Co-occurrence Matrices for Volumetric Data
نویسندگان
چکیده
In this paper, we investigate a new approach to the cooccurrence matrix currently used to extract textural features: co-occurrence matrices for volumetric data. While traditional texture metrics have concentrated on 2D texture, 3D imaging modalities are becoming more and more prevalent, providing the possibility of examining texture as a volumetric phenomenon. Just as computer graphics have used 3D textures as a more realistic alternative to 2D texture mapping, we expect that texture derived from volumetric data will have better discriminating power than 2D texture derived from slice data. An experimental study has been conducted in which the results for textural features derived from 2D are compared to those results derived from using cooccurrence matrices for volumetric data. Our preliminary experimental results indicate that the volumetric texture features have better discriminating power than 2D texture derived from slice data.
منابع مشابه
Identifying Age-related Macular Degeneration In Volumetric Retinal Images
Age-related Macular Degeneration (AMD) is a retina disorder, which is currently on the increase. In this paper, we investigate the use two different statistical methods for detecting AMD in Optical Coherence Tomography (OCT) volumetric data where by a 3-D image is represented using a combination of two matrices: a Voxel Co-occurrence Matrix (VCM) and a Voxel Run-Length Matrix (VRLM). Statistica...
متن کاملHyperspectral image classification based on volumetric texture and dimensionality reduction
A novel approach using volumetric texture and reduced-spectral features is presented for hyperspectral image classification. Using this approach, the volumetric textural features were extracted by volumetric gray-level co-occurrence matrices (VGLCM). The spectral features were extracted by minimum estimated abundance covariance (MEAC) and linear prediction (LP)-based band selection, and a semi-...
متن کاملThree Dimensional Texture Computation of Gray Level Co-occurrence Tensor in Hyperspectral Image Cubes
The traditional gray level co-occurrence matrix (GLCM) is in two-dimensional form. Because hyperspectral imagery in the feature space has the characteristic of volumetric data, it has a great potential for three-dimensional texture analysis. Previous studies have successfully extended traditional 2D GLCM to a 3D form (Gray Level Co-occurrence Matrix for Volumetric Data, GLCMVD) for extracting f...
متن کاملCo-occurrence Matrices and their Applications in Information Science: Extending ACA to the Web Environment
Co-occurrence matrices, such as co-citation, co-word, and co-link matrices, have been used widely in the information sciences. However, confusion and controversy have hindered the proper statistical analysis of this data. The underlying problem, in our opinion, involved understanding the nature of various types of matrices. This paper discusses the difference between a symmetrical co-citation m...
متن کاملVolumetric Texture Description and Discriminant Feature Selection for MRI
This paper considers the problem of classification of Magnetic Resonance Images using 2D and 3D texture measures. Joint statistics such as co-occurrence matrices are common for analysing texture in 2D since they are simple and effective to implement. However, the computational complexity can be prohibitive especially in 3D. In this work, we develop a texture classification strategy by a sub-ban...
متن کامل