Statistical Filters for Processing and Reconstruction of 3D Brain MRI

Abstract

Construction of 3D brain MRI images from 2D brain MRI images is an important step towards an accurate diagnosis and for a correct treatment plan for any disease. The volume segmentation is a challenging task due to the poor quality of DTI brain images. This paper aims to evaluate the efficacy of the spatial statistical Maximum (Max) and Minimum (Min) filters in the construction of 3D brain MRI images. These well-known simple filters were used to analyze the 3D image construction together with noise removal techniques operated by them. Using these semi-processed images, a binarization operation has been applied and a similarity investigation was performed using the Dice score. A dataset of 2D processing images is used for the construction of the 3D images by means of the volume imaging technique. The effectiveness of the 3D construction imaging algorithm was investigated through the mean of the volume computation. 3D volume measurements were performed using ImageJ and a real-time database of brain MRI images that contains image stacks from both healthy and ischemic stroke patients. The Minimum filter provides the best results by keeping the image characteristics and local geometries.