Integrating Segmentation Methods From Different Tools Into a Visualization Program Using an Object-Based Plug-In Interface

In medical visualization, segmentation is an important step prior to rendering. However, it is also a difficult procedure because of the restrictions imposed by variations in image characteristics, human anatomy, and pathology. Moreover, what is interesting from clinical point of view is usually not only an organ or a tissue itself, but also its properties together with adjacent organs or related vessel systems that are going in and coming out. For an informative rendering, these necessitate the usage of different segmentation methods in a single application, and combining/representing the results together in a proper way. This paper describes the implementation of an interface, which can be used to plug-in and then apply a segmentation method to a medical image series. The design is based on handling each segmentation procedure as an object where all parameters of each object can be specified individually. Thus, it is possible to use different plug-ins with different interfaces and parameters for the segmentation of different tissues in the same dataset while rendering all of the results together is still possible. The design allows access to insight registration and segmentation toolkit, Java, and MATLAB functionality together, eases sharing and comparing segmentation techniques, and serves as a visual debugger for algorithm developers.

[1]  Jesus J. Caban,et al.  Rapid Development of Medical Imaging Tools with Open-Source Libraries , 2007, Journal of Digital Imaging.

[2]  Luis Ibáñez,et al.  The ITK Software Guide , 2005 .

[3]  W. Marsden.  I and J , 2012 .

[4]  Jerry L Prince,et al.  Current methods in medical image segmentation. , 2000, Annual review of biomedical engineering.

[5]  Arnold W. M. Smeulders,et al.  Interaction in the segmentation of medical images: A survey , 2001, Medical Image Anal..

[6]  M. Alper Selver,et al.  A software tool for interactive generation, representation, and systematical storage of transfer functions for 3D medical images , 2007, Comput. Methods Programs Biomed..

[7]  Rodrigo A. Vivanco,et al.  Scientific computing with Java and Cpp: a case study using functional magnetic resonance neuroimages , 2005 .

[8]  Oleg S. Pianykh,et al.  Digital Imaging and Communications in Medicine (DICOM) , 2017, Radiopaedia.org.

[9]  Karl Heinz Höhne,et al.  Interactive 3-D segmentation , 1992, Other Conferences.

[10]  Cüneyt Güzelis,et al.  Semiautomatic Transfer Function Initialization for Abdominal Visualization Using Self-Generating Hierarchical Radial Basis Function Networks , 2009, IEEE Transactions on Visualization and Computer Graphics.

[11]  Kevin Barraclough,et al.  I and i , 2001, BMJ : British Medical Journal.

[12]  David H. Laidlaw,et al.  The application visualization system: a computational environment for scientific visualization , 1989, IEEE Computer Graphics and Applications.

[13]  A. Pullan.  Biomedical Imaging. , 2004, Yearbook of medical informatics.

[14]  Sébastien Barré,et al.  Integrating segmentation methods from the Insight Toolkit into a visualization application , 2005, Medical Image Anal..

[15]  Rolf Adams,et al.  Seeded Region Growing , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Melanie Tory,et al.  Human factors in visualization research , 2004, IEEE Transactions on Visualization and Computer Graphics.

[17]  J. Sethian,et al.  A Fast Level Set Method for Propagating Interfaces , 1995 .

[18]  Rodrigo A. Vivanco,et al.  Scientific computing with Java and C++: a case study using functional magnetic resonance neuroimages , 2005, Softw. Pract. Exp..

[19]  Lutz Prechelt,et al.  An empirical comparison of C, C++, Java, Perl, Python, Rexx, and Tcl for a search/string-processing program , 2000 .

[20]  G. Everson,et al.  Adult living donor liver transplantation using a right hepatic lobe. , 1998, Transplantation.

[21]  Jerry L. Prince,et al.  A Survey of Current Methods in Medical Image Segmentation , 1999 .

[22]  Andreas Pommert,et al.  Investigation of medical 3D-rendering algorithms , 1990, IEEE Computer Graphics and Applications.

[23]  W. S. Rasband,et al.  ImageJ: Image processing and analysis in Java , 2012 .

[24]  William E. Lorensen,et al.  The visualization toolkit (2nd ed.): an object-oriented approach to 3D graphics , 1998 .

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

[26]  Robert W. Thompson,et al.  Prospects for the Medical Management of Abdominal Aortic Aneurysms , 2003, Vascular and endovascular surgery.

[27]  Jian Xue,et al.  A Novel Software Platform for Medical Image Processing and Analyzing , 2008, IEEE Transactions on Information Technology in Biomedicine.

[28]  William Schroeder,et al.  The Visualization Toolkit: An Object-Oriented Approach to 3-D Graphics , 1997 .

[29]  Heinz-Otto Peitgen,et al.  Concepts for Rapid Application Prototyping in Medical Image Analysis and Visualization , 2003, SimVis.

[30]  Jan-Martin Kuhnigk,et al.  Comparison of Four Freely Available Frameworks for Image Processing and Visualization That Use ITK , 2007, IEEE Transactions on Visualization and Computer Graphics.

[31]  James A. Sethian,et al.  Level Set Methods and Fast Marching Methods , 1999 .

[32]  C. Guzelis,et al.  Extraction of the liver vessels from the abdomen CTA images by Hessian-based vessel filter , 2009, 2009 14th National Biomedical Engineering Meeting.

[33]  Cüneyt Güzelis,et al.  Patient oriented and robust automatic liver segmentation for pre-evaluation of liver transplantation , 2008, Comput. Biol. Medicine.

[34]  Richard A. Robb,et al.  Biomedical Imaging, Visualization, and Analysis , 1999 .

[35]  Charles Hansen,et al.  The Visualization Handbook , 2011 .