The Dahu graph-cut for interactive segmentation on 2D/3D images. (April 2023)

• Record Type: Journal Article
• Authors: Ôn Vũ Ngọc, Minh; Carlinet, Edwin; Fabrizio, Jonathan; Géraud, Thierry
• Is Part Of: Pattern recognition. Volume 136(2023)
• Publication Date: 2023-04
• Subjects: Vectorial Dahu pseudo-distance -- Minimum barrier distance -- Visual saliency -- Object segmentation -- Mathematical morphology; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2022.109207
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: An efficient method to compute the Dahu pseudo-distance on multivariate images. A new interactive segmentation method (based on the Dahu pseudo-distance). This proposed interactive segmentation method is efficient on 2D images. This proposed segmentation method is usable on 3D images (tested on medical images). The use of this method is investigated to segment objects in video sequences. Abstract: Interactive image segmentation is an important application in computer vision for selecting objects of interest in images. Several interactive segmentation methods are based on distance transform algorithms. However, the most known distance transform, geodesic distance, is sensitive to noise in the image and to seed placement. Recently, the Dahu pseudo-distance, a continuous version of the minimum barrier distance (MBD), is proved to be more powerful than the geodesic distance in noisy and blurred images. This paper presents a method for combining the Dahu pseudo-distance with edge information in a graph-cut optimization framework and leveraging each's complementary strengths. Our method works efficiently on both 2D/3D images and videos. Results show that our method achieves better performance than other distance-based and graph-cut methods, thereby reducing the user's efforts.