Location of mammograms ROI's and reduction of false-positive

Highlights • A scale-invariant algorithm is implemented to find image´s key descriptors. • Is proposed a method to separate breast area from pectoral-muscle to avoid regions that produces noise. • Microcalcification are detected with wavelet transform. • Wavelet performance is reinforced by high-pas...

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Bibliographic Details
Published in:Computer methods and programs in biomedicine 2017-05, Vol.143, p.97-111
Main Authors: Licea, Luis Antonio Salazar, Pedraza-Ortega, Jesús Carlos, Pastrana-Palma, Alberto, Aceves-Fernandez, Marco A
Format: Article
Language:English
Subjects:
Algorithms
Breast - diagnostic imaging
Breast Neoplasms - diagnostic imaging
Calcification
Calcinosis
Classification
Cluster Analysis
False Positive Reactions
Female
Fuzzy Logic
Humans
Image processing
Image Processing, Computer-Assisted
Internal Medicine
Mammography - methods
Models, Statistical
Muscles
Other
Pectoralis Muscles - diagnostic imaging
Predictive Value of Tests
Radiographic Image Interpretation, Computer-Assisted - methods
Reproducibility of Results
Segmentation
Sensitivity and Specificity
Wavelet
Wavelet Analysis
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Summary:Highlights • A scale-invariant algorithm is implemented to find image´s key descriptors. • Is proposed a method to separate breast area from pectoral-muscle to avoid regions that produces noise. • Microcalcification are detected with wavelet transform. • Wavelet performance is reinforced by high-pass filters and high frequency emphasis filter. • The results are presented in terms of sensitivity and false-positives per image.
ISSN:0169-2607
1872-7565
DOI:10.1016/j.cmpb.2017.02.003