Robust point matching method for multimodal retinal image registration. (May 2015)

• Record Type: Journal Article
• Authors: Wang, Gang; Wang, Zhicheng; Chen, Yufei; Zhao, Weidong
• Is Part Of: Biomedical signal processing and control. Volume 19(2015)
• Page Start: 68
• Page End: 76
• Publication Date: 2015-05
• Subjects: Image registration -- Multimodal retinal image -- Robust point matching -- PIIFD -- SURF; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2015.03.004
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract : Highlights: An improved registration framework for multimodal retinal images. Using SURF–PIIFD approach to detect and describe local features. A single Gaussian robust point matching model for outliers removing. Abstract: In this paper, motivated by the problem of multimodal retinal image registration, we introduce and improve the robust registration framework based on partial intensity invariant feature descriptor (PIIFD), then present a registration framework based on speed up robust feature (SURF) detector, PIIFD and robust point matching, called SURF–PIIFD–RPM. Existing retinal image registration algorithms are unadaptable to any case, such as complex multimodal images, poor quality, and nonvascular images. Harris-PIIFD framework usually fails in correctly aligning color retinal images with other modalities when faced large content changes. Our proposed registration framework mainly solves the problem robustly. Firstly, SURF detector is useful to extract more repeatable and scale-invariant interest points than Harris. Secondly, a single Gaussian robust point matching model is based on the kernel method of reproducing kernel Hilbert space to estimate mapping function in the presence of outliers. Most importantly, our improved registration framework performs well even when confronted a large number of outliers in the initial correspondence set. Finally, multiple experiments on our 142 multimodal retinal image pairs demonstrate that our SURF–PIIFD–RPM outperforms existing algorithms, and it is quite robust to outliers.