Diagnosis of Liver Tumor from CT Images Using First Order Statistical Features
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2015 by IJETT Journal|
|Year of Publication : 2015|
|Authors : Dr. Alyaa H. Ali , Entethar M. Hadi
|DOI : 10.14445/22315381/IJETT-V20P228|
Dr. Alyaa H. Ali , Entethar M. Hadi "Diagnosis of Liver Tumor from CT Images Using First Order Statistical Features", International Journal of Engineering Trends and Technology (IJETT), V20(3),155-158 Feb 2015. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group
The detection and diagnose of liver tumors from CT images by using digital image processing, is a modern technique depends on using computer in addition to textural analysis to obtain an accurate liver diagnosis, despite the method`s difficulty that came from liver`s position in the abdomen among the other organs. This method will make the surgeon able to detect the tumor and then easing treatment also it helps physicians and radiologists to identify the affected parts of the liver in order to protect the normal parts as much as possible from exposure to radiation. This study describes a new 2D liver segmentation method for purpose of transplantation surgery as a treatment for liver tumors. Liver segmentation is not only the key process for volume computation but also fundamental for further processing to get more anatomy information for individual patient. Due to the low contrast, blurred edges, large variability in shape and complex context with clutter features surrounding the liver that characterize the CT liver images. In this paper, the CT images are taken, and then the 2D segmentation process which is based on the hybrid method which is the combination of modified k-Mean (which depend on the distance and color), the statistical structure which is the first order statistical feature are applied to the liver image which will find, extract the CT liver boundary and further classify liver diseases.
 Upadhyay, Y. and Wasson, V. 2014. "Analysis of Liver MR Images for Cancer Detection using Genetic Algorithm". International Journal of Engineering Research and General Science. Vol.2, No.4, PP: 730-737.
 Kumar, P. Bhalerao, S. 2014. "Detection of Tumor in Liver Using Image Segmentation and Registration Technique". IOSR Journal of Electronics and Communication Engineering (IOSR-JECE). Vo.9, No.2, PP: 110-115.
 Selle, D.; Spindler, W.; Preim, B. and Peitgen, H. O. 2000. "Mathematical Methods in Medical Imaging: Analysis of Vascular Structures for Liver Surgery Planning". PP: 1-21.
 Zimmer, C. and Olivo-Marin, J. C. 2005. "Coupled Parametric Active Contours". Transactions on pattern Analysis and Machine Intelligence. Vol.27, No.11, PP: 1838-1841.
 Chitra, S. and Balakrishnan, G. 2012. "Comparative Study for Two Color Spaces HSCbCr and YCbCr in Skin Color Detection". Applied Mathematical Sciences. Vol.6, No.85, PP: 4229 – 4238.
Computed Tomography (CT), Modified K-mean, Irregularity.