Analysis of breast CT lesions using computer-aided diagnosis: an application of neural networks on extracted morphologic and texture features

Shonket Ray; Nicolas D. Prionas; Karen K. Lindfors; John M. Boone

doi:10.1117/12.910982

23 February 2012 Analysis of breast CT lesions using computer-aided diagnosis: an application of neural networks on extracted morphologic and texture features

Shonket Ray, Nicolas D. Prionas, Karen K. Lindfors, John M. Boone

Author Affiliations +

Proceedings Volume 8315, Medical Imaging 2012: Computer-Aided Diagnosis; 83152E (2012) https://doi.org/10.1117/12.910982
Event: SPIE Medical Imaging, 2012, San Diego, California, United States

Abstract

Dedicated cone-beam breast CT (bCT) scanners have been developed as a potential alternative imaging modality to conventional X-ray mammography in breast cancer diagnosis. As with other modalities, quantitative imaging (QI) analysis can potentially be utilized as a tool to extract useful numeric information concerning diagnosed lesions from high quality 3D tomographic data sets. In this work, preliminary QI analysis was done by designing and implementing a computer-aided diagnosis (CADx) system consisting of image preprocessing, object(s) of interest (i.e. masses, microcalcifications) segmentation, structural analysis of the segmented object(s), and finally classification into benign or malignant disease. Image sets were acquired from bCT patient scans with diagnosed lesions. Iterative watershed segmentation (IWS), a hybridization of the watershed method using observer-set markers and a gradient vector flow (GVF) approach, was used as the lesion segmentation method in 3D. Eight morphologic parameters and six texture features based on gray level co-occurrence matrix (GLCM) calculations were obtained per segmented lesion and combined into multi-dimensional feature input data vectors. Artificial neural network (ANN) classifiers were used by performing cross validation and network parameter optimization to maximize area under the curve (AUC) values of the resulting receiver-operating characteristic (ROC) curves. Within these ANNs, biopsy-proven diagnoses of malignant and benign lesions were recorded as target data while the feature vectors were saved as raw input data. With the image data separated into post-contrast (n = 55) and pre-contrast sets (n = 39), a maximum AUC of 0.70 ± 0.02 and 0.80 ± 0.02 were achieved, respectively, for each data set after ANN application.

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Shonket Ray, Nicolas D. Prionas, Karen K. Lindfors, and John M. Boone "Analysis of breast CT lesions using computer-aided diagnosis: an application of neural networks on extracted morphologic and texture features", Proc. SPIE 8315, Medical Imaging 2012: Computer-Aided Diagnosis, 83152E (23 February 2012); https://doi.org/10.1117/12.910982

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