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Mammographical mass detection and classification using Local Seed Region Growing–Spherical Wavelet Transform (LSRG–SWT) hybrid scheme

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dc.contributor.author GÖRGEL, Pelin
dc.contributor.author SERTBAŞ, Ahmet
dc.contributor.author UÇAN, Osman Nuri
dc.date.accessioned 2014-01-24T13:58:52Z
dc.date.available 2014-01-24T13:58:52Z
dc.date.issued 2013-07-01
dc.identifier.citation Volume 43, Issue 6, 1 July 2013, Pages 765–774 en_US
dc.identifier.uri http://hdl.handle.net/11547/763
dc.description.abstract The purpose of this study is to implement accurate methods of detection and classification of benign and malignant breast masses in mammograms. Our new proposed method, which can be used as a diagnostic tool, is denoted Local Seed Region Growing–Spherical Wavelet Transform (LSRG–SWT), and consists of four steps. The first step is homomorphic filtering for enhancement, and the second is detection of the region of interests (ROIs) using a Local Seed Region Growing (LSRG) algorithm, which we developed. The third step incoporates Spherical Wavelet Transform (SWT) and feature extraction. Finally the fourth step is classification, which consists of two sequential components: the 1st classification distinguishes the ROIs as either mass or non-mass and the 2nd classification distinguishes the masses as either benign or malignant using a Support Vector Machine (SVM). The mammograms used in this study were acquired from the hospital of Istanbul University (I.U.) in Turkey and the Mammographic Image Analysis Society (MIAS). The results demonstrate that the proposed scheme LSRG–SWT achieves 96% and 93.59% accuracy in mass/non-mass classification (1st component) and benign/malignant classification (2nd component) respectively when using the I.U. database with k-fold cross validation. The system achieves 94% and 91.67% accuracy in mass/non-mass classification and benign/malignant classification respectively when using the I.U. database as a training set and the MIAS database as a test set with external validation. en_US
dc.language.iso en en_US
dc.publisher European Journal of Environmental and Civil Engineering en_US
dc.title Mammographical mass detection and classification using Local Seed Region Growing–Spherical Wavelet Transform (LSRG–SWT) hybrid scheme en_US
dc.type Article en_US


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