Informatics in radiology: Hesse rendering for computer-aided visualization and analysis of anomalies at chest CT and breast MR imaging.

A volume-rendering (VR) technique known as Hesse rendering applies image-enhancement filters to three-dimensional imaging volumes and depicts the filter responses in a color-coded fashion. Unlike direct VR, which makes use of intensities, Hesse rendering operates on the basis of shape properties, such that nodular structures in the resulting renderings have different colors than do tubular structures and thus are easily visualized. The renderings are mouse-click sensitive and can be used to navigate to locations of possible anomalies in the original images. Hesse rendering is meant to complement rather than replace conventional section-by-section viewing or VR. Although it is a pure visualization technique that involves no internal segmentation or explicit object detection, Hesse rendering, like computer-aided detection, may be effective for quickly calling attention to points of interest in large stacks of images and for helping radiologists to avoid oversights.

[1]  B. Marx The Visual Display of Quantitative Information , 1985 .

[2]  Antoine Rosset,et al.  Informatics in radiology (infoRAD): navigating the fifth dimension: innovative interface for multidimensional multimodality image navigation. , 2006, Radiographics : a review publication of the Radiological Society of North America, Inc.

[3]  G. Beddoe,et al.  Efficient Computer-Aided Detection of Ground-Glass Opacity Nodules in Thoracic CT Images , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[4]  Jörg-Stefan Praßni,et al.  Shape-based transfer functions for volume visualization , 2010, 2010 IEEE Pacific Visualization Symposium (PacificVis).

[5]  T. Lindeberg,et al.  Scale-Space Theory : A Basic Tool for Analysing Structures at Different Scales , 1994 .

[6]  Jürgen Weese,et al.  Multi-scale line segmentation with automatic estimation of width, contrast and tangential direction in 2D and 3D medical images , 1997, CVRMed.

[7]  Rafael Wiemker,et al.  Fast computation of isosurface contour spectra for volume visualization , 2001, CARS.

[8]  Steven K. Feiner,et al.  Introduction to Computer Graphics Macintosh Version Software of SRGP and SPHIGS Software , 1994 .

[9]  Mathias Prokop,et al.  Pulmonary nodules: sensitivity of maximum intensity projection versus that of volume rendering of 3D multidetector CT data. , 2007, Radiology.

[10]  Bram van Ginneken,et al.  Automated detection of pulmonary nodules from low-dose computed tomography scans using a two-stage classification system based on local image features , 2007, SPIE Medical Imaging.

[11]  Gordon L. Kindlmann,et al.  Semi-Automatic Generation of Transfer Functions for Direct Volume Rendering , 1998, VVS.

[12]  Qiang Li,et al.  Selective enhancement filters for nodules, vessels, and airway walls in two- and three-dimensional CT scans. , 2003, Medical physics.

[13]  K. Doi,et al.  Current status and future potential of computer-aided diagnosis in medical imaging. , 2005, The British journal of radiology.

[14]  Ilaria Gori,et al.  Pleural nodule identification in low-dose and thin-slice lung computed tomography , 2009, Comput. Biol. Medicine.

[15]  R. Weale Vision. A Computational Investigation Into the Human Representation and Processing of Visual Information. David Marr , 1983 .

[16]  Carl-Fredrik Westin,et al.  Tissue Classification Based on 3D Local Intensity Structures for Volume Rendering , 2000, IEEE Trans. Vis. Comput. Graph..

[17]  A. Retico,et al.  Preprocessing methods for nodule detection in lung CT , 2005 .

[18]  Ross T. Whitaker,et al.  Curvature-based transfer functions for direct volume rendering: methods and applications , 2003, IEEE Visualization, 2003. VIS 2003..

[19]  Barton F. Branstetter Practical Imaging Informatics: Foundations and Applications for PACS Professionals , 2009 .

[20]  Thomas Bülow,et al.  Segmentation of suspicious lesions in dynamic contrast-enhanced breast MR images , 2007, SPIE Medical Imaging.

[21]  Rafael Wiemker,et al.  Fast detection of meaningful isosurfaces for volume data visualization , 2001, Proceedings Visualization, 2001. VIS '01..

[22]  S. Matsumoto,et al.  Computer-aided detection of lung nodules on multidetector row computed tomography using three-dimensional analysis of nodule candidates and their surroundings , 2008, Radiation Medicine.

[23]  Thomas Bülow,et al.  Filter learning and evaluation of the computer aided visualization and analysis (CAVA) paradigm for pulmonary nodules using the LIDC-IDRI database , 2010, Medical Imaging.

[24]  E. Hoffman,et al.  Lung image database consortium: developing a resource for the medical imaging research community. , 2004, Radiology.

[25]  Marleen de Bruijne,et al.  Multiscale Vessel-guided Airway Tree Segmentation , 2009 .

[26]  B. Ginneken,et al.  A comparison of six software packages for evaluation of solid lung nodules using semi-automated volumetry: What is the minimum increase in size to detect growth in repeated CT examinations , 2009, European Radiology.

[27]  Jayaram K. Udupa,et al.  A framework for evaluating image segmentation algorithms , 2006, Comput. Medical Imaging Graph..

[28]  Dev P. Chakraborty,et al.  The FROC, AFROC and DROC Variants of the ROC Analysis , 2000 .

[29]  David M. Hansell,et al.  Incidentally detected small pulmonary nodules on CT. , 2009, Clinical radiology.

[30]  H. Winer-Muram The solitary pulmonary nodule. , 2006, Radiology.

[31]  R. Truyen,et al.  Aspects of computer-aided detection (CAD) and volumetry of pulmonary nodules using multislice CT. , 2005, The British journal of radiology.

[32]  Klaus Mueller,et al.  Overview of Volume Rendering , 2005, The Visualization Handbook.

[33]  Y. Masutani,et al.  Computerized detection of colonic polyps at CT colonography on the basis of volumetric features: pilot study. , 2002, Radiology.

[34]  Barton F. Branstetter Iv Practical imaging informatics , 2010 .

[35]  M D Schnall,et al.  MR imaging of the breast for the detection, diagnosis, and staging of breast cancer. , 2001, Radiology.

[36]  Robert Allen,et al.  Handbook of Medical Imaging—Processing and Analysis , 2001 .

[37]  B. van Ginneken,et al.  Automatic lung segmentation from thoracic computed tomography scans using a hybrid approach with error detection. , 2009, Medical physics.

[38]  Charles Hansen,et al.  Multidimensional Transfer Functions for Volume Rendering , 2005, The Visualization Handbook.