieve better discrimination. This study shows the proposed approach can potentially help expedite pathological diagnosis by triaging potentially suspicious biopsies.

Date Published: 26 February 2015
PDF: 11 pages
Proc. SPIE 9303, Photonic Therapeutics and Diagnostics XI, 930317 (26 February 2015);

Published in SPIE Proceedings Vol. 9303:
Photonic Therapeutics and Diagnostics XI
Hyun Wook Kang; Brian J. F. Wong M.D.; Melissa C. Skala; Bernard Choi; Guillermo J. Tearney M.D.; Andreas Mandelis; Nikiforos Kollias; Kenton W. Gregory M.D.; Mark W. Dewhirst D.V.M.; Justus F. Ilgner M.D.; Alfred Nuttal; Haishan Zeng; Laura Marcu; Claus-Peter Richter, Editor(s)