Toward embedded detection of polyps in WCE images for early diagnosis of colorectal cancer

Abstract

Purpose: Wireless capsule endoscopy (WCE) is commonly used for noninvasive gastrointestinal tract evaluation, including the detection of mucosal polyps. A new embeddable method for polyp detection in wireless capsule endoscopic images was developed and tested.

Methods: First, possible polyps within the image were extracted using geometric shape features. Next, the candidate regions of interest were evaluated with a boosting based method using textural features. Each step was carefully chosen to accommodate hardware implementation constraints. The method's performance was evaluated on WCE datasets including 300 images with polyps and 1,200 images without polyps. Hardware implementation of the proposed approach was evaluated to quantitatively demonstrate the feasibility of such integration into the WCE itself.

Results: The boosting based polyp classification demonstrated a sensitivity of 91.0 %, a specificity of 95.2 % and a false detection rate of 4.8 %. This performance is close to that reported recently in systems developed for an online analysis of video colonoscopy images.

Conclusion: A new method for polyp detection in videoendoscopic WCE examinations was developed using boosting based approach. This method achieved good classification performance and can be implemented in situ with embedded hardware.