Interior point search for nonparametric image segmentation

TL;DR

This paper introduces a nonparametric snake model using interior point search for automatic, robust image segmentation that outperforms traditional models in accuracy and efficiency without requiring user initialization or pre-processing.

Contribution

The paper presents a novel nonparametric snake model employing interior point search, eliminating the need for user initialization and pre-processing, and demonstrating superior robustness and accuracy.

Findings

Better segmentation results across various image types.

More robust to noise and real-world images.

No user interaction needed for initialization.

Abstract

Precise object boundary detection for automatic image segmentation is critical for image analysis, including that used in computer-aided diagnosis. However, such detection traditionally uses active contour or snake models requiring accurate initialization and parameter optimization. Identifying optimal parameter values requires time-consuming multiple runs and provides results that vary by user expertise, limiting the use of these models in high-throughput or real-time situations. Thus, we developed a nonparametric snake model using an interior point search method applied in iterations to find and improve the set of snake points forming the edge of a shape. At each iteration, one or more snake points are replaced by others in the edge map. We validated the model using binary and continuous edge images of single and multiple objects, and noisy and real images, comparing the results to those obtained using traditional snake models. The proposed model not only provides better results on all image types tested but is more robust than traditional snake models. Unlike traditional snake models, the proposed model requires no user interaction for initializing snakes and no pre-processing of noisy images. Thus, our method offers robust automatic image segmentation that is simpler to use and less time-consuming than traditional snake models