IIAE CONFERENCE SYSTEM, The 1st IEEE/IIAE International Conference on Intelligent Systems and Image Processing 2013 (ICISIP2013)

Font Size: 
A Review of Image Segmentation Methods
Yujie Li, Huimin Lu, Shiyuan Yang, Seiichi Serikawa, Yuhki Kitazono

Last modified: 2013-10-01

Abstract


With the rapid development of computer technology and digital medical imaging equipment, medical imaging technology has made a rapid development. Its principle is with the energy and the biology of some kind of interaction, extract information about biological tissue or organs in the body shape, structure and some physiological function, then provides for the organization of biological research and clinical diagnosis. There are x-ray imaging, ultrasonic imaging, magnetic resonance imaging, radionuclide imaging and so on. We can check body, analysis lesions with qualitative and quantitative, provide detailed and accurate information about disease without anatomical. In this paper, we proposed some segmentation methods for getting better results in medical image segmentation systems.

Keywords


review, image segmentation

References


[1].Nigel Gilbert, Researching Social Life 3rd Edition,SAGE Press, London, 2010.

[2].G. Longford, The social impact of informationtechnology on daily life, Canadian Journal of CulturalStudies, 2006.

[3].J.S. Brown, and P. Duguid, The Social Life ofInformation, Harvard Business School Press, 2002.

[4].R.C. Gonzalez, and R.E. Woods, Digital ImageProcessing, 2nd Edition, Prentice Hall, 2008.

[5].M. Kass, A. Witkin, D. Terzopoulos, Snakes: Activecontour models, International Journal of Computer Vision,vol.1, no.4 (1998), p.321.

[6].V.G. Leticia, A.A. Suzim, J. Maeda, A new automaticcircular decomposition algorithm applied to blood cellsimage, IEEE Computer Society, (2000), p.277-280.

[7].N. Sinha, A.G. Ramakrishnan, Automation ofdifferential blood count, Digital Object Identifier, vol.2,no.15-17(2003), p.547-551.

[8].Y. Cong, L. Qiuping, F. Nianlun, Microscopic imageanalysis and recognition on pathological cells, Journal ofBiomedical Engineering Research, vol.28, no.1(2009),p.35-38.

[9].H. Zhoujie, M. Shoushi, P. Xichun, P. Xin, Studies onthe recognition of marrow cell image, ComputingTechnology and Automation, vol.24, no.3 (2005).

[10].J. Debayle, and J.C. Pinoli, Multi-scale image filtering45and segmentation by menas of adaptive neighborhoodmathematical morphology, Proceeding of IEEEInternational Conference on Image Processing, Genova,Italy, vol.3(2005), p.537-540.

[11].T. Xuemin, L. Xueyin, H. Lin, Research on automaticrecognition system for leucocyte image, Journal ofBiomedical Engineering, vol.24, no.6 (2007), p.1250-1255.

[12].B. Funt, K. Bernard, L. Martin, Is machine colorconstancy good enough, Proceeding of the 5th EuropeanConference on Computer Vision, Freiburg, Germany,(1998), p.445-459.

[13].H. Lu, L. Zhang, S. Serikawa, A method for infraredimage segmentation based on sharp frequency localizedcontourlet transform and morphology, Proceeding ofInternational Conference on Intelligent Control andInformation Processing, Dalian, China, (2010), p.79-82.

[14].Y. Li, L. Zhang, H. Lu, S. Serikawa, An improveddetection algorithm based on morphology methods forblood cancer cells detection, Journal of ComputationalInformation Systems, vol.7, no.13 (2011), p.4724-4731.

[15].Y. Li, L. Zhang, H. Lu, S. Serikawa, A new type ofusing morphology methods to detect blood cancer cells,Proceeding of LNCS CCIS, Part I, vol.182(2011), p.16-23.

[16].C.W. Chen, J. Luo, and K.J. Parker, Artifact reductionin low bit rate DCT-based image compression, IEEE Trans.on Image Processing, vol.7, no.12 (1998), p.1673-1683.

[17].J. Han, M. Kamber, Data mining: Concepts andTechniques, Morgan Kaufmann Publishers, 2001.

[18].B. Otman, Z. Hongwei, K. Fakhri, Connectionist-baseddempster shafer evidential reasoning for data fusion, FuzzyBased Image Segmentation, Springer-Verlag, Berlin, 2003.

[19].S. Shen, W.A. Sandham, Fuzzy clustering basedapplications to medical image segmentation, Proceeding ofthe 25th Annual International Conference on the IEEEEMBS, Cancun, UK, (2003), p.747-750.

[20].M. Hung, D. Yang, An efficient fuzzy c-meansclustering algorithm, Proceeding of InternationalConference on Data Mining, (2001), p.225-232.

[21].J.X. Sun, Image Processing, Science Press, 2005.

[22].G. Xinbo, L. Jie, J. Hongbing, A multi-threshold imagesegmentation algorithm based on weighting fuzzy c-meansclustering and statistical test. Acta Electronic Sinica, vol.32,no.4 (2004), p.661-664.

[23].Y. Li, H. Lu, B. Li, and S. Serikawa, An automaticimage segmentation algorithm based on weighting fuzzyc-means clustering, Proceeding of LNCS CCIS, Part I,vol.182(2011), pp.24-29.

[24].L. Chen, H.D. Cheng, J. Zhang, Fuzzy subfiber and itsapplication to seismic lithology classification, informationscience, vol.1, no.2(1994), p.77-95.

[25].J. Shi, and J. Malik, Normalized cuts and imagesegmentation, IEEE Trans. on Pattern Analysis andMachine Intelligence, vol.22, no.8 (2000), p.888-905.

[26].M. Pathegama, Edge-end pixel extraction foredge-based image segmentation, Trans. on EngineeringComputing and Technology, vol.2,(2004), p.213-216.

[27].L.G. Shapiro, and G.C. Stockman, Computer Vision,Prentice Hall, 2001.

[28].S. Osher, and N. Paragios, Geometric level set methodsin imaging vision and graphics, Springer Verlag, 2003.

[29].K. Kang, Recent developments and applications ofradiation/detection technology in Tsinghua university,Nuclear Physics, vol.A-834, (2010), p.736c-742c.

[30].T.F. Chan, L. A. Vese, Active contours without edges,IEEE Trans. on Image processing, vol.10, no.2(2001),p.266-277.

[31].H. Lu, S. Serikawa, Y. Li, Proposal of fast implicitlevel set scheme for medical image segmentation using thechan and vese model, Applied Mechanics and Materials,vol.103 (2012), p.695-699.

[32].G. Kuhne, J. Weickert, Fast implicit active contourmodels, Lecture Notes in Computer Science, (2002),p.133-140.

[33].S. Qi, C. Peng, C. Jingyun, Image segmentation basedon level set method in luggage inspection system, AtomicEnergy Science and Technology, vol.40, no.6(2006),p.745-748.

[34].Y. Li, H. Lu, S. Serikawa, Image Segmentation basedon improved fast implicit level set scheme in x/γ-rayinspection system, Applied Mechanics and Materials,vol.103(2012), p.705-710.


Full Text: PDF