A Feature-Based, Robust, Hierarchical Algorithm for Registering Pairs of Images of the Curved Human Retina

By A. Can, Charles V. Stewart, B. Roysam, H. Tanenbaum. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002

324 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/a265102e3694b9dc14927cc770a0a3d51ebc6461

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In Aerospace Engineering

In Computer Vision and Pattern Recognition

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

A Partial Intensity Invariant Feature Descriptor for Multimodal Retinal Image Registration (2010, unknown): “For instance, the use of different structures of the retina as landmark points [27], a focus on improving the performance of landmark extraction algorithm [36], a narrowing down of the search space by manually or automatically assigning “matched” points [30], and a more complicated mapping strategy ”
The dual-bootstrap iterative closest point algorithm with application to retinal image registration (2003, methodology): “More details are given in [4], [15], and [62].”
A Review on Medical Image Registration as an Optimization Problem (2017, background): “There are some examples as global-to-local matching [43], hierarchical model refinement [44], and dual-bootstrap [45].”
Image change detection algorithms: a systematic survey (2005, background): “An excellent example is registration of curved human retinal images [32], for which an affine transformation is inadequate but a 12-parameter quadratic model suffices.”
Retinal Vessel Centerline Extraction Using Multiscale Matched Filters, Confidence and Edge Measures (2006, background): “[7], image registration [8]–[14], change detection [15]–[21], pathology detection and quantification [22], tracking in video sequences [23]–[25], and computer-aided screening systems [26]– [29] depend on vessel extraction.”
Retinal Imaging and Image Analysis (2010, unknown): “Examples of feature-based registration are global-to-local matching [175], hierarchical model refinement [176], and dual-bootstrap [177].”
Deformable Medical Image Registration: A Survey (2013, methodology): “used the branching and crossover points of the blood vessel structure as feature points [276].”