Authors:
Malihe Javidi
1
;
2
;
Qiang Wang
3
and
Marta Vallejo
4
Affiliations:
1
Heriot-Watt University, Edinburgh, U.K.
;
2
Quchan University of Technology, Iran
;
3
Centre for Inflammation Research, University of Edinburgh, Edinburgh, U.K.
;
4
School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh, U.K.
Keyword(s):
Autofluorescence Intensity Images, Histology Images, Co-Registration, Template Matching, Kullback Leibler Divergence, Misfit-Percent.
Abstract:
Fluorescence lifetime imaging microscopy utilises lifetime contrast to effectively discriminate between healthy and cancerous tissues. The co-registration of autofluorescence images with the gold standard, histology images, is essential for a thorough understanding and clinical diagnosis. As a preliminary step of co-registration, since histology images are whole-slide images covering the entire tissue, the histology patch corresponding to the autofluorescence image must be located using a template matching method. A significant difficulty in a template matching framework is distinguishing correct matching results from incorrect ones. This is extremely challenging due to the different nature of both images. To address this issue, we provide fully experimental results for quantifying template matching outcomes via a diverse set of metrics. Our research demonstrates that the Kullback Leibler divergence and misfit-percent are the most appropriate metrics for assessing the accuracy of our
matching results. This finding is further supported by statistical analysis utilising the t-test.
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