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

Authors
Journal
PLoS ONE
1932-6203
Publisher
Public Library of Science
Publication Date
Volume
7
Issue
3
Identifiers
DOI: 10.1371/journal.pone.0033616
Keywords
  • Research Article
  • Biology
  • Anatomy And Physiology
  • Neurological System
  • Developmental Biology
  • Morphogenesis
  • Engineering
  • Signal Processing
  • Medicine
  • Neurology
  • Radiology
  • Diagnostic Radiology
Disciplines
  • Medicine

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.

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