Microcalcification oriented content-based mammogram retrieval for breast cancer diagnosis

Microcalcifications (MCs) provide a significant early indication of breast malignancy. This work introduces a supervised scheme for malignancy risk assessment of mammograms containing MCs. The proposed scheme employs shape and textural features as input to a support vector machine (SVM) ensemble, in...

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Bibliographic Details
Main Authors: Tsochatzidis, Lazaros, Zagoris, Konstantinos, Savelonas, Michalis, Papamarkos, Nikos, Pratikakis, Ioannis, Arikidis, Nikolaos, Costaridou, Lena
Format: Conference Proceeding
Language:English
Subjects:
Cancer
Design automation
Feature extraction
Shape
Support vector machines
Vectors
Online Access:Request full text
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Summary:Microcalcifications (MCs) provide a significant early indication of breast malignancy. This work introduces a supervised scheme for malignancy risk assessment of mammograms containing MCs. The proposed scheme employs shape and textural features as input to a support vector machine (SVM) ensemble, in order to perform content-based image retrieval (CBIR) of mammograms. The retrieval performance of the proposed scheme has been evaluated by taking into account the variation of MCs morphology as defined in BI-RADS. In our experiments, we use a set of 87 mammograms containing MCs, obtained from the widely adopted DDSM database for screening mammography. The experimental results demonstrate that the proposed supervised CBIR scheme addresses effective retrieval of MCs mammograms outperforming relevant unsupervised schemes.
ISSN:1558-2809
2832-4242
DOI:10.1109/IST.2014.6958484