Automatic Identification of Ultrasound Liver Cancer Tumor Using Support Vector Machine
Venugopal Ulagamuthalvi, D. Sridharan · 2012
Ultrasound liver tumor image are naturally have more spackle noise. Automatic identification of ultrasound liver tumor image is a challenging task. In this proposed system, weapproach fully automatic machine learning system for indentifying the liver cancer tumor from ultrasound images. First, we segment the liver image by calculating the textural features from co-occurrence matrix and run length method. This is the best method for segmentation of ultrasound liver cancer tumor images because it is not affected speckle noise and also preserves spatial information. For classification Support Vector machine are a general algorithm based on the risk bounds of statistical learning theory. They have found numerous applications, such as in optical character recognition, object detection,face verification, text categorization and so on. The textural features for different features methods are given as input to the SVM individually. Performance analysis train and test datasets carried out separately using SVM Model. Whenever an ultrasonic liver cancer tumor image is given to the SVM classifier system, the features are calculated, classified, as normal, benign and malignant liver cancer tumor. We hope the result will be helpful to the physician to identify the liver cancer in non invasive method.