Colonoscopy video quality assessment using hidden Markov random fields

Sun Young Park; Dusty Sargent; Inbar Spofford; Kirby Vosburgh

doi:10.1117/12.878217

9 March 2011 Colonoscopy video quality assessment using hidden Markov random fields

Sun Young Park, Dusty Sargent, Inbar Spofford, Kirby Vosburgh

Proceedings Volume 7963, Medical Imaging 2011: Computer-Aided Diagnosis; 79632P (2011) https://doi.org/10.1117/12.878217
Event: SPIE Medical Imaging, 2011, Lake Buena Vista (Orlando), Florida, United States

Abstract

With colonoscopy becoming a common procedure for individuals aged 50 or more who are at risk of developing colorectal cancer (CRC), colon video data is being accumulated at an ever increasing rate. However, the clinically valuable information contained in these videos is not being maximally exploited to improve patient care and accelerate the development of new screening methods. One of the well-known difficulties in colonoscopy video analysis is the abundance of frames with no diagnostic information. Approximately 40% - 50% of the frames in a colonoscopy video are contaminated by noise, acquisition errors, glare, blur, and uneven illumination. Therefore, filtering out low quality frames containing no diagnostic information can significantly improve the efficiency of colonoscopy video analysis. To address this challenge, we present a quality assessment algorithm to detect and remove low quality, uninformative frames. The goal of our algorithm is to discard low quality frames while retaining all diagnostically relevant information. Our algorithm is based on a hidden Markov model (HMM) in combination with two measures of data quality to filter out uninformative frames. Furthermore, we present a two-level framework based on an embedded hidden Markov model (EHHM) to incorporate the proposed quality assessment algorithm into a complete, automated diagnostic image analysis system for colonoscopy video.

Citation Download Citation

Sun Young Park, Dusty Sargent, Inbar Spofford, and Kirby Vosburgh "Colonoscopy video quality assessment using hidden Markov random fields", Proc. SPIE 7963, Medical Imaging 2011: Computer-Aided Diagnosis, 79632P (9 March 2011); https://doi.org/10.1117/12.878217

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available