Morphological Area Gradient: System-independent Dense Tissue Segmentation in Mammography Images
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
Details
| Original language | English |
|---|---|
| Title of host publication | 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) |
| Publisher | IEEE |
| Pages | 4855-4858 |
| Number of pages | 4 |
| ISBN (Electronic) | 978-1-5386-1311-5 |
| ISBN (Print) | 978-1-5386-1312-2 |
| DOIs | |
| Publication status | Published - 7 Oct 2019 |
| Publication type | A4 Article in a conference publication |
| Event | Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Duration: 1 Jan 1900 → … |
Publication series
| Name | Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) |
|---|---|
| ISSN (Print) | 1557-170X |
| ISSN (Electronic) | 1558-4615 |
Conference
| Conference | Annual International Conference of the IEEE Engineering in Medicine and Biology Society |
|---|---|
| Period | 1/01/00 → … |
Abstract
Breast density has been identified as one of the strongest risk factors for breast cancer. However, the development of reliable and reproducible methods for the automatic dense tissue segmentation has been an important challenge. Due to the complexity of the acquisition process of mammography images, current approaches need to be calibrated for specific mammographic systems or require access to raw mammograms. In this work, we introduce the Morphological Area Gradient (MAG) as a generic measure for mammography images. MAG is generic in the sense that it does not need calibration or access to raw mammograms. At the core of MAG is the derivative of the area of segmented tissue with respect to the pixel intensity. We have found that the high-density regions can be automatically segmented by minimizing the MAG of a mammogram. To verify the performance of MAG, we collected 566 full-field digital mammograms using two different medical devices and a human expert manually annotated the high-density regions in each image. The proposed MAG method yields a median absolute error of 7.6% and a Dices similarity coefficient of 0.83, which are superior to other clinically validated state-of-the-art algorithms.
Keywords
- Mammography, Estimation, Calibration, Image segmentation, Breast cancer