title: Automatic Extraction of Appendix from Ultrasonography with Self-Organizing Map and Shape-Brightness Pattern Learning.
abstract: Accurate diagnosis of acute appendicitis is a difficult problem in practice especially when the patient is too young or women in pregnancy. In this paper, we propose a fully automatic appendix extractor from ultrasonography by applying a series of image processing algorithms and an unsupervised neural learning algorithm, self-organizing map. From the suggestions of clinical practitioners, we define four shape patterns of appendix and self-organizing map learns those patterns in pixel clustering phase. In the experiment designed to test the performance for those four frequently found shape patterns, our method is successful in 3 types (1 failure out of 45 cases) but leaves a question for one shape pattern (80% correct).
authors.affiliation: Department of Computer Engineering, Silla University, Busan 46958, Republic of Korea.
authors.forename: Kwang Baek
authors.initials: KB
authors.lastname: Kim
authors.affiliation: Department of Computer Games, Yong-in Songdam College, Yongin 17145, Republic of Korea.
authors.forename: Doo Heon
authors.initials: DH
authors.lastname: Song
authors.affiliation: Department of Computer Engineering, Pusan National University, Busan 46241, Republic of Korea.
authors.forename: Hyun Jun
authors.initials: HJ
authors.lastname: Park
mesh_terms: D000465:Algorithms; D001064:Appendicitis / Q000175:diagnosis* / Q000000981:diagnostic imaging* / Q000503:physiopathology; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D011247:Pregnancy; D014463:Ultrasonography / Q000379:methods*