Finding mucosal abnormalities (e.g., erythema, blood, ulcer, erosion, and polyp) is one of the most essential tasks during endoscopy video review. Since these abnormalities typically appear in a small number of frames (around 5% of the total frame number), automated detection of frames with an abnormality can save physician[U+05F3]s time significantly. In this paper, we propose a new multi-texture analysis method that effectively discerns images showing mucosal abnormalities from the ones without any abnormality since most abnormalities in endoscopy images have textures that are clearly distinguishable from normal textures using an advanced image texture analysis method. The method uses a "texton histogram" of an image block as features. The histogram captures the distribution of different "textons" representing various textures in an endoscopy image. The textons are representative response vectors of an application of a combination of Leung and Malik (LM) filter bank (i.e., a set of image filters) and a set of Local Binary Patterns on the image. Our experimental results indicate that the proposed method achieves 92% recall and 91.8% specificity on wireless capsule endoscopy (WCE) images and 91% recall and 90.8% specificity on colonoscopy images.
Bibliographical noteFunding Information:
This work is partially supported by NSF STTR -Grant no. 0740596 , 0956847 , National Institute of Diabetes and Digestive and Kidney Diseases ( NIDDK DK083745 ), and the Mayo Clinic . Any opinions, findings, conclusions, or recommendations expressed in this paper are those of authors. They do not necessarily reflect the views of the funding agencies. Johnny Wong, Wallapak Tavanapong, and JungHwan Oh hold positions at EndoMetric Corporation, Ames, IA 50014, USA, a for profit company that markets endoscopy-related software.
Wallapak Tavanapong received the B.S. degree in Computer Science from Thammasat University, Thailand, in 1992 and the M.S. and Ph.D. degrees in Computer Science from the University of Central Florida in 1995 and 1999, respectively. She joined Iowa State University in 1999 and is currently an Associate Professor and Associate Chair of Computer Science. She is a co-founder and a Chief Technology Officer of EndoMetric Corp., a software company that offers computer-aided technology for endoscopy procedures. She received a National Science Foundation Career grant, the 2006 American College of Gastroenterology Governors Award for Excellence in Clinical Research for “The Best Scientific Paper”, and a US patent on Colonoscopy Video Processing for Quality Metrics Determination. Her current research interests include content-based analysis on multimedia data for healthcare and social media, high performance multimedia computing, medical informatics, and databases. National Science Foundation, Agency for Healthcare Research and Quality, the National Institute of Diabetes and Digestive and Kidney Diseases, Mayo Clinic Rochester, Iowa Department of Economic Development, and EndoMetric Corp., supported her research.
Johnny Wong is Professor & Interim Chair of the Computer Science Department at Iowa State University (ISU). His research interests include Software Systems & Networking, Security & Privacy, and Medical Informatics. Most of his research projects are funded by government agencies and industries, including NSF, DoD, HHS, NIH, Mayo, etc. He is the President/CEO of a startup company EndoMetric Corporation, with software products for Medical Informatics. He is a co-director of the Smart Home Lab in the Department of Computer Science at ISU. He has served as a member of program committee of various international conferences on intelligent and software systems and networking. He was the Program co-Chair of the COMPSAC 2006 and General co-Chair of the COMPSAC 2008 conference, the IEEE Signature Conference on Computers, Software, and Applications. He is a member of the ACM and IEEE Computer Society. He has published over 150 papers in peer reviewed journals and conferences.
- Filter bank
- Local binary pattern
- Texton dictionary
- Wireless capsule endoscopy