Investigating the image features landscape for the classification of breast microcalcifications

Andreadis, Ioannis; Nikita, Konstantina S.; Antaraki, Argyro; Ligomenides, Panos A.; Spyrou, George M.

doi:10.1109/ist.2010.5548502

Cited by 1 publication

(1 citation statement)

References 16 publications

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“…Due to the great number of features used in this phase, a feature selection preprocessing is needed in order to find a satisfactory feature subset, eliminating features that may be irrelevant to the classification task. A previous work [27] has indicated the potentiality of the recursive feature elimination (RFE) selection method proposed by Guyon et al [28] for the selection of an optimal subset. The RFE method generates ranking of features during an iterative, multivariate backward feature-elimination.…”

Section: Fatty Tissuesmentioning

confidence: 99%

A comparative study of image features for classification of breast microcalcifications

Andreadis

Spyrou

Nikita

2011

Meas. Sci. Technol.

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Computer-aided diagnosis systems for mammography have been developed in order to assist radiologists in the diagnostic process by providing a reliable and objective discrimination of benign and malignant mammographic findings. The effectiveness of such systems is based on the image features extracted from mammograms, which are mainly related to the morphology, texture and optical density of the suspicious abnormality. There are many methodologies reported in the literature able to provide a mathematical description of a mammographic lesion. In this paper, we apply various feature extraction methodologies on cases containing clusters of microcalcifications. Our purpose is to compare their performance in large scale in terms of classification accuracy and to investigate their potentiality in discriminating benign from malignant clusters. Experiments were performed on 1715 cases (882 benign and 833 malignant) extracted from the Digital Database of Screening Mammography, which is the largest publicly available database of mammograms. The results of our study indicated that texture features outperformed the rest of the considered categories, while the combination of the best features optimized the classification results, leading to an area under the receiver operating characteristic curve equal to 0.82.

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Section: Fatty Tissuesmentioning

confidence: 99%