Automatic Segmentation and Textural Feature Analysis for B-mode Ultrasound Images as a Tool for Noninvasive Liver Fibrosis Identification and Categorization

• Main Authors: Lu, Nanhan, 呂南翰
• Other Authors: Kuo, Chungming
• Format: Others
• Language: en_US
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/82291543770828459903

博士 === 義守大學 === 資訊工程學系 === 101 === Chronic hepatitis is one of the most important health threats in Taiwan. Ultrasound is a widely used medical imaging technique with non-invasive safest modality. Although ultrasound is commonly used to diagnosis of various liver diseases, the accuracy of visual diagnosis is very low, and it also strongly depends on the experience of radiologist. Recently, the identification and categorization of ultrasound images have become very desirable due to the rapid development of computer technology. However, human interaction in the developed method is still inevitable. In this paper, we propose an effective technique to solve this problem. First, Otsu’s method segmentation and sampling approach are applied to sample the region of interest (ROI) from ultrasound images. Second, we propose a modification of LBP (local binary pattern) called FLBP (fuzzy local binary pattern) as texture descriptor, which is used to analyze textural features. Finally, the principle component of texture feature is introduced to classify the textures. The preliminary results indicate that the proposed methods can achieve satisfactory performance for both segmentation and categorization of liver fibrosis. In the future, the further investigation will be continued. Analysis of more ultrasound images will be performed in our experiment. Furthermore, we will also adjust the parameters to optimize the new method. We expect not only improve diagnostic accuracy of liver fibrosis but also increase the categorization accuracy.