title: Profiling the features of pre-segmented healthy liver CT scans: towards fast detection of liver lesions in emergency scenario.
abstract: Automating the detection of lesions in liver CT scans requires a high performance and robust solution. With CT-scan start to become the norm in emergency department, the need for a fast and efficient liver lesions detection method is arising. In this paper, we propose a fast and evolvable method to profile the features of pre-segmented healthy liver and use it to detect the presence of liver lesions in emergency scenario. Our preliminary experiment with the MICCAI 2007 grand challenge datasets shows promising results of a fast training time, ability to evolve the produced healthy liver profiles, and accurate detection of the liver lesions. Lastly, the future work directions are also presented.
authors.affiliation: School of Computer Sciences, Universiti Sains Malaysia, 11800 Minden, Pulau Pinang, Malaysia. fermi@cs.usm.my
authors.forename: Muhammad Fermi
authors.initials: MF
authors.lastname: Pasha
authors.forename: Kee Siew
authors.initials: KS
authors.lastname: Hong
authors.forename: Mandava
authors.initials: M
authors.lastname: Rajeswari
mesh_terms: D000465:Algorithms; D003422:Critical Care / Q000379:methods*; D006801:Humans; D008113:Liver Neoplasms / Q000000981:diagnostic imaging*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D012016:Reference Values; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013382:Subtraction Technique; D014057:Tomography, X-Ray Computed / Q000379:methods*