A Novel Image Segmentation Enhancement Technique based on Active Contour and Topological Alignments

TL;DR

This paper introduces a new image segmentation method combining active contours and topological alignments to improve boundary detection, especially in low contrast images, by leveraging the strengths of both techniques.

Contribution

The paper presents a novel algorithm that integrates topological alignments with active contour models to enhance image segmentation accuracy and robustness.

Findings

Improved segmentation accuracy in low contrast images

Enhanced boundary detection for complex shapes

Better handling of over- and under-segmentation issues

Abstract

Topological alignments and snakes are used in image processing, particularly in locating object boundaries. Both of them have their own advantages and limitations. To improve the overall image boundary detection system, we focused on developing a novel algorithm for image processing. The algorithm we propose to develop will based on the active contour method in conjunction with topological alignments method to enhance the image detection approach. The algorithm presents novel technique to incorporate the advantages of both Topological Alignments and snakes. Where the initial segmentation by Topological Alignments is firstly transformed into the input of the snake model and begins its evolvement to the interested object boundary. The results show that the algorithm can deal with low contrast images and shape cells, demonstrate the segmentation accuracy under weak image boundaries, which responsible for lacking accuracy in image detecting techniques. We have achieved better segmentation and boundary detecting for the image, also the ability of the system to improve the low contrast and deal with over and under segmentation.