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The z-transform is a powerful mathematical tool used in the analysis of discrete-time signals and systems. It is a crucial tool in the analysis of discrete-time systems, but its convergence is limited to specific values of the complex variable z. This range of values, known as the Region of Convergence (ROC), is fundamental in determining the behavior and stability of a system or signal. The ROC defines the region in the complex plane where the z-transform converges, which can take various...

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Updated: Jul 17, 2026

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

A region-based approach combining marker-controlled active contour model and morphological operator for image

Phooi Yee Lau¹, Shinji Ozawa

¹Center for Information, Communication and Media Technologies, Keio University, Yokohama, Japan.

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference

|February 3, 2007

Summary

This study introduces a novel medical image segmentation method for blood vessel analysis. The approach combines active contours and mathematical morphology for robust vessel detection and localization.

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Area of Science:

Medical Imaging
Image Analysis
Computational Biology

Background:

Accurate segmentation of blood vessels in medical images is crucial for diagnosis and treatment planning.
Existing methods may struggle with sensitivity, initialization, and robustness.

Purpose of the Study:

To develop and evaluate a new segmentation strategy for localizing and parameterizing blood vessels in medical images.
To enhance the robustness and accuracy of blood vessel detection using a combined approach.

Main Methods:

A novel segmentation strategy integrating a marker-controlled active contour model with mathematical morphology.
Utilizing implicit active contours (level sets) and mathematical morphology operators (erosion, dilation, opening, closing) for vessel pattern detection.
User-initialized contour placement guided by vessel-like patterns, refined by image-driven forces.

Main Results:

The proposed method demonstrates robustness in detecting blood vessels.
Experimental results show effective localization and parameterization of blood vessels.
Both visual and quantitative evaluations confirm the method's performance.

Conclusions:

The combined active contour and mathematical morphology approach offers a robust solution for medical image segmentation of blood vessels.
The method's performance is sensitive to initialization but provides reliable results.
This technique facilitates subsequent analysis of vascular structures in medical imaging.