A Random Point Initialization Approach to Image Segmentation with Variational Level-sets

TL;DR

This paper introduces a novel random point initialization technique for variational level-set image segmentation, improving boundary detection speed and robustness compared to traditional methods, especially in noisy images.

Contribution

The paper proposes a new random point initialization approach to enhance variational level-set segmentation, addressing noise sensitivity and boundary detection limitations.

Findings

Outperforms classical level set methods in noisy conditions

Achieves faster boundary detection with random initialization

Demonstrates comparable or improved accuracy over Canny method

Abstract

Image segmentation is an essential component in many image processing and computer vision tasks. The primary goal of image segmentation is to simplify an image for easier analysis, and there are two broad approaches for achieving this: edge based methods, which extract the boundaries of specific known objects, and region based methods, which partition the image into regions that are statistically homogeneous. One of the more prominent edge finding methods, known as the level set method, evolves a zero-level contour in the image plane with gradient descent until the contour has converged to the object boundaries. While the classical level set method and its variants have proved successful in segmenting real images, they are susceptible to becoming stuck in noisy regions of the image plane without a priori knowledge of the image and they are unable to provide details beyond object outer boundary locations. We propose a modification to the variational level set image segmentation method that can quickly detect object boundaries by making use of random point initialization. We demonstrate the efficacy of our approach by comparing the performance of our method on real images to that of the prominent Canny Method.