False positive reduction in CADe using diffusing scale space

Janan, Faraz and Brady, Michael and Highnam, Ralph (2014) False positive reduction in CADe using diffusing scale space. Lecture Notes in Computer Science, 8539 . pp. 597-605. ISSN 0302-9743

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Abstract

Segmentation is typically the first step in computer-aided-detection (CADe). The second step is false positive reduction which usually involves computing a large number of features with thresholds set by training over excessive data set. The number of false positives can, in principle, be reduced by extensive noise removal and other forms of image enhancement prior to segmentation. However, this can drastically affect the true positive results and their boundaries. We present a post-segmentation method to reduce the number of false positives by using a diffusion scale space. The method is illustrated using Integral Invariant scale space, though this is not a requirement. It is quite general, does not require any prior information, is fast and easy to compute, and gives very encouraging results. Experiments are performed both on intensity mammograms as well as on Volpara® density maps.

Additional Information:Breast Imaging 12th International Workshop, IWDM 2014, Gifu City, Japan, June 29 – July 2, 2014. Proceedings
Keywords:mammographic, density maps, Integral Invariants, scale space, Fast Marching Algorithm, False positive reduction, mammograms, bmjtype, NotOAChecked
Subjects:G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Science > School of Computer Science
ID Code:25887
Deposited On:09 Mar 2017 16:54

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