access icon free Comprehensive computer-aided diagnosis for breast T1-weighted DCE-MRI through quantitative dynamical features and spatio-temporal local binary patterns

Dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) is a valid complementary diagnostic method for early detection and diagnosis of breast cancer. However, due to the amount of data, the examination is difficult without the support of a computer-aided detection and diagnosis (CAD) system. Since magnetic resonance imaging data includes different tissues and patient movements (i.e. breathing) may introduce artefacts during acquisition, CADs need some stages aimed to identify breast parenchyma and to reduce motion artefacts. Among the major issues in developing a fully automated CAD, there are the accurate segmentation of lesions in regions of interest and their consequent staging (classification). This work introduces breast lesion automatic detection and diagnosis system (BLADeS), a comprehensive fully automated breast CAD aimed to support the radiologist during the patient diagnosis. The authors propose a hierarchical architecture that implements modules for breast segmentation, attenuation of motion artefacts, localisation of lesions and, finally, classification according to their malignancy. Performance was evaluated on 42 patients with histopathologically proven lesions, performing cross-validation to ensure a fair comparison. Results show that BLADeS can be successfully used to perform a fully automated breast lesion diagnosis starting from T1-weighted DCE-MRI, without requiring any operator interaction in any of the processing stages.

Inspec keywords: medical image processing; biomedical MRI; image classification; image motion analysis; cancer; biological organs; image segmentation; feature extraction; biological tissues

Other keywords: histopathologically proven lesions; breast T1-weighted DCE-MRI; patient diagnosis; dynamic contrast enhanced-magnetic resonance imaging; hierarchical architecture; diagnosis system; fully automated breast lesion diagnosis; comprehensive fully automated breast CAD; computer-aided detection; spatiotemporal local binary patterns; magnetic resonance imaging data; radiologist; breast segmentation; comprehensive computer-aided diagnosis; patient movements; breast parenchyma; breast lesion automatic detection; early detection; complementary diagnostic method; motion artefacts; biological tissues; quantitative dynamical features; breast cancer

Subjects: Biomedical magnetic resonance imaging and spectroscopy; Medical magnetic resonance imaging and spectroscopy; Image recognition; Biology and medical computing; Computer vision and image processing techniques; Patient diagnostic methods and instrumentation

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content/journals/10.1049/iet-cvi.2018.5273
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