Authors:
Sahar Nasser
;
Shashwat Pathak
;
Keshav Singhal
;
Mohit Meena
;
Nihar Gupte
;
Ananya Chinmaya
;
Prateek Garg
and
Amit Sethi
Affiliation:
Department of Electrical Engineering, Indian Institute of Technology Bombay, Mumbai, India
Keyword(s):
Image Matching, Image Registration, Kepoint Detection, Radiomic Features, Brain MRI, GNN.
Abstract:
Graph neural networks (GNNs) present a promising alternative to CNNs and transformers for certain image processing applications due to their parameter-efficiency in modeling spatial relationships. Currently, an active area of research is to convert image data into graph data as input for GNN-based models. A natural choice for graph vertices, for instance, are keypoints in images. SuperRetina is a promising semi-supervised technique for detecting keypoints in retinal images. However, its limitations lie in the dependency on a small initial set of ground truth keypoints, which is progressively expanded to detect more keypoints. We encountered difficulties in detecting a consistent set of initial keypoints in brain images using traditional keypoint detection techniques, such as SIFT and LoFTR. Therefore, we propose a new approach for detecting the initial keypoints for SuperRetina, which is based on radiomic features. We demonstrate the anatomical significance of the detected keypoints
by showcasing their efficacy in improving image registration guided by these keypoints. We also employed these keypoints as ground truth for a modified keypoint detection method known as LK-SuperRetina for improved image matching in terms of both the number of matches and their confidence scores.
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