Computer-aided diagnosis for classification of focal liver lesions on contrast-enhanced ultrasonography: feature extraction and characterization of vascularity patterns

Junji Shiraishi; Katsutoshi Sugimoto; Naohisa Kamiyama; Fuminori Moriyasu; Kunio Doi

doi:10.1117/12.770361

17 March 2008 Computer-aided diagnosis for classification of focal liver lesions on contrast-enhanced ultrasonography: feature extraction and characterization of vascularity patterns

Junji Shiraishi, Katsutoshi Sugimoto, Naohisa Kamiyama, Fuminori Moriyasu, Kunio Doi

Author Affiliations +

Proceedings Volume 6915, Medical Imaging 2008: Computer-Aided Diagnosis; 691532 (2008) https://doi.org/10.1117/12.770361
Event: Medical Imaging, 2008, San Diego, California, United States

Abstract

We have developed a computer-aided diagnostic (CAD) scheme for classifying focal liver lesions (FLLs) into hepatocellular carcinoma (HCC), liver metastasis, and hemangioma, by use of B-mode and micro flow imaging (MFI) of contrast-enhanced ultrasonography. We used 98 cases in this study, in which 104 FLLs consisted of 68 HCCs, 21 metastases, and 15 hemangiomas. MFI was obtained with contrast-enhanced low-mechanical-index (MI) pulse subtraction imaging at a fixed plane which included a distinctive cross section of the FLL. In the MFI, the inflow high signals in the plane, which were due to the vascular patterns and the contrast agent, were accumulated following flash scanning with a high-MI ultrasound exposure. In this study, in addition to the existing 29 image features extracted from MFI images, such as replenishment time, the average and the standard deviation of pixel values in a FLL, and the average thickness of vessel-like patterns, four types of image features were extracted from MFI, temporal subtraction and B-mode images based on small square regions of interest (ROIs: 4x4 matrix size) placed to cover a whole region of the FLL. The four features were 1) uniformity of average pixel values for all ROIs, 2) peak pixel values in a histogram of average pixel values of ROIs, 3) fraction of hypoechoic regions within an FLL, and 4) cross-correlation of pixel values within an FLL between B-mode and MFI images. Overall classification accuracies performed by this CAD scheme were 87.5% for all 104 liver lesions.

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Junji Shiraishi, Katsutoshi Sugimoto, Naohisa Kamiyama, Fuminori Moriyasu, and Kunio Doi "Computer-aided diagnosis for classification of focal liver lesions on contrast-enhanced ultrasonography: feature extraction and characterization of vascularity patterns", Proc. SPIE 6915, Medical Imaging 2008: Computer-Aided Diagnosis, 691532 (17 March 2008); https://doi.org/10.1117/12.770361

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KEYWORDS

Liver

Image enhancement

Image segmentation

Ultrasonography

Computer aided diagnosis and therapy

Feature extraction

Aluminium phosphide

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