An improved brain image classification technique with mining and shape prior segmentation procedure

A Method to Differentiate Mild Cognitive Impairment and Alzheimer in MR Images using Eigen Value Descriptors

Automated analysis and differentiation of mild cognitive impairment and Alzheimer's condition using MR images is clinically significant in dementic disorder. Alzheimer's Disease (AD) is a fatal and common form of dementia that progressively affects the patients. Shape descriptors could better differentiate the morphological alterations of brain structures and aid in the development of prospective disease modifying therapies. Ventricle enlargement is considered as a significant biomarker in the AD diagnosis. In this work, a method has been proposed to differentiate MCI from the healthy normal and AD subjects using Laplace-Beltrami (LB) eigen value shape descriptors. Prior to this, Reaction Diffusion (RD) level set is used to segment the ventricles in MR images and the results are validated against the Ground Truth (GT). LB eigen values are infinite series of spectrum that describes the intrinsic geometry of objects. Most significant LB shape descriptors are identified and their performance is analysed using linear Support Vector Machine (SVM) classifier. Results show that, the RD level set is able to segment the ventricles. The segmented ventricles are found to have high correlation with GT. The eigen values in the LB spectrum could show distinction in the feature space better than the geometric features. High accuracy is observed in the classification results of linear SVM. The proposed automated system is able to distinctly separate the MCI from normal and AD subjects. Thus this pipeline of work seems to be clinically significant in the automated analysis of dementic subjects.

A Novel Algorithm for Detection and Classification of Brain Tumors

Segmentation of an image is the partition or separation of the image into disjoint regions of related features. In clinical practice, magnetic resonance imaging (MRI) is used to differentiate pathologic tissues from normal tissues, especially for brain tumors. The main objective of this paper is to develop a system that can follow a medical technician way of work, considering his experience and knowledge. In this paper, a step by step methodology for the automatic MRI brain tumor segmentation and classification is presented. Initially acquired MRI brain images are preprocessed by the Gaussian filter. After preprocessing, initial segmentation is done by hierarchical topology preserving map (HTPM). From the resultant images, the features are extracted using gray level co-occurrence matrix (GLCM) method, and the same are given as inputs to adaptive neuro fuzzy inference systems (ANFIS) for final segmentation and the classification of brain images into normal or abnormal. In case of abnormal, the MRI brain images are classified as benign subject (tumor without cancerous tissues) or malignant subject (tumor with cancerous tissues). Based on the analysis, it has been discovered that the overall accuracy of classification of our method is above 94%, and F1-score is about 1. The simulation results also show that the proposed approach is a valuable diagnosing technique for the physicians and radiologists to detect the brain tumors.