Performance evaluation of feature extraction methods for classifying abnormalities in ultrasound liver images using neural network.
Image analysis techniques have played an important role in several medical applications. In general, the applications involve the automatic extraction of features from the image which is further used for a variety of classification tasks, such as distinguishing normal tissue from abnormal tissue. In this paper, the classification of ultrasonic liver images is studied by using texture features extracted from Laws' method, autocorrelation method, Gabor wavelet and edge frequency method. The features from these methods are used to classify three sets of ultrasonic liver images-normal, cyst and benign and how well they suit in classifying the abnormalities is reported. A neural network classifier is employed to evaluate the performance of these features based on their recognition ability.
Poonguzhali
S
S
Centre for Med. Electron., Anna Univ., Guindy, Chennai, Tamil Nadu, India. poongsma@yahoo.com
Ravindran
G
G
D000465:Algorithms; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000295:instrumentation; D007091:Image Processing, Computer-Assisted; D008099:Liver / Q000000981:diagnostic imaging*; D008107:Liver Diseases / Q000145:classification* / Q000175:diagnosis*; D008113:Liver Neoplasms / Q000175:diagnosis* / Q000000981:diagnostic imaging*; D015233:Models, Statistical; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation; D014463:Ultrasonography / Q000295:instrumentation* / Q000379:methods*