A Fast, Semi-Automatic Brain Structure Segmentation Algorithm for Magnetic Resonance Imaging
This is an incremental improvement for medical imaging researchers and clinicians, addressing the need for flexible and efficient brain structure segmentation.
The paper tackles the problem of segmenting brain structures in MRI by introducing a semi-automatic method that combines region-based and boundary-based approaches separately to improve efficiency, achieving high accuracy and efficiency in experiments.
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.