Segmentation and classification of breast tumor using dynamic contrast-enhanced MR images.

Yuanjie Zheng, Sajjad Baloch, Sarah Englander, Mitchell D. Schnall, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceedingChapter

25 Citations (Scopus)

Abstract

Accuracy of automatic cancer diagnosis is largely determined by two factors, namely, the precision of tumor segmentation, and the suitability of extracted features for discrimination between malignancy and benignancy. In this paper, we propose a new framework for accurate characterization of tumors in contrast enhanced MR images. First, a new graph cut based segmentation algorithm is developed for refining coarse manual segmentation, which allows precise identification of tumor regions. Second, by considering serial contrast-enhanced images as a single spatio-temporal image, a spatio-temporal model of segmented tumor is constructed to extract Spatio-Temporal Enhancement Patterns (STEPs). STEPs are designed to capture not only dynamic enhancement and architectural features, but also spatial variations of pixel-wise temporal enhancement of the tumor. While temporal enhancement features are extracted through Fourier transform, the resulting STEP framework captures spatial patterns of temporal enhancement features via moment invariants and rotation invariant Gabor textures. High accuracy of the proposed framework is a direct consequence of this two pronged approach, which is validated through experiments yielding, for instance, an area of 0.97 under the ROC curve.

Original languageEnglish
Title of host publicationMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Pages393-401
Number of pages9
Volume10
EditionPt 2
Publication statusPublished - 2007 Dec 1
Externally publishedYes

Fingerprint

Breast Neoplasms
Neoplasms
Fourier Analysis
ROC Curve

ASJC Scopus subject areas

  • Medicine(all)

Cite this

Zheng, Y., Baloch, S., Englander, S., Schnall, M. D., & Shen, D. (2007). Segmentation and classification of breast tumor using dynamic contrast-enhanced MR images. In Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention (Pt 2 ed., Vol. 10, pp. 393-401)

Segmentation and classification of breast tumor using dynamic contrast-enhanced MR images. / Zheng, Yuanjie; Baloch, Sajjad; Englander, Sarah; Schnall, Mitchell D.; Shen, Dinggang.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Vol. 10 Pt 2. ed. 2007. p. 393-401.

Research output: Chapter in Book/Report/Conference proceedingChapter

Zheng, Y, Baloch, S, Englander, S, Schnall, MD & Shen, D 2007, Segmentation and classification of breast tumor using dynamic contrast-enhanced MR images. in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Pt 2 edn, vol. 10, pp. 393-401.
Zheng Y, Baloch S, Englander S, Schnall MD, Shen D. Segmentation and classification of breast tumor using dynamic contrast-enhanced MR images. In Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Pt 2 ed. Vol. 10. 2007. p. 393-401
Zheng, Yuanjie ; Baloch, Sajjad ; Englander, Sarah ; Schnall, Mitchell D. ; Shen, Dinggang. / Segmentation and classification of breast tumor using dynamic contrast-enhanced MR images. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Vol. 10 Pt 2. ed. 2007. pp. 393-401
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