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A method to detect landmark pairs accurately between intra‐patient volumetric medical images
Author(s) -
Yang Deshan,
Zhang Miao,
Chang Xiao,
Fu Yabo,
Liu Shi,
Li Harold H.,
Mutic Sasa,
Duan Ye
Publication year - 2017
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.12526
Subject(s) - artificial intelligence , landmark , scale invariant feature transform , feature (linguistics) , image registration , pattern recognition (psychology) , computer vision , computer science , matching (statistics) , blob detection , feature extraction , imaging phantom , image processing , mathematics , image (mathematics) , nuclear medicine , edge detection , medicine , philosophy , linguistics , statistics
Purposes An image processing procedure was developed in this study to detect large quantity of landmark pairs accurately in pairs of volumetric medical images. The detected landmark pairs can be used to evaluate of deformable image registration ( DIR ) methods quantitatively. Methods Landmark detection and pair matching were implemented in a Gaussian pyramid multi‐resolution scheme. A 3D scale‐invariant feature transform ( SIFT ) feature detection method and a 3D Harris–Laplacian corner detection method were employed to detect feature points, i.e., landmarks. A novel feature matching algorithm, Multi‐Resolution Inverse‐Consistent Guided Matching or MRICGM , was developed to allow accurate feature pairs matching. MRICGM performs feature matching using guidance by the feature pairs detected at the lower resolution stage and the higher confidence feature pairs already detected at the same resolution stage, while enforces inverse consistency. Results The proposed feature detection and feature pair matching algorithms were optimized to process 3D CT and MRI images. They were successfully applied between the inter‐phase abdomen 4 DCT images of three patients, between the original and the re‐scanned radiation therapy simulation CT images of two head‐neck patients, and between inter‐fractional treatment MRI s of two patients. The proposed procedure was able to successfully detect and match over 6300 feature pairs on average. The automatically detected landmark pairs were manually verified and the mismatched pairs were rejected. The automatic feature matching accuracy before manual error rejection was 99.4%. Performance of MRICGM was also evaluated using seven digital phantom datasets with known ground truth of tissue deformation. On average, 11855 feature pairs were detected per digital phantom dataset with TRE = 0.77 ± 0.72 mm. Conclusion A procedure was developed in this study to detect large number of landmark pairs accurately between two volumetric medical images. It allows a semi‐automatic way to generate the ground truth landmark datasets that allow quantitatively evaluation of DIR algorithms for radiation therapy applications.