TY - GEN
T1 - A curvelet-based lacunarity approach for ulcer detection from Wireless Capsule Endoscopy images
AU - Eid, Alexis
AU - Charisis, Vasileios S.
AU - Hadjileontiadis, Leontios J.
AU - Sergiadis, George D.
PY - 2013
Y1 - 2013
N2 - Wireless Capsule Endoscopy (WCE) is a fairly new technology that offers a low-risk, non invasive visual inspection of the patient's digestive tract, especially the small bowel, that was previously unreachable using the traditional endoscopic methods. However, the large amount of images produced by WCE requires a highly trained physician to manually inspect them; a procedure that is time consuming and prone to human error. This was the rationale to propose a novel strategy for automatic detection of WCE images related to ulcer, one of the most common findings of the digestive tract. This paper introduces a new texture extraction method based on the Discrete Curvelet Transform (DCT), a recent multi-resolution analysis tool. Textural information is acquired by calculating the lacunarity index of DCT subbands of the WCE images. The classification step is performed by a Support Vector Machine (SVM), demonstrating promising classification accuracy (86.5%) and pointing towards further research in this field.
AB - Wireless Capsule Endoscopy (WCE) is a fairly new technology that offers a low-risk, non invasive visual inspection of the patient's digestive tract, especially the small bowel, that was previously unreachable using the traditional endoscopic methods. However, the large amount of images produced by WCE requires a highly trained physician to manually inspect them; a procedure that is time consuming and prone to human error. This was the rationale to propose a novel strategy for automatic detection of WCE images related to ulcer, one of the most common findings of the digestive tract. This paper introduces a new texture extraction method based on the Discrete Curvelet Transform (DCT), a recent multi-resolution analysis tool. Textural information is acquired by calculating the lacunarity index of DCT subbands of the WCE images. The classification step is performed by a Support Vector Machine (SVM), demonstrating promising classification accuracy (86.5%) and pointing towards further research in this field.
UR - http://www.scopus.com/inward/record.url?scp=84889010212&partnerID=8YFLogxK
U2 - 10.1109/CBMS.2013.6627801
DO - 10.1109/CBMS.2013.6627801
M3 - Conference contribution
AN - SCOPUS:84889010212
SN - 9781479910533
T3 - Proceedings of CBMS 2013 - 26th IEEE International Symposium on Computer-Based Medical Systems
SP - 273
EP - 278
BT - Proceedings of CBMS 2013 - 26th IEEE International Symposium on Computer-Based Medical Systems
T2 - 26th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2013
Y2 - 20 June 2013 through 22 June 2013
ER -