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3 Author(s)
Colleen A. Lingley-papadopoulos ; Dept. of Electr. & Comput. Eng., George Washington Univ., DC ; Murray H. Loew ; Jason M. Zara

The lining of the bladder is comprised of well defined layers that are clearly visible in optical coherence tomography (OCT) images of healthy bladder tissue. These layers are disturbed when cancerous cells are present. Consequently, recognition and classification of the layers of the bladder is very important in recognizing and staging bladder cancer. We present an algorithm that uses texture analysis, the k-means clustering algorithm, and edge detection software to segment OCT images of the lining of the bladder. We visually segmented 101 OCT images of bladder tissue, ran our algorithm on the images, and compared the results. The segmentation matched 59% of the time, matched reasonably well, with clear sources of error 29% of the time, and was incorrect 12% of the time. The combination of this algorithm with future texture analysis of the segmented layers will provide the tools required to perform a real-time optical biopsy using OCT

Published in:

2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro

Date of Conference:

12-15 April 2007