TY - GEN
T1 - Spatial Image Segmentation for Breast Cancer Detection in Terahertz Imaging
AU - Chavez, Tanny
AU - Vohra, Nagma
AU - Wu, Jingxian
AU - El-Shenawee, Magda
AU - Bailey, Keith
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/7/5
Y1 - 2020/7/5
N2 - This paper proposes a new spatial image segmentation algorithm for breast cancer detection in terahertz (THz) images of freshly excised human tumors. Region classifications of fresh tissue with 3 or more regions, such as cancer, fat, and collagen, remain a challenge for cancer detection. We propose to tackle this problem by exploiting the spatial correlation among neighboring pixels in THz images, that is, pixels that are close to each other are more likely to belong to the same region. The spatial correlation among pixels is modeled by using Markov random fields (MRF). A Gaussian mixture model (GMM) with expectation maximization (EM) is then used to represent the statistical distributions of the THz images in both the frequency and spatial domain. Experiment results demonstrated that the proposed spatial image segmentation algorithm outperforms existing algorithms that do not consider spatial information.
AB - This paper proposes a new spatial image segmentation algorithm for breast cancer detection in terahertz (THz) images of freshly excised human tumors. Region classifications of fresh tissue with 3 or more regions, such as cancer, fat, and collagen, remain a challenge for cancer detection. We propose to tackle this problem by exploiting the spatial correlation among neighboring pixels in THz images, that is, pixels that are close to each other are more likely to belong to the same region. The spatial correlation among pixels is modeled by using Markov random fields (MRF). A Gaussian mixture model (GMM) with expectation maximization (EM) is then used to represent the statistical distributions of the THz images in both the frequency and spatial domain. Experiment results demonstrated that the proposed spatial image segmentation algorithm outperforms existing algorithms that do not consider spatial information.
UR - http://www.scopus.com/inward/record.url?scp=85101620392&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85101620392&partnerID=8YFLogxK
U2 - 10.1109/IEEECONF35879.2020.9330445
DO - 10.1109/IEEECONF35879.2020.9330445
M3 - Conference contribution
AN - SCOPUS:85101620392
T3 - 2020 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, IEEECONF 2020 - Proceedings
SP - 1157
EP - 1158
BT - 2020 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, IEEECONF 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, IEEECONF 2020
Y2 - 5 July 2020 through 10 July 2020
ER -