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Updated: Mar 17, 2026

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

Automated Robust Image Segmentation: Level Set Method Using Nonnegative Matrix Factorization with Application to

Dimah Dera¹, Nidhal Bouaynaya¹, Hassan M Fathallah-Shaykh²

¹Department of Electrical and Computer Engineering, Rowan University, Glassboro, NJ, 08028, USA.

Bulletin of Mathematical Biology

|July 16, 2016

Summary

This summary is machine-generated.

A new method combines nonnegative matrix factorization (NMF) and level set methods (LSM) for automated image segmentation. This NMF-LSM approach accurately identifies brain tumor regions in MRI scans, improving diagnostic capabilities.

Keywords:

Image segmentation Intensity inhomogeneity LSM MRI NMF

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Area of Science:

Medical image analysis
Computational imaging
Biomedical engineering

Background:

Automated image segmentation is crucial for medical diagnostics.
Existing methods often struggle with accuracy, robustness, and automation.

Observation:

A novel deformable model integrating probabilistic nonnegative matrix factorization (NMF) and level set methods (LSM) was developed.
NMF determines the number and distribution of image regions, informing the LSM's energy functional.

Findings:

The NMF-LSM method demonstrated superior performance on synthetic and clinical MRI data, including brain tumors.
It offers fully automated, highly accurate segmentation, robust to noise and initial conditions.
The method relies on histogram information, avoiding nuisance parameters and detecting small regions.

Implications:

This approach provides a general solution for robust, automated region discovery and segmentation in heterogeneous images.
NMF-LSM shows potential for early detection of tumor progression and monitoring treatment response in clinical MRI.
It addresses a significant need for automated segmentation in medical imaging analysis.