Detection of Liver Cancer Using Modified Fuzzy Clustering and Decision Tree Classifier in CT Images

• Published in: Pattern recognition and image analysis Vol. 29; no. 2; pp. 201 - 211
• Main Authors: Das, Amita, Das, Priti, Panda, S. S., Sabut, Sukanta
• Format: Journal Article
• Language: English
• Published: Moscow Pleiades Publishing 01.04.2019; Springer Nature B.V
• Subjects: Algorithms; Classifiers; Clustering; Computed tomography; Computer Science; Decision trees; Feature extraction; Image detection; Image Processing and Computer Vision; Image segmentation; Lesions; Liver; Liver cancer; Mathematical Method in Pattern Recognition; Medical imaging; Medical science; Multilayer perceptrons; Pattern Recognition; Sensitivity analysis; Similarity coefficient method; computed tomography; metastatic carcinoma; classifier; segmentation; liver cancer; hepatocellular carcinoma
• ISSN: 1054-6618; 1555-6212
• DOI: 10.1134/S1054661819020056

Manual detection and characterization of liver cancer using computed tomography (CT) scan images is a challenging task. In this paper, we have presented an automatic approach that integrates the adaptive thresholding and spatial fuzzy clustering approach for detection of cancer region in CT scan images of liver. The algorithm was tested in a series of 123 real-time images collected from the different subjects at Institute of Medical Science and SUM Hospital, India. Initially the liver was separated from other parts of the body with adaptive thresholding and then the cancer affected lesions from liver was segmented with spatial fuzzy clustering. The informative features were extracted from segmented cancerous region and were classified into two types of liver cancers i.e., hepatocellular carcinoma (HCC) and metastatic carcinoma (MET) using multilayer perceptron (MLP) and C4.5 decision tree classifiers. The performance of the classifiers was evaluated using 10-fold cross validation process in terms of sensitivity, specificity, accuracy and dice similarity coefficient. The method was effectively detected the lesion with accuracy of 89.15% in MLP classifier and of 95.02% in C4.5 classifier. This results proves that the spatial fuzzy c-means (SFCM) based segmentation with C4.5 decision tree classifier is an effective approach for automatic recognition of the liver cancer.