Data on MRI brain lesion segmentation using K-means and Gaussian Mixture Model-Expectation Maximization

Area of Science:

• Medical imaging analysis
• Artificial intelligence in healthcare

Background:

• Accurate segmentation of brain lesions in MRI is crucial for diagnosis and treatment monitoring.
• Automated methods are needed to improve efficiency and consistency in lesion segmentation.

Purpose of the Study:

• To compare the performance of K-means and Gaussian Mixture Model-Expectation Maximization (GMM-EM) algorithms for automatic brain MRI lesion segmentation.
• To evaluate the impact of lesion size on the accuracy and sensitivity of these segmentation methods.

Main Methods:

• Brain MRI data were analyzed using K-means clustering and GMM-EM algorithms for lesion segmentation.
• Performance metrics including accuracy, sensitivity, specificity, false positive rate, and misclassification rate were calculated for both algorithms.

Main Results:

• Both K-means and GMM-EM algorithms demonstrated comparable performance in segmenting brain MRI lesions.
• No significant differences in accuracy, sensitivity, or other performance metrics were found between the two methods.
• Lesion size did not significantly affect the accuracy or sensitivity of either K-means or GMM-EM segmentation.

Conclusions:

• K-means and GMM-EM are both effective and comparable automated methods for brain MRI lesion segmentation.
• These algorithms offer reliable performance regardless of lesion size, aiding in consistent clinical assessment.