APPLICATION CHAN-VESE METHODS IN MEDICAL IMAGE SEGMENTATION
Paweł Prokop
pawel.prokop@pollub.edu.plPolitechnika Lubelska, Wydział Elektrotechniki i Informatyki (Poland)
Abstract
The article presents the problem of determining the edges of objects enclosed in a medical CT images, which will be subject to further analysis, for the purpose of medical diagnosis. The use of a transformation which introduces two-point thresholding, eliminates presenting pixels of objects for tissues that are not a subject to further analysis. This approach allowed us to sharpen the edges of objects presenting soft tissue. A way to detect the edge of the soft tissue was compared for the original image and processed one using the transformation using the method of Chan-Vese. Sharpening of edges of the image have improved the accuracy of detection of objects presenting the soft tissue.
Keywords:
thresholding, Chan-Vese, image processing, CTReferences
Brox T., Weickert J.: Level Set Segmentation with Multiple Regions. Mathematical Image Analysis Group, Faculty of Mathematics and Computer Science, Saarland University, Building 27.1, 66041 Saarbrücken, Germany, 2005.
Google Scholar
Chan T. F., Vese L. A.: Active Contours Without Edges. IEEE Transactions on image processing, col. 10, No.2, February 2001.
Google Scholar
Demirkaya O., Sahoo P. K.: Image Processing with MATLAB®; Applications in Medicine and Biology. CRC Press 2009.
Google Scholar
Maciejewski M., Surtel W., Małecka-Massalska T.: Level-set image processing methods in medical image segmentation. NTAV/SPA 2012 – New Trends in Audio and Video sSignal Processing Algorithms, Architectures, Arrangements and Applications, 27-29 September 2012, Łódź, 2012.
Google Scholar
Madhu S. N., Revathy K., Tatavarti R.: Removal of Salt-and Pepper Noise in Images: A New Decision-Based Algorithm. IMECS 2008, 19-21 March, 2008, Hong Kong, 2008.
Google Scholar
Moelich M., Chan T.: Tracking objects with the Chan-Vese algorithm. Mathematics Department, UCLA, 405 Hilgard Avenue, Los Angeles, 2003.
Google Scholar
Petrou M., Bosdogianni P.: Image Processing the Fundamentals, Wiley, London, 2004.
Google Scholar
Pratt W. K.: Digital Image Processing, Wiley-Interscience, Los Altos, California, 2007.
Google Scholar
Rumpf M., Strzodka R.: Level set segmentation in graphics hardware. University of Duisburg, Applied Mathematics, Lotharstr. 65, D-47048 Duisburg.
Google Scholar
Salman N.: Image Segmentation and Edge Detection Based on Chan-Vese Algorithm” Computer Science Department, Zarqa Private Univesity, Jordan, 2006.
Google Scholar
Tadeusiewicz R., Śmietański J.: Pozyskiwanie obrazów medycznych oraz ich pozyskiwanie, analiza, automatyczne rozpoznawanie i diagnostyczna interpretacja. WSTN, Kraków 2011.
Google Scholar
Authors
Paweł Prokoppawel.prokop@pollub.edu.pl
Politechnika Lubelska, Wydział Elektrotechniki i Informatyki Poland
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