Application of Hausdorff distance in image matching

Zhu, Li; Zhu, Chunqiang

doi:10.1109/iweca.2014.6845566

Cited by 2 publications

(1 citation statement)

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“…The Hausdorff distance is a metric measuring how far two subsets of a metric space are from each other. It presents a practical advantage of taking into account, how similar two structures are in term of distance and shape, and it is used in various areas where the problems of shape matching and comparison are addressed; especially in computer vision, the Hausdorff distance-based measure is commonly employed to match a set of point of a binary reference image with their corresponding points in a computed image (HuttenLocher et al 1993, You et al 1994, Zhu and Zhu 2014. The one-sided Hausdorff distance, h(A, B), between two closed and bounded sets of points,…”

Section: Patients Geometric Parametersmentioning

confidence: 99%

A stochastic frontier analysis for enhanced treatment quality of high-dose-rate brachytherapy plans

et al. 2019

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The purpose of the present study is to develop patient specific unbiased quality control (QC) models for high dose rate (HDR) brachytherapy plans. The proposed models are based on the stochastic frontier analysis formalism, a method of economic modeling. They act as a QC tool by predicting before the treatment planning process starts, the dosimetric coverage achievable for a HDR brachytherapy prostate plan. The geometric parameters considered in developing the models were: patient clinical target volume (CTV), organs at risk (OAR) volume, the bidirectional Hausdorff distance between CTV and OARs, and a fourth parameter measuring the catheters degree of non-parallelism within the target volume. Dosimetry parameters of interest are V100 for the CTV, V75 (bladder, rectum) and D10 (urethra). Results show that the built models can provide valuable information on the personalization of the optimization process based on the patient geometric parameters. The impact on the quality plan due to the planner’s experience variability and judgment can be reduced by using those models, since the planner will attempt to achieve dosimetric parameters predicted by the models. Furthermore, the models provide information on the better trade-off between the target volume coverage and OARs sparing that can be achieved, regardless of the planner’s experience; the latter being achieved by moving each plan at least around their respective frontier for V100, V75 and D10. The shortfall of the dosimetric parameters values computed by the treatment planning system (TPS) from those predicted by the models for a proportion of plans in the dataset reveals that optimized plans from a TPS, even clinically acceptable, are not necessarily the best that could be achieved. These represent 83% of plans in the training set for the target volume coverage (V100), ∼50% for the bladder (V75) and ∼72% for the urethra (D10).

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Section: Patients Geometric Parametersmentioning

confidence: 99%