title: A knowledge-based technique for liver segmentation in CT data.
abstract: Liver cancer is one of the major death factors in the world. Transplantation and tumor removal are two main therapies in common clinical practice. Both tasks need image assisted planning and quantitative evaluations. Automatic liver segmentation is required for corresponding quantitative evaluations. Conventional approaches in liver segmentation consist of finding the initial liver border followed by tuning the border to the final mask. Finding the liver initial border is of great importance as the latter step largely depends on the initial step. In the previous works, the liver initial border was determined by applying thresholding and morphological filters. In order to estimate the liver initial boundary, we have proposed a technique based on anatomical knowledge of liver, its surrounding tissues as well as the approach that a clinician follows in screening liver in a CT dataset. Based on the above reasoning, we developed a multi-step heuristic technique to segment liver from other tissues in multi-slice CT images. The proposed technique can deal with various shapes, locations, and liver sizes. The method was evaluated in the presence of 50 actual liver data sets and the results were encouraging.
authors.affiliation: Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran. aforuzan@ece.ut.ac.ir
authors.forename: Amir H
authors.initials: AH
authors.lastname: Foruzan
authors.forename: Reza A
authors.initials: RA
authors.lastname: Zoroofi
authors.forename: Masatoshi
authors.initials: M
authors.lastname: Hori
authors.forename: Yoshinobu
authors.initials: Y
authors.lastname: Sato
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008099:Liver / Q000000981:diagnostic imaging*; D008113:Liver Neoplasms / Q000000981:diagnostic imaging*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*