Application of artificial neural networks for the classification of liver lesions by image texture parameters.
Ultrasound imaging is a powerful tool for characterizing the state of soft tissues; however, in some cases, where only subtle differences in images are seen as in certain liver lesions such as hemangioma and malignancy, existing B-scan methods are inadequate. More detailed analyses of image texture parameters along with artificial neural networks can be utilized to enhance differentiation. From B-scan ultrasound images, 11 texture parameters comprising of first, second and run length statistics have been obtained for normal, hemangioma and malignant livers. Tissue characterization was then performed using a multilayered backpropagation neural network. The results for 113 cases have been compared with a classification based on discriminant analysis. For linear discriminant analysis, classification accuracy is 79.6% and with neural networks the accuracy is 100%. The present results show that neural networks classify better than discriminant analysis, demonstrating a much potential for clinical application.
Sujana
H
H
Department of Applied Mechanics, Indian Institute of Technology, Madras, India.
Swarnamani
S
S
Suresh
S
S
D003937:Diagnosis, Differential; D016002:Discriminant Analysis; D006391:Hemangioma / Q000145:classification / Q000000981:diagnostic imaging*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008113:Liver Neoplasms / Q000145:classification / Q000000981:diagnostic imaging*; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D014463:Ultrasonography