Image-guided multi-modality registration and visualization for breast cancer detection

James Qingyang Zhang; John M. Sullivan Jr.; Hongliang Yu; Ziji Wu

doi:10.1117/12.595517

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12 April 2005 Image-guided multi-modality registration and visualization for breast cancer detection

James Qingyang Zhang, John M. Sullivan Jr., Hongliang Yu, Ziji Wu

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Proceedings Volume 5744, Medical Imaging 2005: Visualization, Image-Guided Procedures, and Display; (2005) https://doi.org/10.1117/12.595517
Event: Medical Imaging, 2005, San Diego, California, United States

Abstract

It is crucial that breast cancer be detected in its earlier and more curable stages of development. New imaging modalities are emerging, such as electrical impedance spectroscopy (EIS), microwave imaging and spectroscopy (MIS), magnetic resonance elastography (MRE), and near-infrared (NIR) imaging. These alternative imaging modalities strive to alleviate limitations of traditional screening and diagnostic tools on dense breast tissue and detection of small abnormalities. The purpose of this study is to combine the results from alternative imaging modalities with T1 and T2-weighted MR Imaging. Two categories of data are presented, pixel data (MRIs) and geometry model with scalar values (MRE and MIS). Three dimensional mesh models (surface/volume meshes) are generated using the automatic mesh generator for biological models developed in the laboratory. A graphic user interface (GUI) for medical image processing powered by Visualization Toolkit (VTK) was developed which supports interactive and automatic image registration, image volume manipulation and geometry rendering. Registration of image/image and image/geometry is a fundamental requirement for multi-spectral data visualization within the same workspace. Various physical properties can be visualized to reveal the correlations between alternative imaging modalities and subsequently for breast tissue classification. A registration strategy was implemented using T1 and T2-weighted MR data as the standard subject. It combined automated image registration (AIR) with interactive registration routines. The final synthetic datasets are rendered in 3D views. This framework was created for multi-modality breast imaging data registration and visualization. The aligned image/geometry data facilitate breast tissue classification.

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James Qingyang Zhang, John M. Sullivan Jr., Hongliang Yu, and Ziji Wu "Image-guided multi-modality registration and visualization for breast cancer detection", Proc. SPIE 5744, Medical Imaging 2005: Visualization, Image-Guided Procedures, and Display, (12 April 2005); https://doi.org/10.1117/12.595517

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