Paper Info
Title
A computer aided for image processing of computed tomography in hepatocellular carcinoma
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 卤 2.167 for the original images and 6.260 卤 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.
Year
DOI
Venue
2011
10.1109/BIBMW.2011.6112513
BIBM Workshops
Keywords
Field
DocType
unenhanced information,computed tomography,image processing,reference information,clinical diagnosis,relevant information,ct image,enhanced image,clustering information,image fusion process,hepatocellular carcinoma,clinical practice,statistical analysis,k means clustering,cancer,k mean clustering,image fusion,image segmentation,cluster analysis,standard deviation,region of interest,contrast to noise ratio,stochastic resonance
Computer vision,k-means clustering,Data set,Image fusion,Computer science,Image processing,Image segmentation,Artificial intelligence,Region of interest,Cluster analysis,Contrast-to-noise ratio
Conference
ISSN
Citations 
PageRank 
2163-6966
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Wei-Tai Hsu100.68
Jia-Rong Yeh2685.99
Yi-Chung Chang321.13
Men-Tzung Lo48313.65
Yi-hsien Lin5566.13