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Updated: May 16, 2026

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

Hybrid softcomputing model for lesion identification and information combination: some case studies.

Arpit Srivastava¹, Abhinav Asati, Sandeep Kumar

¹Department of Electronics and Communication Engineering, Maulana Azad National Institute of Technology, Madhya Pradesh Bhopal 462051, India. arpit.nitbpl@gmail.com

International Journal of Data Mining and Bioinformatics

|November 20, 2012

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This study introduces an improved hybrid algorithm for brain image segmentation, enhancing accuracy by integrating Rough and Fuzzy sets. The novel approach accelerates clustering for better analysis of MR T1 and MR T2 brain images.

Area of Science:

Medical Imaging
Artificial Intelligence
Computational Neuroscience

Background:

Accurate segmentation of human brain images is crucial for neurological disorder diagnosis and treatment.
Existing clustering algorithms face challenges with uncertainty and vagueness in image data.
Hybrid approaches are needed to improve segmentation accuracy and efficiency.

Purpose of the Study:

To develop an improved hybrid algorithm for brain image segmentation and information fusion.
To enhance clustering by integrating Rough sets, Fuzzy sets, and probabilistic/possibilistic memberships.
To accelerate the segmentation process for efficient analysis of MR T1 and MR T2 brain images.

Main Methods:

A novel hybrid clustering algorithm integrating Rough sets, Fuzzy sets, probabilistic, and possibilistic memberships (RFPCM).

Related Experiment Videos

Last Updated: May 16, 2026

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

Application of Rough set approximations to handle uncertainty and vagueness in class definitions.

Image fusion using wavelet and curvelet-based techniques.

Implementation of a membership suppression mechanism to accelerate clustering.

Main Results:

The proposed RFPCM algorithm demonstrates improved clustering performance for brain image segmentation.
Wavelet and curvelet fusion techniques effectively combine segmented image information.
The membership suppression mechanism significantly speeds up the segmentation process.
Successful application to MR T1 and MR T2 brain images.

Conclusions:

The developed hybrid algorithm offers a robust and efficient method for human brain image segmentation.
Integration of Rough and Fuzzy set concepts enhances the handling of data uncertainty.
The accelerated segmentation process facilitates quicker and more accurate analysis of brain MRI data.