An Investigation of Challenges in Automatic Segmentation of Medical Images. | [Bahria University Journal of Information & Communication Technologies • 2017]

Author(s):

1. SamiUr Rahman: Department of Computer Science and Information Technology, University of Malakand, KPK, Pakistan

2. Hussain Rahman: Department of Computer Science and Information Technology, University of Malakand, KPK, Pakistan

3. Fakhrud Din: Department of Computer Science and Information Technology, University of Malakand, KPK, Pakistan

4. Sehat Ullah: Department of Computer Science and Information Technology, University of Malakand, KPK, Pakistan

5. Aziz Uddin: Department of Computer Science, University of Peshawar, KPK, Pakistan

Abstract:

- Automatic medical image segmentation is an emerging field with upcoming new techniques that revolutionized how we view functional and pathological events in the body. Medical image segmentation is a very challenging problem and there is no standard segmentation technique that can automatically segment all types of three-dimensional medical images. Doctors and clinicians still prefer manual segmentation due to unreliability and unavailability of standard automatic segmentation techniques. Most of the segmentation algorithms are semi-automatic that require user interaction and are difficult for use in practical applications. Some of the algorithms that are automatic require very high resolution of images for segmentation. Moreover, segmentation algorithms for medical images are application specific and the algorithms developed for one application may not work for other type of application. There are a number of factors such as image noise, anatomy variation, disease type, intensity homogeneity, non-uniform object texture, image content, occlusion, input nature and special characteristics of image continuity that make the process of automatic segmentation more difficult and challenging. In this paper, we have categorized these challenges and have described their effects on commonly used segmentation algorithms using the criterion functions input type, dimensionality, anatomy variation, parameter tuning and need of user interaction.

Page(s): 6-13

DOI: DOI not available

Published: Journal: Bahria University Journal of Information & Communication Technologies, Volume: 10, Issue: 1, Year: 2017

Keywords:

Keywords are not available for this article.

References:

[1] T. Acharya and A. K. Ray, “Image Processing Principles and Applications”, John Wiley & Sons Inc. 2005.

[2] R.Saini,M.Dutta,R.Kumar, 2012.A comparative study of several image segmentation techniques,Journal of Information and Operations Management ISSN: 09767754, EISSN: 09767762 3 -

[3] A.Ahirwar, 2013.Study of Techniques used for Medical Image Segmentation and Computation of Statistical Test for Region Classification of Brain MRI,I. J. Information Technology and Computer Science 05 44 -53

[4] S. M.Larie,S. S.Abukmeil, 1998.Brain abnormality in schizophrenia: a systematic and quantitative review of volumetric magnetic resonance imaging studies,J. Psych 172 110120 -

[5] E.Isselbacher,BritishMachine, 2006.Thoracic and abdominal aortic aneurysms,In The 17th BMVC (Edinburgh, Sept 247257 18 -828

[6] S.Worz,K.Rohr, 2007.Segmentation and Quantification of Human Vessels Using a 3-D Cylindrical Intensity Model,Image Processing, IEEE Transactions on 16, 8 17 19942004 -

[7] T.Kovacs,P.Cattin,H.Alkadhi,S.Wildermuth,G., 2006.Szekely: Automatic Segmentation of the Aortic Dissection Membrane from 3D CTA Images,In Medical Imaging and Augmented Reality (MIAR) 17 34 -

[8] Flehmann,Metaxas D. , Axel,L., 2011.In Functional Imaging and Modeling of the Heart, 6666 145152 -

[9] Y.Zheng, 2010.In Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I (Berlin, -

[10] N.Sergiu, 2013.Towards generalized optimization solutions for segmentation and reconstruction in medical imaging, 55 10 -11

[11] M.Rastgarpour,Shanbehzadeh,HongKong,vol I, 2011.March 16-1,Proceedings of the International Multi Conference of Engineers and Computer Scientists -

[12] D.Rueckert,P.Burger,S. M.Forbat,R. D.Mohiaddin,G. Z.Yang, 1997.Automatic tracking of the aorta in cardiovascular MR images using deformable models,Medical Imaging 16 581 -590

[13] Y.Zheng,A.Barbu,B.Georgescu,M.Scheuering,D.Comaniciu, 2008.Four-chamber heart modeling and automatic segmentation for 3-D cardiac CT volumes using marginal space learning and steerable features,Medical Imaging 27 1668 -1681

[14] H.Rahman,S. U.Rahman,F.Din, 2017.Automatic Segmentation of the Aorta in Cardiac Medical Images,The Nucleus 54 90 -96

[15] C. S.Albert, .Image Segmentation Methods for Detecting Blood Vessels in Angiography. Medical Image Analysis Laboratory,9th Int. Conf. Control, Automation, Robotics and Vision Singapore 5 -8th

[16] V. M.Kakade, 2013.Overview on approaches of image segmentation with its algorithms and its applications,International Journal of Engineering 2 2319 -7242

[17] C. Y.Hu,M. D.Grossberg,G. S.Mageras, 2009.Survey of recent volumetric medical image segmentation techniques,Biomedical Engineering 321 -346

[18] S.Wilbert,P.Philip, 2012.Computers ranked as key literacy,The West Australian, para. 3 -

[19] A.Abdo Mohammed Al-Kubati,J. A. M.Saif,M. A.Taher, 2012.Dubai, -

[20] L.Ibanez,W.Schroeder,L.Ng,Cates, 2003.,The ITK Software Guide. (Kitware Inc.). -

[21] R.Pohle, 13371346.Segmentation of medical images using adaptive region growing,Proceedings of SPIE 4322 -

[22] L.Ling, 2006.Image Division Method Research and Realization,Journal of Suzhou College 21 85 -88

[23] O.Wink,W. J.Niessen,M. A.Viergever, 2000.Fast delineation and visualization of vessels in 3-D angiographic images,IEEE Transactions on Medical Imaging 337346 16 -

[24] W.Katz,M.Merickel, 1990.Aorta detection in magnetic resonance images using multiple artificial neural networks,Proceedings of the Twelfth Annual International Conference of the IEEE (Nov 13021303 17 -

[25] T.Deschamps,L. D.Cohen, .Fast extraction of tubular and tree 3D surfaces with front propagation methods,IEEE Computer Society 10731 112 -

[26] F.Zhao,H.Zhang,A.Wahle,T. D.Scholz,M.,Wink,Katz,Deschamps,Zhao,Kovacs,Rahman,Rueckert,Zheng, .Sonka: Automated 4D segmentation of aortic magnetic resonance Pohle et al, 25 -

Citations

Downloads

Views