Article

A novel registration method for retinal images based on local features

Institute of Automation, Chinese Academy of Science, Beijing, China.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 02/2008; 2008:2242-5. DOI: 10.1109/IEMBS.2008.4649642
Source: PubMed

ABSTRACT

Sometimes it is very hard to automatically detect the bifurcations of vascular network in retinal images so that the general feature based registration methods will fail to register two images. In order to solve this problem, we developed a novel local feature based retinal image registration method. We first detect the corner points instead of bifurcations since corner points are sufficient and uniformly distributed in the overlaps. Second, a novel highly distinctive local feature is extracted around each corner point. These local features are invariant to rotation and contrast, and partially invariant to scaling. Third, a bilateral matching technique is applied to identify the corresponding features between two images. Finally a second order polynomial transformation is used to register two images. Experimental results show that our method is very robust and compute efficient to register retinal images even of very low quality.

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Available from: Roland Theodore Smith, Jul 10, 2015
    • "The registration techniques have been developed in the past decades for increasing the camera's field of view and acquiring adequate details by stitching together more images. The basis for this approach was the alignment of images set of thin serial sections using image registration (Huang and Cooper, 2006; Chen et al., 2008; Brown and Lowe, 2007; Ma et al., 2015b). The examples of SRBSDV (Southern rice black-streaked dwarf virus)virus images are shown in Fig. 1. "
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    • "Nonetheless, the main deficiency is its low distinctiveness due to the reduced dimension of SIFT. In order to achieve higher distinctiveness , the partial intensity invariant feature descriptor (PIIFD) [5] is introduced. Similar to SIFT constituting of a 128-dimensional vector and having some common characteristics [6], PIIFD combines constrained gradient orientations between 0 to í µí¼‹ linearly, and performs a rotation to address the multimodal problem of gradient orientations of corresponding points in opposite directions. "
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    • "Feature based techniques [18] [4] [10] involve the detection of landmark points in retinal vascular network and the extraction of features representing the landmark points, followed by the application of a match metric to identify the correspondences between two images. Most of the feature based methods use bifurcation points as landmarks since they are a remarkable indicator of vasculature, but some of them use also other control points such as Harris corners [5]. "
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