Computer-assisted bladder cancer grading: α-shapes for color space decomposition

M. Khalid Khan Niazi; Anil V. Parwani M.D.; Metin N. Gurcan

doi:10.1117/12.2216967

23 March 2016 Computer-assisted bladder cancer grading: α-shapes for color space decomposition

M. Khalid Khan Niazi, Anil V. Parwani M.D., Metin N. Gurcan

Proceedings Volume 9791, Medical Imaging 2016: Digital Pathology; 979107 (2016) https://doi.org/10.1117/12.2216967
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

According to American Cancer Society, around 74,000 new cases of bladder cancer are expected during 2015 in the US. To facilitate the bladder cancer diagnosis, we present an automatic method to differentiate carcinoma in situ (CIS) from normal/reactive cases that will work on hematoxylin and eosin (H and E) stained images of bladder. The method automatically determines the color deconvolution matrix by utilizing the α-shapes of the color distribution in the RGB color space. Then, variations in the boundary of transitional epithelium are quantified, and sizes of nuclei in the transitional epithelium are measured. We also approximate the “nuclear to cytoplasmic ratio” by computing the ratio of the average shortest distance between transitional epithelium and nuclei to average nuclei size. Nuclei homogeneity is measured by computing the kurtosis of the nuclei size histogram. The results show that 30 out of 34 (88.2%) images were correctly classified by the proposed method, indicating that these novel features are viable markers to differentiate CIS from normal/reactive bladder.

Citation Download Citation

M. Khalid Khan Niazi, Anil V. Parwani M.D., and Metin N. Gurcan "Computer-assisted bladder cancer grading: α-shapes for color space decomposition", Proc. SPIE 9791, Medical Imaging 2016: Digital Pathology, 979107 (23 March 2016); https://doi.org/10.1117/12.2216967

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