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Related Experiment Video

Updated: Jul 7, 2026

Clinical Anthropometrics and Body Composition from 3-Dimensional Optical Imaging

Clinical Anthropometrics and Body Composition from 3-Dimensional Optical Imaging

Published on: June 7, 2024

Texture anisotropy in 3-D images.

V A Kovalev¹, M Petrou, Y S Bondar

¹Institute of Mathematics, Belarus Academy of Sciences, Gomel 246652, Belarus.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|February 12, 2008

Summary

This study compares gradient vectors and generalized co-occurrence matrices for analyzing 3-D medical image textures. The gradient-based method shows promise for microtexture characterization and pathology monitoring.

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Clinical Anthropometrics and Body Composition from 3-Dimensional Optical Imaging

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Correlative Microscopy for 3D Structural Analysis of Dynamic Interactions

Correlative Microscopy for 3D Structural Analysis of Dynamic Interactions

Published on: June 24, 2013

Area of Science:

Medical imaging analysis
Quantitative pathology
Texture analysis

Background:

Characterizing three-dimensional (3-D) textures in medical images is crucial for diagnosis and monitoring.
Existing methods for texture analysis require evaluation for robustness and applicability to complex medical data.

Purpose of the Study:

To compare two distinct approaches for 3-D texture characterization: gradient vectors and generalized co-occurrence matrices.
To assess the performance of these methods with simulated data under varying noise levels and parameters.
To explore their potential in analyzing medical volume images with microtextures for diagnostic purposes.

Main Methods:

Investigated simulated data to evaluate method behavior with noise and parameter variations.
Applied gradient-based and generalized co-occurrence matrix methods to medical volume images.
Focused on images with microtextures to assess diagnostic potential.

Main Results:

The gradient-based method demonstrated superior performance in characterizing microtextures compared to generalized co-occurrence matrices.
The gradient-based approach showed more consistent behavior in describing pathologies.
No definitive medical conclusions were drawn due to insufficient clinical data.

Conclusions:

The gradient-based method is more suitable for microtexture characterization in medical imaging.
This approach shows potential for quantifying and monitoring disease progression.
Further clinical validation is necessary to establish its diagnostic utility.