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Unsupervised connectivity-based thresholding segmentation of midsagittal brain MR images
C Lee1, S Huh, T A Ketter
1Division of Electrical Engineering, Yonsei University, Seoul, South Korea.
Computers in Biology and Medicine
|October 24, 1998
Summary
This study introduces an automated algorithm for segmenting midsagittal brain MR images. The novel method accurately separates brain regions using adaptive thresholding and landmark-based preprocessing.
Area of Science:
- Medical Imaging
- Neuroscience
- Computer Vision
Background:
- Accurate segmentation of brain Magnetic Resonance Imaging (MRI) is crucial for quantitative analysis.
- Existing segmentation methods often require manual intervention or struggle with complex anatomical structures.
Purpose of the Study:
- To develop an automated algorithm for segmenting midsagittal brain MR images.
- To improve the accuracy and efficiency of brain image segmentation.
Main Methods:
- Applying thresholding to obtain binary images and locating anatomical landmarks.
- Preprocessing binary images using landmarks and anatomical information for simplification.
- Utilizing a novel connectivity-based threshold algorithm with adaptive thresholding for region separation.
Main Results:
- The algorithm successfully segmented midsagittal brain MR images.
- Satisfactory results were obtained on a dataset of 120 images, demonstrating robustness.
Conclusions:
- The proposed automated segmentation algorithm is effective and robust for midsagittal brain MR images.
- This method offers a simplified and efficient approach to brain image analysis.