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Resolving the initial contour problem of GVF snake in the sequential images
Yunhwan Seol1, Jaeseung Yu, Teahoon Kang
1Department of Electronics & Information Engineering, Korea University, Seoul, South Korea. yhseol@korea.ac.kr
Summary
This study enhances the Gradient Vector Flow (GVF) algorithm for image segmentation in video and CT sequences. By using previous contours as initial points, it improves motion tracking and segmentation accuracy.
Area of Science:
- Medical Imaging
- Computer Vision
- Image Processing
Background:
- Gradient Vector Flow (GVF) algorithm is effective for object contour extraction in static images.
- Traditional GVF faces challenges in segmenting dynamic image sequences like videos and CT scans due to motion and initialization issues.
Purpose of the Study:
- To adapt the GVF algorithm for robust image segmentation in video and CT sequences.
- To address the initial point problem in GVF for improved motion tracking.
Main Methods:
- Implemented motion tracking using Mean Square Error (MSE) to handle challenges in image sequences.
- Utilized the contour from the previous image frame as the initial contour for the current frame, addressing the initial point problem.
Main Results:
- The proposed method demonstrates improved segmentation of objects in image sequences.
- Successfully addressed limitations of traditional GVF in handling motion and initialization for video and CT image segmentation.
Conclusions:
- The adapted GVF algorithm provides a more effective solution for image segmentation in dynamic imaging applications.
- Leveraging previous contours as initial points significantly enhances the performance and applicability of GVF in sequence analysis.
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