Segmentation of Regions of Interest Using Active Contours with SPF Function

Area of Science:

• Medical Imaging
• Computer Vision
• Image Processing

Background:

• Image segmentation is crucial for identifying regions of interest, particularly in medical imaging for detecting abnormalities like tumors.
• Traditional methods often face challenges in accurately segmenting dense or high-intensity regions.

Purpose of the Study:

• To present a novel region-based image segmentation technique using active contours with a signed pressure force (SPF) function.
• To accurately segment high-intensity or dense regions in images, applicable to medical imaging modalities.

Main Methods:

• The proposed algorithm integrates a region-based SPF function into a traditional edge-based level set model.
• It iteratively partitions images into subregions, focusing on inner regions until a stopping condition is met.
• A Gaussian kernel is employed for regularization, eliminating the need for computationally intensive reinitialization.

Main Results:

• The algorithm effectively traces and segments high-intensity or dense regions by evolving contours inwards.
• Demonstrated accuracy and effectiveness in segmenting various types of images.
• The method shows robustness in its segmentation capabilities.

Conclusions:

• The developed region-based active contour model with SPF offers an accurate and effective solution for image segmentation.
• The technique shows significant promise for applications in medical image analysis, particularly for tumor and dense tissue detection.
• The regularization approach simplifies the process and enhances computational efficiency.