3D Shape-Weighted Level Set Method for Breast MRI 3D Tumor Segmentation

Autor: Sheng-Chih Yang, Chuin-Mu Wang, Chieh-Ling Huang
Jazyk: angličtina
Rok vydání: 2018
Předmět:
Level set method
lcsh:Medical technology
Article Subject
Computer science
Surface Properties
Biomedical Engineering
Health Informatics
Image processing
Breast Neoplasms
02 engineering and technology
030218 nuclear medicine & medical imaging
03 medical and health sciences
0302 clinical medicine
Imaging
Three-Dimensional
0202 electrical engineering
electronic engineering
information engineering
medicine
Image Processing
Computer-Assisted
Breast MRI
Humans
Mass Screening
Segmentation
False Positive Reactions
Breast
Medical diagnosis
Mass screening
Early Detection of Cancer
lcsh:R5-920
medicine.diagnostic_test
business.industry
Reproducibility of Results
Pattern recognition
Image segmentation
Magnetic Resonance Imaging
Rate of convergence
lcsh:R855-855.5
020201 artificial intelligence & image processing
Surgery
Female
Artificial intelligence
business
lcsh:Medicine (General)
Algorithms
Biotechnology
Research Article
Zdroj: Journal of Healthcare Engineering, Vol 2018 (2018)
Journal of Healthcare Engineering
ISSN: 2040-2309
2040-2295
Popis: Three-dimensional (3D) medical image segmentation is used to segment the target (a lesion or an organ) in 3D medical images. Through this process, 3D target information is obtained; hence, this technology is an important auxiliary tool for medical diagnosis. Although some methods have proved to be successful for two-dimensional (2D) image segmentation, their direct use in the 3D case has been unsatisfactory. To obtain more precise tumor segmentation results from 3D MR images, in this paper, we propose a method known as the 3D shape-weighted level set method (3D-SLSM). The proposed method first converts the LSM, which is superior with respect to 2D image segmentation, into a 3D algorithm that is suitable for overall calculations in 3D image models, and which improves the efficiency and accuracy of calculations. A 3D shape-weighted value is then added for each 3D-SLSM iterative process according to the changes in volume. Besides increasing the convergence rate and eliminating background noise, this shape-weighted value also brings the segmented contour closer to the actual tumor margins. To perform a quantitative analysis of 3D-SLSM and to examine its feasibility in clinical applications, we have divided our experiments into computer-simulated sequence images and actual breast MRI cases. Subsequently, we simultaneously compared various existing 3D segmentation methods. The experimental results demonstrated that 3D-SLSM exhibited precise segmentation results for both types of experimental images. In addition, 3D-SLSM showed better results for quantitative data compared with existing 3D segmentation methods.
Databáze: OpenAIRE
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