Detection Of Abnormal Liver In Ultrasonic Images From Fcm Features

The objective of this paper is to detect the liver abnormalities from ultrasonicimage database. The liver cancer is one of the major health issuesnow a day. MedicalImaging techniques are proposed to diagnose the abnormalities in the earlier stage. In thispaper extracted Fuzzy C means (FCM) Clustering features of liver images and fiveclassifiers like Expectation maximization, Gaussian Mixture Model, Linear DiscriminantAnalysis,Bayesian Linear Discriminant Analysis Classifier, Logistic regression classifierare used to detect the normal or abnormal condition of the liver. The classifierperformance are analyzed by the bench mark parameters Sensitivity, Specificity, Accuracy,Precision, Error Rate, Mathew Correlation Coefficient (MCC), and Classifier SuccessIndex (CSI) and compared. The Logistic regression achieved a higher accuracy of 80.95%and outperformed other four classifiers.