TY - GEN
T1 - A classification-enhanced vote accumulation scheme for detecting colonic polyps
AU - Tajbakhsh, Nima
AU - Gurudu, Suryakanth R.
AU - Liang, Jianming
PY - 2013
Y1 - 2013
N2 - Colorectal cancer most often begins as abnormal growth of the colon wall, commonly referred to as polyps. It has been shown that the timely removal of polyps with optical colonoscopy (OC) significantly reduces the incidence and mortality of colorectal cancer. However, a significant number of polyps are missed during OC in clinical practice - the pooled miss-rate for all polyps is 22% (95% CI, 19%-26%). Computer-aided detection may offer promises of reducing polyp miss-rate. This paper proposes a new automatic polyp detection method. Given a colonoscopy image, the main idea is to identify the edge pixels that lie on the boundary of polyps and then determine the location of a polyp from the identified edges. To do so, we first use the Canny edge detector to form a crude set of edge pixels, and then apply a set of boundary classifiers to remove a large portion of irrelevant edges. The polyp locations are then determined by a novel vote accumulation scheme that operates on the positively classified edge pixels. We evaluate our method on 300 images from a publicly available database and obtain results superior to the state-of-the-art performance.
AB - Colorectal cancer most often begins as abnormal growth of the colon wall, commonly referred to as polyps. It has been shown that the timely removal of polyps with optical colonoscopy (OC) significantly reduces the incidence and mortality of colorectal cancer. However, a significant number of polyps are missed during OC in clinical practice - the pooled miss-rate for all polyps is 22% (95% CI, 19%-26%). Computer-aided detection may offer promises of reducing polyp miss-rate. This paper proposes a new automatic polyp detection method. Given a colonoscopy image, the main idea is to identify the edge pixels that lie on the boundary of polyps and then determine the location of a polyp from the identified edges. To do so, we first use the Canny edge detector to form a crude set of edge pixels, and then apply a set of boundary classifiers to remove a large portion of irrelevant edges. The polyp locations are then determined by a novel vote accumulation scheme that operates on the positively classified edge pixels. We evaluate our method on 300 images from a publicly available database and obtain results superior to the state-of-the-art performance.
KW - Optical colonoscopy
KW - boundary classification
KW - polyp detection
KW - random forest
KW - voting scheme
UR - http://www.scopus.com/inward/record.url?scp=84886578037&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84886578037&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-41083-3_7
DO - 10.1007/978-3-642-41083-3_7
M3 - Conference contribution
AN - SCOPUS:84886578037
SN - 9783642410826
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 53
EP - 62
BT - Abdominal Imaging
T2 - 5th International Workshop on Abdominal Imaging: Computation and Clinical Applications, Held in Conjunction with 16th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013
Y2 - 22 September 2013 through 22 September 2013
ER -