Neutrosophic segmentation of breast lesions for dedicated breast computed tomography

Juhun Lee, Robert M. Nishikawa, Ingrid Reiser, John M Boone

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

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).

Original languageEnglish (US)
Article number014505
JournalJournal of Medical Imaging
Volume5
Issue number1
DOIs
StatePublished - Jan 1 2018

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Breast
Tomography
Area Under Curve
Noise

Keywords

  • Breast CT
  • CADx
  • Neutrosophy
  • Quantitative feature analysis
  • Segmentation

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

Cite this

Neutrosophic segmentation of breast lesions for dedicated breast computed tomography. / Lee, Juhun; Nishikawa, Robert M.; Reiser, Ingrid; Boone, John M.

In: Journal of Medical Imaging, Vol. 5, No. 1, 014505, 01.01.2018.

Research output: Contribution to journalArticle

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