Literature DB >> 27278579

Clinical application of a new method that segments the region of interest into multiple layers for RF amplitude histogram analysis in the cirrhotic liver.

Yasutomo Fujii1, Nobuyuki Taniguchi1, Yi Wang1, Kouichiro Shigeta1, Kiyoka Omoto1, Kouichi Itoh1, Jing-Wen Tsao2, Kenji Kumazaki2, Takashi Itoh2, Tomotsugu Takayama2.   

Abstract

PURPOSE: We used texture analysis in conjunction with an alternative method of analyzing the amplitude histogram using a radiofrequency (RF) signal to differentiate ultrasonograms of normal and cirrhotic livers. This method segments the region of interest (ROI) into multiple layers (sub-ROIs). In each sub-ROI of a homogeneous medium, the histogram of enveloped-amplitude of RF backscattered echoes resembles a Rayleigh distribution. Theoretically, the values of the signal-to-noise ratio (SNR), skewness, and kurtosis for Rayleigh statistics are constant and independent of the mean scattering intensity, which is contributed by such undesirable effects as tissue attenuation, beam diffraction, and incident waveforms. These values, which averaged overall sub-ROI, should provide an unbiased estimator.
METHODS: We studied 36 normal livers and 28 cirrhotic livers, all confirmed by clinical findings including laboratory and pathology data; the SNR, skewness, and kurtosis values of the disease groups were compared. At the same time, these values were estimated using the conventional method, which did not segment the ROI into multiple sub-ROIs. The unpaired t-test was used to determine statistical significance.
RESULTS: With the new method, all values obtained from cirrhotic livers differed significantly from those obtained from normal livers, and the standard deviation of these values was smaller than those obtained using the conventional method.
CONCLUSIONS: These results suggest that the new method can be used to diagnose the cirrhotic liver objectively.

Keywords:  histogram; liver cirrhosis; radiofrequency signal; tissue characterization; ultrasonography

Year:  2004        PMID: 27278579     DOI: 10.1007/s10396-004-0015-x

Source DB:  PubMed          Journal:  J Med Ultrason (2001)        ISSN: 1346-4523            Impact factor:   1.314


  7 in total

1.  Usefulness of textural analysis as a tool for noninvasive liver fibrosis staging.

Authors:  Cristian Vicas; Monica Lupsor; Radu Badea; Sergiu Nedevschi
Journal:  J Med Ultrason (2001)       Date:  2011-05-27       Impact factor: 1.314

2.  Trial of a quantitative method for evaluating hemangioma of the liver and hepatocellular carcinoma using a radio-frequency signal.

Authors:  Kazutoki Kogure
Journal:  J Med Ultrason (2001)       Date:  2005-12       Impact factor: 1.314

3.  Comparison of ultrasound colored image views produced by application of statistical analysis of radio-frequency signals and histological findings in patients with chronic hepatitis C.

Authors:  Sanae Nakajima; Kazutoshi Shibuya; Naohisa Kamiyama; Yasukiyo Sumino
Journal:  J Med Ultrason (2001)       Date:  2009-12-11       Impact factor: 1.314

4.  Quantitative processed images acquired by histogram-SNR imaging used to evaluate parenchymal heterogeneity in the liver.

Authors:  Yasutomo Fujii; Nobuyuki Taniguchi; Kouichi Itoh; Yi Wang; Kouichiro Shigeta; Tomoko Ono; Jing-Wen Tsao; Kenji Kumasaki; Takashi Itoh
Journal:  J Med Ultrason (2001)       Date:  2003-03       Impact factor: 1.314

5.  Proposal of a parametric imaging method for quantitative diagnosis of liver fibrosis.

Authors:  Tadashi Yamaguchi; Hiroyuki Hachiya
Journal:  J Med Ultrason (2001)       Date:  2010-07-13       Impact factor: 1.314

6.  Ultrasound Image Computerized Analysis for Non-invasive Quantitative Evaluation of Hepatic Fibrosis.

Authors:  Georgiana Nagy; Maria Adriana Neag; Mihaela Gordan; Doinita Crisan; Mircea Petru; Romeo Chira
Journal:  Turk J Gastroenterol       Date:  2021-10       Impact factor: 1.852

7.  Multiparametric ultrasomics of significant liver fibrosis: A machine learning-based analysis.

Authors:  Wei Li; Yang Huang; Bo-Wen Zhuang; Guang-Jian Liu; Hang-Tong Hu; Xin Li; Jin-Yu Liang; Zhu Wang; Xiao-Wen Huang; Chu-Qing Zhang; Si-Min Ruan; Xiao-Yan Xie; Ming Kuang; Ming-De Lu; Li-Da Chen; Wei Wang
Journal:  Eur Radiol       Date:  2018-09-03       Impact factor: 5.315

  7 in total

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