Robust Edge-Stop Functions for Edge-Based Active Contour Models in Medical Image Segmentation

Robust Edge-Stop Functions for Edge-Based Active Contour Models in Medical Image Segmentation enhances edge-based active contour segmentation by integrating gradient information with classifier-derived probability scores to improve delineation of poorly defined boundaries in medical images.

Key Features:

• Incorporation of Gradient Information: Expands traditional edge-stop functions that rely on gradient magnitudes to better handle ambiguous edge regions.
• Utilization of Probability Scores: Integrates classifier probability scores, including k-nearest neighbors (k-NN) and support vector machines (SVM), to inform edge stopping.
• Versatility Across Classifiers: Allows construction of edge-stop functions using any classification algorithm to adapt to different segmentation scenarios.
• Compatibility with Distance Regularized Level Set Method: Designed to operate with the distance regularized level set method for implementing active contour models.

Scientific Applications:

• Medical image segmentation: Improves segmentation of anatomical structures in medical images by combining gradient and classifier information.
• Delineation of poorly defined boundaries: Enhances contour evolution stopping at weak or low-gradient edges commonly found in medical imaging.
• Bioinformatics and medical image analysis: Provides a classifier-informed edge detection approach applicable to analysis tasks within bioinformatics and medical image analysis.

Methodology:

Construct multiple edge-stop functions by integrating gradient information with classifier-derived probability scores (k-NN and SVM tested); apply these functions within the distance regularized level set method; validate via experiments on medical images and compare classifier performance.