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Feasibility Testing: Three-dimensional Tumor Mapping in Different Orientations of Automated Breast Ultrasound
- Source :
- Ultrasound in Medicine & Biology. 42:1201-1210
- Publication Year :
- 2016
- Publisher :
- Elsevier BV, 2016.
-
Abstract
- A tumor-mapping algorithm was proposed to identify the same regions in different passes of automated breast ultrasound (ABUS). A total of 53 abnormal passes with 41 biopsy-proven tumors and 13 normal passes were collected. After computer-aided tumor detection, a mapping pair was composed of a detected region in one pass and another region in another pass. Location criteria, including the radial position as on a clock, the relative distance and the distance to the nipple, were used to extract mapping pairs with close regions. Quantitative intensity, morphology, texture and location features were then combined in a classifier for further classification. The performance of the classifier achieved a mapping rate of 80.39% (41/51), with an error rate of 5.97% (4/67). The trade-offs between the mapping and error rates were evaluated, and Az = 0.9094 was obtained. The proposed tumor-mapping algorithm was capable of automatically providing location correspondence information that would be helpful in reviews of ABUS examinations.
- Subjects :
- Adult
Radial position
Pathology
medicine.medical_specialty
Acoustics and Ultrasonics
Computer science
Biophysics
Word error rate
Breast Neoplasms
Sensitivity and Specificity
Pattern Recognition, Automated
030218 nuclear medicine & medical imaging
Machine Learning
03 medical and health sciences
Imaging, Three-Dimensional
0302 clinical medicine
Image Interpretation, Computer-Assisted
medicine
Humans
Radiology, Nuclear Medicine and imaging
Breast ultrasound
Aged
Neoplasm Staging
Radiological and Ultrasound Technology
medicine.diagnostic_test
business.industry
Reproducibility of Results
Pattern recognition
Middle Aged
Image Enhancement
Computer aided detection
Tumor Burden
Tumor detection
Subtraction Technique
030220 oncology & carcinogenesis
Feasibility Studies
Female
Ultrasonography, Mammary
Artificial intelligence
One pass
business
Classifier (UML)
Algorithms
Subjects
Details
- ISSN :
- 03015629
- Volume :
- 42
- Database :
- OpenAIRE
- Journal :
- Ultrasound in Medicine & Biology
- Accession number :
- edsair.doi.dedup.....fc82c71b74751320f78652aa35fbf676