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6 March 2018 Neutrosophic segmentation of breast lesions for dedicated breast computed tomography
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We proposed the neutrosophic approach for segmenting breast lesions in breast computed tomography (bCT) images. The neutrosophic set considers the nature and properties of neutrality (or indeterminacy). We considered the image noise as an indeterminate component while treating the breast lesion and other breast areas as true and false components. We iteratively smoothed and contrast-enhanced the image to reduce the noise level of the true set. We then applied one existing algorithm for bCT images, the RGI segmentation, on the resulting noise-reduced image to segment the breast lesions. We compared the segmentation performance of the proposed method (named as NS-RGI) to that of the regular RGI segmentation. We used 122 breast lesions (44 benign and 78 malignant) of 111 noncontrast enhanced bCT cases. We measured the segmentation performances of the NS-RGI and the RGI using the Dice coefficient. The average Dice values of the NS-RGI and RGI were 0.82 and 0.80, respectively, and their difference was statistically significant (p value  =  0.004). We conducted a subsequent feature analysis on the resulting segmentations. The classifier performance for the NS-RGI (AUC  =  0.80) improved over that of the RGI (AUC  =  0.69, p value  =  0.006).
© 2018 Society of Photo-Optical Instrumentation Engineers (SPIE) 2329-4302/2018/$25.00 © 2018 SPIE
Juhun Lee, Robert M. Nishikawa, Ingrid Reiser, and John M. Boone "Neutrosophic segmentation of breast lesions for dedicated breast computed tomography," Journal of Medical Imaging 5(1), 014505 (6 March 2018).
Received: 6 December 2017; Accepted: 12 February 2018; Published: 6 March 2018

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