Abstract
Shape descriptors have been identified as important features in distinguishing malignant masses from benign masses. Thus, an effective morphological irregularity measure could provide a helpful reference to indicate the likelihood of malignancy of breast masses. In this paper, a new Fourier-Transform-based measure of irregularity—Fourier Irregularity Index (F 2), is proposed to provide reliable malignant/benign tumor/mass classification. The proposed measure has been evaluated on 418 breast masses, including 190 malignant masses and 218 benign lesions identified by radiologists on film mammograms. The results show the proposed measure has better performance than other approaches, such as Compactness Index (CI), Fractal Dimension (FD) and the Fourier-descriptor-based shape Factor (FF). Furthermore, these mentioned measures are paired to investigate the possibility of performance improvement. The results showed the combination of F 2 and CI further enhances the performance in indicating the likelihood of malignancy of breast masses.
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Notes
Our implementation is based on package PyRadbas: http://cybercase.github.io/pyradbas/
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This research was funded by the MSIP (Ministry of Science, ICT & Future Planning), Korea in the ICT R&D Program 2013.
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Zhang, G., Wang, W., Shin, S. et al. Fourier irregularity index: A new approach to measure tumor mass irregularity in breast mammogram images. Multimed Tools Appl 74, 3783–3798 (2015). https://doi.org/10.1007/s11042-013-1799-8
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DOI: https://doi.org/10.1007/s11042-013-1799-8