Hierarchical classification of liver tumor from CT images based on difference-of-features (DOF)

Alahmer, Hussein and Ahmed, Amr (2016) Hierarchical classification of liver tumor from CT images based on difference-of-features (DOF). In: International Conference of Signal and Image Engineering, 29 June - 01 July 2016, London, U.K..

Hierarchical Classification of Liver Tumor from CT Images Based on Difference-of-features (DOF)
WCE2016_pp490-495.pdf - Whole Document

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


This manuscript presents an automated classification approach to classifying lesions into four categories of liver diseases, based on Computer Tomography (CT) images. The four diseases types are Cyst, Hemangioma, Hepatocellular carcinoma (HCC), and Metastasis.
The novelty of the proposed approach is attributed to utilising the difference of features (DOF) between the lesion area and the surrounding normal liver tissue. The DOF (texture and intensity) is used as the new feature vector that feeds the classifier. The classification system consists of two phases. The first phase differentiates between Benign and Malignant lesions, using a Support Vector Machine (SVM) classifier. The second phase further classifies the Benign into Hemangioma or Cyst and the Malignant into Metastasis or HCC, using a Naïve Bayes (NB) classifier. The experimental results show promising improvements to classify the liver lesion diseases. Furthermore, the proposed approach can overcome the problems of varying intensity ranges, textures between patients, demographics, and imaging devices and settings.

Keywords:CAD system, Difference of feature, Fuzzy-c- means, Lesion detection, Liver segmentation
Subjects:G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Science > School of Computer Science
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ID Code:24857
Deposited On:28 Oct 2016 15:05

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