title: Ultrasonic liver tissues classification by fractal feature vector based on M-band wavelet transform.
abstract: This paper describes the feasibility of selecting fractal feature vector based on M-band wavelet transform to classify ultrasonic liver images-normal liver, cirrhosis, and hepatoma. The proposed feature extraction algorithm is based on the spatial-frequency decomposition and fractal geometry. Various classification algorithms based on respective texture measurements and filter banks are presented and tested. Classifications for the three sets of ultrasonic liver images reveal that the fractal feature vector based on M-band wavelet transform is trustworthy. A hierarchical classifier, which is based on the proposed feature extraction algorithm is at least 96.7% accurate in the distinction between normal and abnormal liver images and is at least 93.6% accurate in the distinction between cirrhosis and hepatoma liver images. Additionally, the criterion for feature selection is specified and employed for performance comparisons herein.
authors.affiliation: Department of Electrical Engineering, National Tsing Hua University, Hsinchu, Taiwan 300, ROC.
authors.forename: Wen-Li
authors.initials: WL
authors.lastname: Lee
authors.forename: Yung-Chang
authors.initials: YC
authors.lastname: Chen
authors.forename: Kai-Sheng
authors.initials: KS
authors.lastname: Hsieh
mesh_terms: D000465:Algorithms; D006528:Carcinoma, Hepatocellular / Q000000981:diagnostic imaging; D005240:Feasibility Studies; D005355:Fibrosis / Q000000981:diagnostic imaging; D017709:Fractals; 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; D012016:Reference Values; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D014463:Ultrasonography