We present a computer-aided detection algorithm for polyps in optical colonoscopy images. Polyps are the precursors to colon cancer. In the US alone, more than 14 million optical colonoscopies are performed every year, mostly to screen for polyps. Optical colonoscopy has been shown to have an approximately 25% polyp miss rate due to the convoluted folds and bends present in the colon. In this work, we present an automatic detection algorithm to detect these polyps in the optical colonoscopy images. We use a machine learning algorithm to infer a depth map for a given optical colonoscopy image and then use a detailed pre-built polyp profile to detect and delineate the boundaries of polyps in this given image. We have achieved the best recall of 84.0% and the best specificity value of 83.4%.
@article{arxiv.1609.01329,
title = {Depth Reconstruction and Computer-Aided Polyp Detection in Optical Colonoscopy Video Frames},
author = {Saad Nadeem and Arie Kaufman},
journal= {arXiv preprint arXiv:1609.01329},
year = {2016}
}
Comments
**The title has been modified to highlight the contributions more clearly. The original title is: "Computer-Aided Detection of Polyps in Optical Colonoscopy Images". Keywords: Machine learning, computer-aided detection, segmentation, endoscopy, colonoscopy, videos, polyp, detection, medical imaging, depth maps, 3D, reconstruction, computed tomography, virtual colonoscopy, colorectal cancer, SPIE Medical Imaging, 2016