Automatic detection of injuries in mammograms using image analysis techniques

Breast cancer is the most common cancer and the second cause of cancer death among women. Early detection is the key to reducing the associated mortality rate, for this identify the presence of microcalcifications is very important. This paper presents an approach for micro calcification detection i...

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Bibliographic Details
Published in:	2015 International Conference on Systems, Signals and Image Processing (IWSSIP) pp. 245 - 248
Main Authors:	Fiallos, Carlos B., Perez, Maria G., Conci, Aura, Andaluz, Victor H.
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01-09-2015
Subjects:	Breast Discrete wavelet transforms Feature extraction Image segmentation Mammographic images Mammography Microcalcification ROI Texture descriptor
Online Access:	Get full text
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Summary:	Breast cancer is the most common cancer and the second cause of cancer death among women. Early detection is the key to reducing the associated mortality rate, for this identify the presence of microcalcifications is very important. This paper presents an approach for micro calcification detection in mammography based on the following steps: noise reduction, image segmentation, extraction of the region of interest (ROI) and features that describe the possible asymmetries between the ROI of both breasts. The new aspect of our work is how we detect the microcalcifications by using wavelet decomposition. All decompositions were conducted using orthogonal wavelet filter set to computes the four filters associated with the scaling filter corresponding to a wavelet: low-pass filter and high-pass filter. Several mother families have been tested and we are confident to recommend the coiflets as the best one.
ISSN:	2157-8672
DOI:	10.1109/IWSSIP.2015.7314222