NDLI: A computer aided for image processing of computed tomography in hepatocellular carcinoma

Content Provider	IEEE Xplore Digital Library
Author	Wei-Tai Hsu Jia-Rong Yeh Yi-Chung Chang Men-Tzung Lo Yi-Hsien Lin
Copyright Year	2011
Description	Author affiliation: Research Center for Adaptive Data Analysis & Center for Dynamical Biomarkers and Translational Medicine, National Central University, Chungli, Taiwan (Wei-Tai Hsu; Jia-Rong Yeh; Yi-Chung Chang; Men-Tzung Lo) \|\| Division of Radiotherapy, Cheng Hsin General Hospital, Taipei, Taiwan (Yi-Hsien Lin)
Abstract	Low contrast to noise ratio (CNR) of unenhanced computed tomography (CT) is sometimes hard to visualize by the clinical practice. In order to assist the clinical diagnosis, a computer aided for unenhanced CT image processing is introduced in detection of hepatocellular carcinoma (HCC). This study utilized the stochastic resonance (SR) filter by adjusting localized threshold range with adding random noise for enhancing the region of interest (ROI). The quantitative measurement by using the measure of enhancement or measure of improvement (EME) is applied on the series of original and enhanced images. The value of mean and standard deviation of EME values is 2.652 ± 2.167 for the original images and 6.260 ± 1.206 for enhanced images. Then k-mean clustering method played the role based on the cluster analysis with the nearest mean for the local segmentation. The diagnostic check for determining the number of clusters on each enhanced images is important for getting a better result. In fact, K = 10 is more appropriate for the data sets of enhanced images. Finally, the image fusion process is involved two sets of data, enhanced and post-processed of enhanced and clustering information, to provide relevant information. Using the T = 0.45 as the threshold value applied on clustering and enhanced images eliminates the stronger intensity of pixels. Though those processes, the unenhanced information could be extracted out as the reference information for the clinical diagnosis. HCC was well isolated on processed images. Our results demonstrated the utilization of the computer aided for image processing of CT images might help to detect the HCC.
Starting Page	942
Ending Page	944
File Size	878056
Page Count	3
File Format	PDF
ISBN	9781457716126
e-ISBN	9781457716133
DOI	10.1109/BIBMW.2011.6112513
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-11-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Strontium computed tomography image processing k-mean clustering Computed tomography Stochastic resonance Noise image fusion stochastic resonance Image fusion Tumors Biomedical imaging
Content Type	Text
Resource Type	Article

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