Classification of Benign and Malignant Bone Lesions on CT Images using Random Forest

Bones form the supporting framework of the body. It has a hard outer layer made of compact (cortical) bone that covers a lighter spongy (trabecular) bone inside. Osteoblast (cell that lays down new bone) and osteoclast (cell that dissolves old bone) are the two types of cells present in the bone. Throughout our lifetime, new bone keeps replacing the dissolving old bone. An uncontrollable division of these cells along with fat cells and blood forming cells in the bone marrow could destroy surrounding body tissue causing bone cancer. This work presents a Computer Aided Diagnosis (CAD) system that helps radiologists in differentiating malignant and benign bone lesions in the spine on CT images. Firstly, the lesions are segmented using active contour models and then texture is analyzed through second order statistical measurements based on the Gray Level Co-occurrence Matrix (GLCM). We use features like autocorrelation, contrast, cluster shade, cluster prominence, energy, maximum probability, variance and difference variance to train and test the Random Forest. The aim of this paper is to discuss a technique that improves the sensitivity, specificity and accuracy of detecting the bone lesions.