Automatic breast tissue segmentation in MRIs with morphology snake and deep denoiser training via extended Stein’s unbiased risk estimator
610
Applied Computing
Bioengineering
02 engineering and technology
s unbiased risk estimator
03 medical and health sciences
Breast cancer
0302 clinical medicine
Information and Computing Sciences
Health Services and Systems
616
Health Sciences
Breast Cancer
Health services and systems
0202 electrical engineering, electronic engineering, information engineering
Adaptive histogram equalization
Extended Stein’
Cancer
Extended Stein’s unbiased risk estimator
Image segmentation
Applied computing
Inverse Gaussian gradient
4.1 Discovery and preclinical testing of markers and technologies
Women's Health
Biomedical Imaging
Morphology snakes
MRI
DOI:
10.1007/s13755-021-00143-x
Publication Date:
2021-04-05T17:02:55Z
AUTHORS (7)
ABSTRACT
Accurate segmentation of the breast tissue is a significant challenge in the analysis of breast MR images, especially analysis of breast images with low contrast. Most of the existing methods for breast segmentation are semi-automatic and limited in their ability to achieve accurate results. This is because of difficulties in removing landmarks from noisy magnetic resonance images (MRI). Especially, when tumour is imaged for scanning, how to isolate the tumour region from chest will directly affect the accuracy for tumour to be detected. Due to low intensity levels and the close connection between breast and chest portion in MRIs, this study proposes an innovative, fully automatic and fast segmentation approach which combines histogram with inverse Gaussian gradient for morphology snakes, along with extended Stein's unbiased risk estimator (eSURE) applied for unsupervised learning of deep neural network Gaussian denoisers, aimed at accurate identification of landmarks such as chest and breast.
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CITATIONS (5)
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