Texture Classification of 3D MR Images using 2.5D Rank Filters
Arun Kumar A1, E. G. Rajan2

1Arun Kumar A*, Department of Computer Science University of Mysore, Manasa Gangotri Mysore, Karnataka, India.
2E. G. Rajan, Director, Rajiv Gandhi International School Of Information Technology, MG-MIRSA Approved Research Centre of Mysore University.
Manuscript received on September 14, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 3078-3081 | Volume-8 Issue-12, October 2019. | Retrieval Number: H6973068819/2019©BEIESP | DOI: 10.35940/ijitee.H6973.1081219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The 3-D items utilized in 3D computer games and augmented reality are empty polygon networks with surfaces concerned them. Then again, volume information portrayal stores the external surface highlights, yet in addition the highlights inside the volume. For instance, representation of 3-D MRI/CT information is tied in with appearing inside parts as well. Envisioning volumetric information requires more video memory. A large portion of the genuine 3D volume information created particularly by MRI scanners is dim dimension pictures. This paper tends to a novel system of texturizing the MRI information slides and its handling for extraction of shallow and volumetric highlights.
Keywords: Superficial & Volumetric Features, 3-D Images, Texture Classification, Monotone-coloring.
Scope of the Article: Classification