A pragmatic approach for detecting liver cancer using image processing and data mining techniques

Cancer diagnosis and treatment has a great significance due to the prevalent episodes of the diseases, high death rate and reappearance after treatment. On the world scale, cancer stands in the fifth position which causes death. Among the various cancers, liver cancer stands in the third position. Liver cancer is generally diagnosed by three different test like blood test, image test and biopsy. To make the task of detecting the liver cancer simpler, less time consuming, an effective and efficient approach is adopted for the same. In this research a computer aided diagnostic system for detecting liver cancer is put forward. The proposed detection methodology makes use of MRI, CT and USG scan imagery. K-means clustering technique is adopted so as to segment the images in order to capture the region of interest. Later, Haar wavelet transform is considered to compute the threshold values for the region of interest. The experiment put forth gave an average accuracy of 82% besides reducing the time complexity and computational complexity of the test.