RELAXED ORIENTED IMAGE FORESTING TRANSFORM FOR SEEDED IMAGE SEGMENTATION

Author(s)

Demario, Caio L. ; Miranda, Paulo A. V. ; IEEE

• Document type: Journal article
• Source: 2019 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP); v. N/A, p. 5-pg., 2019-01-01.

Abstract

In this work, we propose a hybrid method for seeded image segmentation, named Relaxed OIFT, which extends a method by Malmberg et al. to directed graphs, to properly incorporate the boundary polarity constraint. Relaxed OIFT, lies between the pure Oriented Image Foresting Transform (OIFT) at one end and the extension of Random Walks (RW) to directed graphs as proposed by Singaraju et al. Relaxed OIFT is evaluated in MR and CT medical images, producing more intuitively correct segmentation results than both OIFT and RW, and being easy to be extended to multi-dimensional images. (AU)

• FAPESP's process: 14/12236-1 - AnImaLS: Annotation of Images in Large Scale: what can machines and specialists learn from interaction?
• Grantee: Alexandre Xavier Falcão
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 16/21591-5 - Development of robust methods for edge delineation in images using graphs
• Grantee: Fábio Augusto Menocci Cappabianco
• Support Opportunities: Regular Research Grants