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Updated: May 29, 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

[MRI kidney segmentation based on thresholding and active contour model].

Xiaowei Wu¹, Chuanqing Zhou, Qiu-Shi Ren

¹Department of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.

Zhongguo Yi Liao Qi Xie Za Zhi = Chinese Journal of Medical Instrumentation

|September 30, 2011

Summary

This summary is machine-generated.

This study introduces a hybrid method for segmenting kidney regions in MRI scans. The technique combines thresholding and active contour models to accurately delineate the renal cortex and medulla.

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

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Published on: January 7, 2019

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

Area of Science:

Medical Imaging
Biomedical Engineering
Image Analysis

Context:

Accurate segmentation of kidney structures from MRI is crucial for diagnosis and treatment planning.
Existing segmentation methods may lack precision in delineating complex renal anatomies.
Renal imaging analysis requires robust techniques for identifying specific tissue regions.

Purpose:

To develop and validate a hybrid image segmentation method for precise extraction of kidney cortical and medullary regions from MRI data.
To integrate thresholding segmentation with a simplified active contour model for improved accuracy.
To enhance the delineation of renal substructures using gradient information.

Summary:

A hybrid image segmentation approach is proposed for MRI renal images.
The method employs preprocessing and thresholding to generate a binary mask, followed by a simplified active contour model for initial contour generation.
A traditional active contour model refines the contour, guided by image gradients to accurately identify renal cortex and medulla boundaries.

Impact:

Provides a more accurate and automated method for renal image segmentation.
Facilitates improved quantitative analysis of kidney structures in clinical settings.
Contributes to advancements in medical image processing for urological applications.