Extended gray level co-occurrence matrix computation for 3D image volume

Nurulazirah M. Salih; Dyah Ekashanti Octorina Dewi

doi:10.1117/12.2266977

8 February 2017 Extended gray level co-occurrence matrix computation for 3D image volume

Nurulazirah M. Salih, Dyah Ekashanti Octorina Dewi

Proceedings Volume 10225, Eighth International Conference on Graphic and Image Processing (ICGIP 2016); 102250W (2017) https://doi.org/10.1117/12.2266977
Event: Eighth International Conference on Graphic and Image Processing, 2016, Tokyo, Japan

Abstract

Gray Level Co-occurrence Matrix (GLCM) is one of the main techniques for texture analysis that has been widely used in many applications. Conventional GLCMs usually focus on two-dimensional (2D) image texture analysis only. However, a three-dimensional (3D) image volume requires specific texture analysis computation. In this paper, an extended 2D to 3D GLCM approach based on the concept of multiple 2D plane positions and pixel orientation directions in the 3D environment is proposed. The algorithm was implemented by breaking down the 3D image volume into 2D slices based on five different plane positions (coordinate axes and oblique axes) resulting in 13 independent directions, then calculating the GLCMs. The resulted GLCMs were averaged to obtain normalized values, then the 3D texture features were calculated. A preliminary examination was performed on a 3D image volume (64 x 64 x 64 voxels). Our analysis confirmed that the proposed technique is capable of extracting the 3D texture features from the extended GLCMs approach. It is a simple and comprehensive technique that can contribute to the 3D image analysis.

Citation Download Citation

Nurulazirah M. Salih and Dyah Ekashanti Octorina Dewi "Extended gray level co-occurrence matrix computation for 3D image volume", Proc. SPIE 10225, Eighth International Conference on Graphic and Image Processing (ICGIP 2016), 102250W (8 February 2017); https://doi.org/10.1117/12.2266977

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