# A Fast, Semi-Automatic Brain Structure Segmentation Algorithm for Magnetic Resonance Imaging

## TL;DR

This paper introduces a hybrid semi-automatic 3D brain structure segmentation algorithm for MRI that combines region-based and boundary-based methods, offering improved efficiency and accuracy over previous techniques.

## Contribution

The proposed method uniquely separates region-based and boundary-based steps, enabling more efficient segmentation and robust seed initialization for brain MRI analysis.

## Key findings

- Achieves high accuracy in brain structure segmentation.
- Demonstrates increased efficiency compared to existing methods.
- Effective seed initialization reduces local minima issues.

## Abstract

Medical image segmentation has become an essential technique in clinical and research-oriented applications. Because manual segmentation methods are tedious, and fully automatic segmentation lacks the flexibility of human intervention or correction, semi-automatic methods have become the preferred type of medical image segmentation. We present a hybrid, semi-automatic segmentation method in 3D that integrates both region-based and boundary-based procedures. Our method differs from previous hybrid methods in that we perform region-based and boundary-based approaches separately, which allows for more efficient segmentation. A region-based technique is used to generate an initial seed contour that roughly represents the boundary of a target brain structure, alleviating the local minima problem in the subsequent model deformation phase. The contour is deformed under a unique force equation independent of image edges. Experiments on MRI data show that this method can achieve high accuracy and efficiency primarily due to the unique seed initialization technique.