TUTCRIS Research Portal

TY - GEN

T1 - Morphological Area Gradient: System-independent Dense Tissue Segmentation in Mammography Images

AU - Torres, German F.

AU - Sassi, Antti

AU - Arponen, Otso

AU - Holli-Helenius, Kirsi

AU - Lääperi, Anna-Leena

AU - Rinta-Kiikka, Irina

AU - Kämäräinen, Joni

AU - Pertuz, Said

N1 - INT=COMP, "Torres, German F." EXT="Pertuz, Said" DUPL=51545706

PY - 2019/10/7

Y1 - 2019/10/7

N2 - 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.

AB - 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.

KW - Mammography

KW - Estimation

KW - Calibration

KW - Image segmentation

KW - Breast cancer

U2 - 10.1109/EMBC.2019.8857320

DO - 10.1109/EMBC.2019.8857320

M3 - Conference contribution

SN - 978-1-5386-1312-2

T3 - Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

SP - 4855

EP - 4858

BT - 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

PB - IEEE

ER -