Automated analysis of breast tumour in the breast DCE-MR images using level set method and selective enhancement of invasive regions

Atsushi Teramoto, Satomi Miyajo, Hiroshi Fujita, Osamu Yamamuro, Kumiko Omi, Masami Nishio

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Analysis of invasive regions using breast magnetic resonance (MR) images plays an important role in diagnosis and decision-making regarding the treatment method. However, many images are obtained by MR imaging (MRI); development of an automated analysis method for breast tumours is desired. The main purpose of this study was to develop a novel method for automated analysis of the tumour region in breast MR images. First, early and late-subtraction images were obtained by subtracting early- and late-contrast-enhanced MR images, respectively, from the pre-contrast ones. Then, tumours in the images were enhanced based on the signal values of the normal mammary regions. Subsequently, using the level set method, a type of dynamic contour extraction, the outline of the tumour in the tumour-enhanced images was obtained. In order to evaluate the usefulness of the analysis method, we compared the tumour size listed in the interpretation report by a physician and analyzed the results obtained from the proposed method using clinical images from 10 cases. The mean absolute error of the size of tumours in all cases was less than 3.0 mm. These results indicate that the proposed method may be useful for the automated analysis of invasive breast tumours using breast MR images.

Original languageEnglish
Title of host publicationBreast Imaging - 13th International Workshop, IWDM 2016, Proceedings
EditorsKristina Lang, Anders Tingberg, Pontus Timberg
PublisherSpringer Verlag
Number of pages7
ISBN (Print)9783319415451
Publication statusPublished - 2016
Event13th International Workshop on Breast Imaging, IWDM 2016 - Malmo, Sweden
Duration: 19-06-201622-06-2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other13th International Workshop on Breast Imaging, IWDM 2016

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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