Corroborating the Subjective Classification of Ultrasound Images of Normal and Fatty Human Livers by the Radiologist through Texture Analysis and SOM

The objective of this study is to establish that subjective evaluation of fatty as well as normal ultrasound human liver images based on echotexture (spatial pattern of echoes) and echogenicity by visual inspection can be corroborated by Haralick's statistical texture analysis. Seventy-six ultrasound scan images of human normal livers and twenty-four ultrasound images of fatty livers as identified by the radiologist on the basis of echotexture and echogenecity, have been collected from hospital for this study. An unsupervised neural network learning technique, namely, Self Organising Map (SOM) has been employed to generate profile plots. Using Student's t like statistic for each feature as a measure of distinction between normal and fatty livers, two most appropriate features, namely, maximum probability (Maxp) and uniformity (Uni) are selected from this profile plots. These two features are found to form clusters with little overlap for normal and fatty livers. Thus statistical texture analysis of the ultrasound human images using 'Maxp" and "Uni" presented the best results for corroborating the classification as made the radiologist by visual inspection. This work may be a humble beginning to model the radiologists' perceptual findings that may emerge in future as a new tool with respect to 'ultrasonic biopsy'.