Development of CAD System for Detection, Classification, Retrieval and 3d Reconstruction of Brain and Liver Tumors on MRI and CT Images

Abstract

Brain and liver tumors are the life-threatening diseases due to low survival rate. Hence, accurate diagnosis of brain and liver tumors is necessary to provide effective treatment. Medical imaging techniques like Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) help in acquiring images of the tumor. The visual analysis of these medical images by the radiologist is time consuming, subjective and inaccurate. The needle biopsy of the tumor provides accurate diagnosis but it is an invasive technique and generally not recommended. In order to overcome these drawbacks, there is a need to develop Computer-Aided Diagnosis (CAD) system for assisting the radiologist in fast and accurate diagnosis of tumors. Therefore, this thesis proposes an effective and efficient CAD system for tumor detection, classification, Content-Based Image Retrieval (CBIR), and 3D reconstruction to provide complete assistance to the radiologist in the diagnosis of brain and liver tumors. In the first methodology, this thesis aims at tumor detection, by proposing automatic, effective and efficient segmentation methods for brain and liver tumors on medical images. The brain tumor is detected using the proposed segmentation technique based on Modified Fuzzy C-Means (MFCM) clustering algorithm. The liver tumor is detected using the proposed segmentation technique based on the automatic region growing algorithm. In the second methodology, this thesis targets at identification of the type of brain/liver tumor as benign or malignant, by proposing an effective and efficient tumor classification scheme. Precisely, the proposed scheme represents the tumor characteristics using its significant features, selects most discriminating features using a two-level feature selection technique consisting of Information Gain (IG) based feature ranking and Independent Component Analysis (ICA) based feature section methods. Then, the tumor is classified using an ensemble classifier consisting of Support Vector Machine (SVM), Artificial Neural Network (ANN) and k-Nearest Neighbor (k-NN) classifiers. In the third methodology, this thesis proposes two CBIR methods based on image rotation correction and rotation invariant features to assist the radiologist in brain/liver tumor diagnosis based on past resolved cases. In order to provide fast retrieval of tumor images from the database, the tumor features in the database are indexed using the proposed indexing technique called as Cluster with IG-ICA and KD-tree (CIKD). The features in the database are partitioned into different groupsusing modified k-means clustering which identifies the number of clusters and initial cluster centers automatically. In the fourth methodology, this thesis aims to build the 3D model of the brain/liver tumor, by proposing an effective and efficient 3D reconstruction scheme. Precisely, it proposes an enhanced shape-based interpolation algorithm to estimate missing slices in a given set of brain/liver tumor slices. Further, the 3D mesh simplification algorithm is proposed to reduce the number of triangles in the reconstructed mesh and accelerate the rendering phase. The tumor volume is also computed to assist the radiologist in estimating the stage of cancer. Experiments are carried out on a dataset consisting of MRI images of the brain tumor and CT images of the liver tumor. Experimental results demonstrate that the proposed CAD system is automatic, effective and efficient in the diagnosis of brain and liver tumors.