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.