Liver fibrosis scoring using ultrasound parametric images and texture analysis
Date Issued
2009
Date
2009
Author(s)
Chang, Chia-Wei
Abstract
Liver fibrosis is a tardy and long-time process, and it has high probability to induce liver cirrhosis if we pay no attention on it. However, the development of medicine nowadays lacks an effective quantitative technique to diagnose fibrosis stages except biopsy, an invasive examination. Ultrasound is a non-invasive diagnose tool. It has the advantage of real-time and has became the front-line diagnose tool on detecting fibrosis stage. But the traditional ultrasound gray-scale image is a qualitative image. It cannot describe the characteristic of fibrosis clearly and always need the experienced doctor to judge. In fact, even experienced doctors cannot perceive the initial stage of fibrosis with traditional images. Consider the above reasons, so we decide to develop the quantitative ultrasonic image and use statistic parameters to describe fibrosis stages, and finally achieve the purpose of fibrosis diagnosis. The ultrasound transducer emits signals into tissues, and receives the random backscatter signals of echoes. We can obtain the hidden information of tissue by analyzing these random signals. To describe the characteristic of tissues, we introduce Nakagami statistic distribution, and find the Nakagami parameter – m can quantitate fibrosis stages. Besides, Nakagami images are more effective to distinguish fibrosis stages than traditional gray-scale images, too. In animal experiments, we injected DMN to rats to induce liver fibrosis, and calculated the value of m at each fibrosis stage, and compared m with the biopsy scores. The result shows that fibrosis stages are positive related with the value of m. Even some biopsy scores are the same of zero, the value of m still increases with the fibrosis become serious step by step. It means the sensitivity of m is higher than biopsy score. And the value of m conforms the need of clinical diagnose with detecting initial stage of fibrosis successfully. At the same time, we also introduce the texture analysis to describe fibrosis stages and calculate four types of texture parameters to compare with the value of m. They respectively are contrast, correlation, energy, homogeneity. We find that no matter trend or dynamic ranges, the description of texture parameters about fibrosis is less complete than the value of m. So we use the value of m to distinguish liver fibrosis stages is reasonable and adequate.
Subjects
ultrasound
liver fibrosis
backscatter signal
Nakagami statistic distribution
texture analysis
Type
thesis
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