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

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 ima...

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Vydáno v:Pattern recognition and image analysis Ročník 29; číslo 2; s. 201 - 211
Hlavní autoři: Das, Amita, Das, Priti, Panda, S. S., Sabut, Sukanta
Médium: Journal Article
Jazyk:angličtina
Vydáno: Moscow Pleiades Publishing 01.04.2019
Springer Nature B.V
Témata:
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
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Shrnutí: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.
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ISSN:1054-6618
1555-6212
DOI:10.1134/S1054661819020056