This paper presents a study of the state of the art techniques applied to computer based analysis and classification of liver diseases from ultrasound images. The diseased portions from the ultrasound images are analyzed and categorized using techniques such as Despeckling, Segmentation, Feature extraction and Classification. Automatic segmentation of ultrasound images is complicated due to the fact that the image may include other organs which are close to the liver, irregular structure of disease, poor quality of image, lack of color cues, and lack of definite boundaries and presence of noise. This work makes a study of different techniques used in the different phases of biomedical liver ultrasound processing such as noise removal, segmentation, Feature Extraction and classification. This work also presents the segmentation results obtained using Gray Level Difference Weights Method on 10 types of liver diseases from ultrasound images.
M. M. and K. Raghesh Krishnan, “A Study of the Phases of Classification of Liver diseases from Ultrasound Images and Gray Level Difference Weights based Segmentation”, IEEE Digital Library, 2017 International Conference on Communication and Signal Processing (ICCSP). IEEE, Chennai, India, 2017.