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Computed Tomographic (CT) imaging is extensively implemented for liver tumor visualization and detection.
Computer aided image processing algorithms can provide aid to the physicians and radiologists in detecting
deadly diseases of liver specifically cancerous liver tumors. This paper presents a novel image processing
technique to automatically classify liver for its abnormality without going through the liver segmentation stage.
The study is conducted on a dataset of 39 samples of abdominal CT images. The CT dataset comprises of images
bearing unhealthy liver. The unhealthy liver is further divided into livers bearing malignant tumors namely
hepatoma and benign tumors namely hemangiomas. The methodology adopted for the study comprises of
feature extraction from original CT images of all types of liver with special focus on textural information. The
extracted features undergo the process of classification for malignancy and benignancy of the liver tumor. The
classifiers used for textural feature analysis include Support Vector Machines (SVM), K-Nearest Neighbor
(KNN) and ensemble classifier. Amongst these classifiers SVM yields a classification accuracy of 100% as
compare to KNN and ensemble classifier which give the classification accuracy of 94.7% and 52.6%
respectively. The proposed method of classification applied on entire abdominal CT scans without segmentation
is performed by using the feature extraction matrix of structural similarity index (SSIM), which gives an
improved classification accuracy of 100% as compared to the traditional GLCM matrix. The methodology can
be tested to classify the liver for malignancy using other non-invasive techniques of ultrasounds and Magnetic
Resonance Imaging (MRI) as well.
Ayesha Amir Siddiqi, Attaullah Khawaja, Adnan Hashmi. (2020) Classification of Abdominal CT Images bearing Liver Tumor Using Structural Similarity Index and Support Vector Machine , Mehran University Research Journal of Engineering & Technology, Volume 39, Issue 4.
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