Liver CT enhancement using Fractional Differentiation and Integration

Ghatwary, Noha, Ahmed, Amr and Jalab, Hamid (2016) Liver CT enhancement using Fractional Differentiation and Integration. In: World Congress on Engineering 2016 (WCE 2016), 29 June - 1 July 2016, London.

Full content URL:

WCE2016_pp426-431.pdf - Whole Document

Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive


In this paper, a digital image filter is proposed to enhance the Liver CT image for improving the classification of tumors area in an infected Liver. The enhancement process is based on improving the main features within the image by utilizing the Fractional Differential and Integral in the wavelet sub-bands of an image. After enhancement, different features were extracted such as GLCM, GRLM, and LBP, among others. Then, the areas/cells are classified into tumor or non-tumor, using different models of classifiers to compare our proposed model with the original image and various established filters. Each image is divided into 15x15 non-overlapping blocks, to extract the desired features. The SVM, Random Forest, J48 and Simple Cart were trained on a supplied dataset, different from the test dataset. 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 enhancement in the proposed technique.

Keywords:image enhancement, Medical Image Processing., Medical Image Enhancement, Liver CT, Fractional Differential, Fractional Integration, JCOpen
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science > School of Computer Science
ID Code:23757
Deposited On:19 Aug 2016 08:47

Repository Staff Only: item control page