Classification of Abdominal CT Images bearing Liver Tumor Using Structural Similarity Index and Support Vector Machine

• Main Authors: Ayesha Amir Siddiqi, Attaullah Khawaja, Adnan Hashmi
• Format: Article
• Language: English
• Published: Mehran University of Engineering and Technology 2020-10-01
• Series: Mehran University Research Journal of Engineering and Technology
• Online Access: https://publications.muet.edu.pk/index.php/muetrj/article/view/1833
• ISSN: 0254-7821; 2413-7219

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.