Texture Classification of 3D MR Color Images using 3D Orthogonal Rank Filters

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Arun Kumar A
Arun Kumar A
α University of Mysore University of Mysore

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Texture Classification of 3D MR Color Images using 3D Orthogonal Rank Filters

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Abstract

The term ‘texture’ refers to patterns arranged in an order in a line or a curve. Textures allow one to make a meaningful interpretation of certain geometric regularity of spatially repeated patterns. In addition, texture also exhibits useful information about spatial distribution of color or gray intensities in an image. Correct interpretation of latent textures of various tissues in a body is an important requirement for a surgeon as a preoperative measure. In this context, extraction of textures in an MR scanned 3D image would assist a medical professional in the preoperative decision making process. This paper proposes a novel technique for extracting directional textures of a 3D MR image in all three axes separately.

References

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Arun Kumar A. 2021. \u201cTexture Classification of 3D MR Color Images using 3D Orthogonal Rank Filters\u201d. Global Journal of Computer Science and Technology - F: Graphics & Vision GJCST-F Volume 21 (GJCST Volume 21 Issue F1): .

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Issue Cover
GJCST Volume 21 Issue F1
Pg. 15- 23
Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords
Classification
GJCST-F Classification: I.2.10
Version of record

v1.2

Issue date

March 17, 2021

Language
en
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The term ‘texture’ refers to patterns arranged in an order in a line or a curve. Textures allow one to make a meaningful interpretation of certain geometric regularity of spatially repeated patterns. In addition, texture also exhibits useful information about spatial distribution of color or gray intensities in an image. Correct interpretation of latent textures of various tissues in a body is an important requirement for a surgeon as a preoperative measure. In this context, extraction of textures in an MR scanned 3D image would assist a medical professional in the preoperative decision making process. This paper proposes a novel technique for extracting directional textures of a 3D MR image in all three axes separately.

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Texture Classification of 3D MR Color Images using 3D Orthogonal Rank Filters

Arun Kumar A
Arun Kumar A University of Mysore

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