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Proceedings Paper

The edge-driven dual-bootstrap iterative closest point algorithm for multimodal retinal image registration
Author(s): Chia-Ling Tsai; Chun-Yi Li; Gehua Yang
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Paper Abstract

Red-free (RF) fundus retinal images and fluorescein angiogram (FA) sequence are often captured from an eye for diagnosis and treatment of abnormalities of the retina. With the aid of multimodal image registration, physicians can combine information to make accurate surgical planning and quantitative judgment of the progression of a disease. The goal of our work is to jointly align the RF images with the FA sequence of the same eye in a common reference space. Our work is inspired by Generalized Dual-Bootstrap Iterative Closest Point (GDB-ICP), which is a fully-automatic, feature-based method using structural similarity. GDB-ICP rank-orders Lowe keypoint matches and refines the transformation computed from each keypoint match in succession. Albeit GDB-ICP has been shown robust to image pairs with illumination difference, the performance is not satisfactory for multimodal and some FA pairs which exhibit substantial non-linear illumination changes. Our algorithm, named Edge-Driven DBICP, modifies generation of keypoint matches for initialization by extracting the Lowe keypoints from the gradient magnitude image, and enriching the keypoint descriptor with global-shape context using the edge points. Our dataset consists of 61 randomly selected pathological sequences, each on average having two RF and 13 FA images. There are total of 4985 image pairs, out of which 1323 are multimodal pairs. Edge-Driven DBICP successfully registered 93% of all pairs, and 82% multimodal pairs, whereas GDB-ICP registered 80% and 40%, respectively. Regarding registration of the whole image sequence in a common reference space, Edge-Driven DBICP succeeded in 60 sequences, which is 26% improvement over GDB-ICP.

Paper Details

Date Published: 17 March 2008
PDF: 9 pages
Proc. SPIE 6915, Medical Imaging 2008: Computer-Aided Diagnosis, 691533 (17 March 2008); doi: 10.1117/12.770840
Show Author Affiliations
Chia-Ling Tsai, National Chung-Cheng Univ. (Taiwan)
Chun-Yi Li, National Chung-Cheng Univ. (Taiwan)
Gehua Yang, Rensselaer Polytechnic Institute (United States)

Published in SPIE Proceedings Vol. 6915:
Medical Imaging 2008: Computer-Aided Diagnosis
Maryellen L. Giger; Nico Karssemeijer, Editor(s)

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