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Medical image segmentation using watershed segmentation with texture-based region merging.

H P Ng¹, S Huang, S H Ong

¹Biomedical Imaging Lab, Agency for Science Technology and Research, Singapore. ng_hsiao_piau@sbic.a-star.edu.sg

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

|January 24, 2009

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This study introduces a novel method to improve watershed algorithm image segmentation. By thresholding gradient magnitude and merging similar regions, it significantly reduces over-segmentation in medical imaging.

Area of Science:

Medical image analysis
Computer vision
Biomedical engineering

Background:

The watershed algorithm is widely used for image segmentation due to its ability to create complete image divisions.
A significant drawback of the watershed algorithm is its susceptibility to over-segmentation, which hinders accurate anatomical representation in medical imaging.

Purpose of the Study:

To address the over-segmentation issue in watershed-based medical image segmentation.
To improve the representation of anatomical structures by refining initial segmentation maps.

Main Methods:

Implemented an automated thresholding technique on the gradient magnitude image.
Performed post-segmentation merging based on textural feature similarity (angular second moment, contrast, entropy, inverse difference moment) between neighboring partitions.

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Applied the method to segment facial anatomical structures from magnetic resonance (MR) images.

Main Results:

Achieved a 92.6% overlap index compared to manual contour tracings for facial anatomical structures.
Successfully merged over 80% of initial partitions, demonstrating a substantial reduction in over-segmentation.
Presented comparative results of the watershed algorithm with and without the proposed post-segmentation merging.

Conclusions:

The proposed method effectively reduces over-segmentation in watershed-based medical image segmentation.
This technique enhances the accuracy of anatomical structure representation in MR images.
The merging strategy based on textural similarity offers a robust solution for refining segmentation maps.