Using texture analysis of ultrasound imaging to detect fibrotic and fatty livers
Date Issued
2011
Date
2011
Author(s)
Yang, Chun-Hsien
Abstract
Due to the advantages of its convenience, instantaneity, non-invasion and low cost than other systems such as MRI or CT, ultrasound system is widely applied to medical imaging diagnosis, and becomes the first choice for clinical diagnosis. Because there is hardly any effective and specific non-invasive way to diagnose the liver diseases except doing biopsy, this research will cut into the point by using B-mode and adopting texture analysis. There are two parts in this experiment: the first part is the in vitro rat experiment which divides the rats into three groups: induced fibrosis liver by medicine injection, induced fatty liver by medicine feeding and the normal liver as the control group. The second part is the cases of clinical experiments. We obtain the fibrosis cases by linear array and convex array transducer, adopting Metavir score from 0~4 as the assessment for the degree of severity after doing biopsy, and investigate the texture features along with the degree of severity.
This study uses gray level co-occurrence matrix (GLCM), which was proposed by Haralick in 1973, to analyze B-mode image and calculate the 11 texture features. GLCM is computed for various angular relationships and distances between neighboring pixel pairs on the image. Therefore we can know the probability of pixel pairs of gray level relationship as i and j in GLCM. 11 texture features was extracted from GLCM, including Energy, Contrast, Correlation, Entropy, Homogeneity, Variance, Sum Average, Sum Variance, Sum Entropy, Difference Variance and Difference Entropy.
In the in vitro rat experiment, we found that the trend of texture features along with the degree of severity maintained at a stable level in the normal control group, concluding that the normal liver has stationary texture features and could be considered as a control in comparison with diseased livers. Whereas the texture features of the fibrosis and fatty liver groups proceeded in a similar trend. Therefore we can separate the normal livers from fibrosis and fatty livers, but failing to separate fibrosis and fatty liver. In the clinical experiments, we found that the texture features have good performances to separate the non-fibrosis (Metavir score 0) and fibrosis (Metavir score 1 to 4), but they could hardly identify the fibrosis degrees.
Subjects
Ultrasound B-mode
Liver fibrosis
Fatty liver
Texture analysis
Type
thesis
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