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Joint segmentation-registration of organs using geometric models.

Alper Ayvaci¹, Daniel Freedman

¹Department of Computer Science, Rensselaer Polytechnic Institute, 110 8th Street, Troy, NY 12180, USA. ayvaca@cs.rpi.edu

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

|November 16, 2007

Summary

This study introduces a new method for segmenting organs in CT and MR images using shape models and graph-cuts. The algorithm enhances accuracy and efficiency for medical image analysis.

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Area of Science:

Medical Imaging
Computer Vision
Image Segmentation

Background:

Accurate organ segmentation in CT and MR images is crucial for medical diagnosis and treatment planning.
Challenges in segmentation arise from similar intensity profiles between organs and surrounding tissues.

Purpose of the Study:

To present a novel, robust method for organ segmentation in medical images.
To address segmentation difficulties caused by similar intensity profiles and complex anatomical structures.

Main Methods:

Utilizes a shape model of the target organ for robust segmentation.
Employs graph-cuts for image labeling and level-sets for incorporating shape priors.
Proposes a unified registration-segmentation framework to ensure accurate template registration.

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Optimizes computational cost by operating on watershed regions instead of individual voxels.

Main Results:

Demonstrates the accuracy and robustness of the proposed segmentation algorithm on medical imaging datasets.
The unified framework effectively solves the registration-segmentation problem.

Conclusions:

The novel method provides accurate and efficient organ segmentation in CT and MR images.
The approach is particularly effective for challenging cases involving organs with similar intensity profiles.