Kernel eigenspace template matching for detection of lacunar infarcts on MR images

Nihon Hoshasen Gijutsu Gakkai zasshi
Saki MurakawaHiroshi Fujita

Abstract

Detection of lacunar infarcts is important because their presence indicates an increased risk of severe cerebral infarction and dementia. However, accurate identification of lacunar infarcts is often difficult for radiologists. Our previous computer-aided detection (CAD) scheme achieved a sensitivity of 96.8% with 0.76 false positives (FPs) per slice. However, further reduction of FPs remained an issue for the clinical application. The purpose of this study is to improve our CAD scheme by using kernel eigenspace template matching. First, we selected the regions of interest (ROIs) around the candidate regions detected in our previous method. A kernel eigenspace was then made by using kernel principal component analysis of the training data set. A test ROI was projected onto the same kernel eigenspace as the training data set. The cross-correlation coefficients between the test ROI and all the training ROIs were calculated on the kernel eigenspace. By comparing the two maxima of coefficients with a lacunar ROI and an FP ROI, the test ROI was classified. By using the proposed method, the quantity of the templates became 1.9% of that in template matching on the real space and 31. 9% of FPs could be eliminated while keeping the same...Continue Reading

References

Mar 28, 2003·The New England Journal of Medicine·Sarah E VermeerMonique M B Breteler
Oct 2, 2007·Journal of Stroke and Cerebrovascular Diseases : the Official Journal of National Stroke Association·Hirokazu BokuraKazuo Takahashi
Nov 24, 2007·Academic Radiology·Yoshikazu UchiyamaHiroshi Fujita
Feb 26, 2009·Journal of Medical Systems·Niyazi KilicOnur Osman
Jun 20, 2014·Journal of Digital Imaging·Yoshikazu UchiyamaHiroshi Fujita

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