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Outer-Boundary Assisted Segmentation and Quantification of Trabecular Bones by an Imagej Plugin

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Bone Region Segmentation in Medical Images Based on Improved Watershed Algorithm.

Jun Zhou^1,2, Mei Yang^3,4

¹School of Artificial Intelligence, Chongqing Business Vocational College, Chongqing 401331, China.

Computational Intelligence and Neuroscience

|April 4, 2022

Summary

This summary is machine-generated.

This study introduces an improved image segmentation method combining K-means and watershed algorithms to overcome oversegmentation issues in human bone imaging. The new approach effectively segments bone regions, significantly reducing segmentation blocks compared to traditional watershed methods.

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Area of Science:

Medical Imaging
Computer Vision
Image Processing

Background:

The watershed algorithm is a common image segmentation technique.
However, it suffers from oversegmentation, particularly in complex medical images like human bone scans.

Purpose of the Study:

To propose a novel image segmentation algorithm that combines K-means clustering with an improved watershed algorithm.
The goal is to effectively segment human bone regions and mitigate the oversegmentation problem.

Main Methods:

Denoising human skeleton images using a Gaussian filter.
Applying K-means clustering to segment the image and extract the initial bone region.
Utilizing morphological operations (opening and closing) to refine the bone region.
Implementing an improved watershed algorithm with adaptive merging based on block similarity and Otsu's method for thresholding.

Main Results:

The proposed algorithm significantly reduced the number of segmented blocks from 2775-3357 (watershed) to 221-559.
Effectively segmented human bone regions from 100 medical images.
Demonstrated superior performance in solving the oversegmentation issue.

Conclusions:

The combined K-means and improved watershed algorithm effectively addresses the oversegmentation problem in human bone image segmentation.
This method offers a more accurate and efficient approach for segmenting medical imaging targets.