Colorization and Automated Segmentation of Human T2 MR Brain Images for Characterization of Soft Tissues

Abstract

Characterization of tissues like brain by using magnetic resonance (MR) images and colorization of the gray scale image has been reported in the literature, along with the advantages and drawbacks. Here, we present two independent methods; (i) a novel colorization method to underscore the variability in brain MR images, indicative of the underlying physical density of bio tissue, (ii) a segmentation method (both hard and soft segmentation) to characterize gray brain MR images. The segmented images are then transformed into color using the above-mentioned colorization method, yielding promising results for manual tracing. Our color transformation incorporates the voxel classification by matching the luminance of voxels of the source MR image and provided color image by measuring the distance between them. The segmentation method is based on single-phase clustering for 2D and 3D image segmentation with a new auto centroid selection method, which divides the image into three distinct regions (gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) using prior anatomical knowledge). Results have been successfully validated on human T2-weighted (T2) brain MR images. The proposed method can be potentially applied to gray-scale images from other imaging modalities, in bringing out additional diagnostic tissue information contained in the colorized image processing approach as described.

A Proposed HSV-Based Pseudo Coloring Scheme for Enhancing Medical Image

“…al. [6] also used image fusion for brain MRI images. They utilized Single slice of T2-weighted (T2) brain MR images using two methods; (i) A novel colorization method to underscore the variability in brain MR images, indicative of the underlying physical density of bio-tissue, (ii) A segmentation method to characterize gray brain MR images.…”

“…In our proposed system, both models are used considering the same transformations (6). Finally, the final RGB color image is generated after the well-known HSV/HSL to RGB conversion [3].…”

“…There are two approaches for medical images colorization in the literature; semi-automatic (interactive) pseudo-coloring [1] and automatic (non-interactive) pseudo-coloring [4][5][6][7][8][9][10].…”

Medical Image Colorization for Better Visualization and Segmentation

“…In another technique brain MRI classification was achieved by applying two independent methods, (i) highlighting the variability of input images, (ii) segmentation for outlining gray images [2]. It was then followed by a colorization technique to create chromatic brain MRI image.…”

“…The short fall of this technique is a consumption of higher system resources during execution [15,17,27]. Comparing the luminance intensity among images and colorized MRI (magnetic resonance imaging) images [2].…”