Comput. Biol. Med. - Region based stellate features combined with variable selection using AdaBoost learning in mammographic computer-aided detection.

Tópicos

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Resumo

In this paper, a new method is developed for extracting so-called region-based stellate features to correctly differentiate spiculated malignant masses from normal tissues on mammograms. In the proposed method, a given region of interest (ROI) for feature extraction is divided into three individual subregions, namely core, inner, and outer parts. The proposed region-based stellate features are then extracted to encode the different and complementary stellate pattern information by computing the statistical characteristics for each of the three different subregions. To further maximize classification performance, a novel variable selection algorithm based on AdaBoost learning is incorporated for choosing an optimal subset of variables of region-based stellate features. In particular, we develop a new variable selection metric (criteria) that effectively determines variable importance (ranking) within the conventional AdaBoost framework. Extensive and comparative experiments have been performed on the popular benchmark mammogram database (DB). Results show that our region-based stellate features (extracted from automatically segmented ROIs) considerably outperform other state-of-the-art features developed for mammographic spiculated mass detection or classification. Our results also indicate that combining region-based stellate features with the proposed variable selection strategy has an impressive effect on improving spiculated mass classification and detection.

Resumo Limpo

paper new method develop extract socal regionbas stellat featur correct differenti spicul malign mass normal tissu mammogram propos method given region interest roi featur extract divid three individu subregion name core inner outer part propos regionbas stellat featur extract encod differ complementari stellat pattern inform comput statist characterist three differ subregion maxim classif perform novel variabl select algorithm base adaboost learn incorpor choos optim subset variabl regionbas stellat featur particular develop new variabl select metric criteria effect determin variabl import rank within convent adaboost framework extens compar experi perform popular benchmark mammogram databas db result show regionbas stellat featur extract automat segment roi consider outperform stateoftheart featur develop mammograph spicul mass detect classif result also indic combin regionbas stellat featur propos variabl select strategi impress effect improv spicul mass classif detect

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