-
[show abstract]
[hide abstract]
ABSTRACT: Colonoscopy is the most popular screening tool for colorectal cancer. Recent studies reported that retroflexion during colonoscopy improved polyp yields. Retroflexion is an endoscope maneuver that enables visualization of internal mucosa along the shaft of the endoscope, enabling visualization of the mucosa area that is difficult to see with typical forward viewing. This paper describes our new method that detects endoscopic images showing retroflexion. This problem has not been investigated in the literature. We propose new region features that encapsulate important properties of endoscope appearance during retroflexion. Our experimental results on 25 colonoscopy videos show that trained Decision Tree classifiers can effectively identify retroflexion in the rectum at 92.0% accuracy and 94.4% precision.
Computer-Based Medical Systems (CBMS), 2011 24th International Symposium on; 07/2011
-
Proceedings of the 24th IEEE International Symposium on Computer-Based Medical Systems, 27-30 June, 2011, Bristol, United Kingdom; 01/2011
-
[show abstract]
[hide abstract]
ABSTRACT: Colonoscopy is an endoscopic technique that allows a physician to inspect the inside of the human colon. The appearance of the appendiceal orifice during colonoscopy indicates a complete traversal of the colon, which is an important quality indicator of the colon examination. In this paper, we present two new algorithms. The first algorithm determines whether an image shows the clearly seen appendiceal orifice. This algorithm uses our new local features based on geometric shape, illumination difference, and intensity changes along the norm direction (cross section) of an edge. The second algorithm determines whether the video is an appendix video (the video showing at least 3 s of the appendiceal orifice inspection). Such a video indicates good visualization of the appendiceal orifice. This algorithm utilizes frame intensity histograms to detect a near camera pause during the apendiceal orifice inspection. We tested our algorithms on 23 videos captured from two types of endoscopy procedures. The average sensitivity and specificity for the detection of appendiceal orifice images with the often seen crescent appendiceal orifice shape are 96.86% and 90.47%, respectively. The average accuracy for the detection of appendix videos is 91.30%.
IEEE Transactions on Biomedical Engineering 04/2010; · 2.28 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: Colonoscopy is an endoscopic technique that allows a physician to inspect the inside of the human colon. The appearance of the appendiceal orifice during colonoscopy indicates a complete traversal of the colon, which is one of important quality indicators of examination of the colon. In this paper, we propose a new algorithm that detects appendix images—images showing the appendiceal orifice. We introduce new features based on geometric shape, saturation and intensity changes along the norm direction (cross-section) of an edge to discriminate appendix images. Our experimental results on real colonoscopic images show the average sensitivity and specificity of 88.12% and 94.25%, respectively.
Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE; 09/2008
-
[show abstract]
[hide abstract]
ABSTRACT: Colonoscopy is an endoscopic technique that allows a physician to inspect the inside of the human colon. The appearance of the appendiceal orifice during colonoscopy indicates a complete traversal of the colon, which is one of important quality indicators of examination of the colon. In this paper, we propose a new algorithm that detects appendix images-images showing the appendiceal orifice. We introduce new features based on geometric shape, saturation and intensity changes along the norm direction (cross-section) of an edge to discriminate appendix images. Our experimental results on real colonoscopic images show the average sensitivity and specificity of 88.12% and 94.25%, respectively.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 02/2008; 2008:3000-3.
