N. Ghatwary and A. Ahmed and H. Jalab
Liver Tumor Detection by Classification through FD Enhancement of CT Image
2355 - 2358
2015
9
11
International Journal of Computer and Information Engineering
https://publications.waset.org/pdf/10003283
https://publications.waset.org/vol/107
World Academy of Science, Engineering and Technology
In this paper, an approach for the liver tumor detection
in computed tomography (CT) images is represented. The detection
process is based on classifying the features of target liver cell to
either tumor or nontumor. Fractional differential (FD) is applied for
enhancement of Liver CT images, with the aim of enhancing texture
and edge features. Later on, a fusion method is applied to merge
between the various enhanced images and produce a variety of
feature improvement, which will increase the accuracy of
classification. Each image is divided into NxN nonoverlapping
blocks, to extract the desired features. Support vector machines
(SVM) classifier is trained later on a supplied dataset different from
the tested one. Finally, the block cells are identified whether they are
classified as tumor or not. Our approach is validated on a group of
patients’ CT liver tumor datasets. The experiment results
demonstrated the efficiency of detection in the proposed technique.
Open Science Index 107, 2015