Breast ultrasound image segmentation: a survey

Autor: Qiangzhi Zhang, Yaozhong Luo, Qinghua Huang
Rok vydání: 2016
Předmět:
medicine.medical_specialty
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Biomedical Engineering
Health Informatics
CAD
Breast Neoplasms
02 engineering and technology
Signal-To-Noise Ratio
030218 nuclear medicine & medical imaging
03 medical and health sciences
0302 clinical medicine
0202 electrical engineering
electronic engineering
information engineering
Medicine
Humans
Radiology
Nuclear Medicine and imaging
Segmentation
Computer vision
Breast
Diagnosis
Computer-Assisted
Breast ultrasound
Ultrasonography
Active contour model
Markov random field
medicine.diagnostic_test
business.industry
Speckle noise
General Medicine
Image segmentation
Models
Theoretical
Computer Graphics and Computer-Aided Design
Markov Chains
Computer Science Applications
Computer-aided diagnosis
020201 artificial intelligence & image processing
Surgery
Female
Computer Vision and Pattern Recognition
Artificial intelligence
Radiology
Ultrasonography
Mammary
business
Algorithms
Zdroj: International journal of computer assisted radiology and surgery. 12(3)
ISSN: 1861-6429
Popis: Breast cancer is the most common form of cancer among women worldwide. Ultrasound imaging is one of the most frequently used diagnostic tools to detect and classify abnormalities of the breast. Recently, computer-aided diagnosis (CAD) systems using ultrasound images have been developed to help radiologists to increase diagnosis accuracy. However, accurate ultrasound image segmentation remains a challenging problem due to various ultrasound artifacts. In this paper, we investigate approaches developed for breast ultrasound (BUS) image segmentation.In this paper, we reviewed the literature on the segmentation of BUS images according to the techniques adopted, especially over the past 10 years. By dividing into seven classes (i.e., thresholding-based, clustering-based, watershed-based, graph-based, active contour model, Markov random field and neural network), we have introduced corresponding techniques and representative papers accordingly.We have summarized and compared many techniques on BUS image segmentation and found that all these techniques have their own pros and cons. However, BUS image segmentation is still an open and challenging problem due to various ultrasound artifacts introduced in the process of imaging, including high speckle noise, low contrast, blurry boundaries, low signal-to-noise ratio and intensity inhomogeneity CONCLUSIONS: To the best of our knowledge, this is the first comprehensive review of the approaches developed for segmentation of BUS images. With most techniques involved, this paper will be useful and helpful for researchers working on segmentation of ultrasound images, and for BUS CAD system developers.
Databáze: OpenAIRE
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