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Medical Images Segmentation

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Creating New Medical Ontologies for Image Annotation

Part of the book series: SpringerBriefs in Electrical and Computer Engineering ((BRIEFSELECTRIC))

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Abstract

This chapter presents a new and efficient unsupervised color segmentation scheme named GBOD to detect visual objects from medical color images and to extract their color and geometric features, in order to determine later the contours of the visual objects and to perform syntactic analysis. The presented method is a general-purpose segmentation algorithm, and it produces good results from two different perspectives: (a) from the perspective of perceptual grouping of regions from the natural images (standard RGB) and also (b) from the perspective of determining regions if the input images contain salient visual objects. We present a unified framework for image segmentation and contour extraction that uses a virtual hexagonal structure defined on the set of the image pixels. This proposed graph-based segmentation method is divided into two different steps: (a) a presegmentation step that produces a maximum spanning tree of the connected components of the triangular grid graph constructed on the hexagonal structure of the input image and (b) the final segmentation step that produces a minimum spanning tree of the connected components, representing the visual objects, by using dynamic weights based on the geometric features of the regions. The presegmentation step uses only color information extracted from the input image, whereas the final segmentation step uses both color and geometric and spatial configuration of image regions. Despite the majority of the segmentation methods, our method does not require any parameter to be chosen or tuned in order to produce a better segmentation, and thus our method is totally adaptive. The entire approach is fully unsupervised and does not need a priori information about the image scene. The new proposed algorithm is compared with other two well-known segmentation algorithms: the color set back-projection algorithm and the local variation algorithm. In order to evaluate these segmentation algorithms, we used error-measuring methods that quantify the consistency between them. The evaluation is made on a database with color medical images representing pathologies of the digestive tract.

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Referencess

  1. Alatan, A., Onural, L., Wollborn, M., Mech, R., Tuncel, E., Sikora, T.: Image sequence analysis for emerging interactive multimedia services-the european cost 211 framework. IEEE Transactions on Circuits and Systems for Video Technology, 8(7), 800–813 (1998)

    Article  Google Scholar 

  2. Carson, C., Belongie, S., Greenspan, H., J, J.M.: Blobworld: Color- and texture-based image segmentation using em and its application to image querying and classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(8), 26–37, (2002)

    Article  Google Scholar 

  3. Mezaris, V., Kompatsiaris, I., Strintzis, M.: Still image segmentation tools for object-based multimedia applications. International Journal of Pattern Recognition and Artificial Intelligence, 18(4), 700–725 (2004)

    Article  Google Scholar 

  4. Fauqueur, J., Boujemaa, N.: Region-based image retrieval: Fast coarse segmentation and fine color description. Journal of Visual Languages and Computing, 15(1), 60–95 (2004)

    Article  Google Scholar 

  5. Muller H., Michoux N., Bandon D., Geissbuhler A.: A Review of Content-based Image Retrieval Systems in Medical Application - Clinical Benefits and Future Directions, Int J Med Inform (2004)

    Google Scholar 

  6. Pham D.L., Xu C., Prince J.L.: Current methods in medical image segmentation, Annual Review of Biomedical Engineering, 2, 315-337 (2000)

    Article  Google Scholar 

  7. Stanescu L., Burdescu D., Brezovan M.: Chapter Book: Multimedia Medical Databases. In: Sidhu, Amandeep S.; Dillon, Tharam; Bellgard, Matthew (Eds.) Biomedical Data and Applications, Series: Studies in Computational Intelligence, 224 (2009)

    Google Scholar 

  8. Jiang C., Zhang X., Christoph M.: Hybrid framework for medical image segmentation, Lecture Notes in Computer Science, 3691, 264-271 (2005)

    Article  Google Scholar 

  9. Belongie S., Carson C., Greenspan H., Malik J.: Color and Texture-based Image Segmentation Using the Expectation-Maximization Algorithm and Its Application to Content-Based Image Retrieval, ICCV, 675 - 682 (1998)

    Google Scholar 

  10. Carson C., Belongie S., Greenspan H., Malik J.: Blobworld: Image segmentation using expectation-maximization and its application to image querying, IEEE Trans. Pattern Analysis and Machine Intelligence, 24(8), 1026-1038 (2002)

    Article  Google Scholar 

  11. Ballerini L.: Medical image segmentation using Genetic snakes, Applications and science of neural networks, fuzzy systems, and evolutionary computation II, Denver Co (1999)

    Google Scholar 

  12. Ghebreab S., Smeulders A.W.M.: Medical Images Segmentation by Strings, Proceedings of the VISIM Workshop: Information Retrieval and Exploration in Large Collections of Medical Images (2001)

    Google Scholar 

  13. Ghebreab S., Smeulders A.W.M.: An approximately complete string representation of local object boundary features for concept-based image retrieval, IEEE International Symposium on Biomedical Imaging (2004)

    Google Scholar 

  14. Gordon S., Zimmerman G., Greenspan H.: Image segmentation of uterine cervix images for indexing in PACS, Proceedings of the 17th IEEE Symposium on Computer-Based Medical Systems, CBMS (2004)

    Google Scholar 

  15. Lamecker H., Lange T., Seeba M., Eulenstein S., Westerhoff M., Hege H.C.: Automatic Segmentation of the Liver for Preoperative Planning of Resections, Proceedings of Medicine Meets Virtual Reality, Studies in Health Technologies and Informatics, 94, 171-174 (2003)

    Google Scholar 

  16. Muller H., Marquis S., Cohen G., Poletti P.A., Lovis C.L., Geissbuhler A.: Automatic abnormal region detection in lung CT images for visual retrieval, Swiss Medical Informatics, 57, 2-6 (2005)

    Google Scholar 

  17. Felzenszwalb, P., Huttenlocher, W. : Efficient graph-based image segmentation. International Journal of Computer Vision, 59(2), 167–181 (2004)

    Article  Google Scholar 

  18. Guigues, L., Herve, L., Cocquerez, L.P.: The hierarchy of the cocoons of a graph and its application to image segmentation. Pattern Recognition Letters, 24(8), 1059–1066 (2003).

    Article  MATH  Google Scholar 

  19. Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8), 885–905 (2000)

    Google Scholar 

  20. Wu, Z., Leahy, R.: An optimal graph theoretic approach to data clustering: theory and its application to image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(11), 1101–1113 (1993)

    Article  Google Scholar 

  21. Zahn, C.: Graph-theoretical methods for detecting and describing gestal clusters. IEEE Transactions on Computers, 20(1), 68–86 (1971)

    Article  MATH  Google Scholar 

  22. Urquhar, R.: Graph theoretical clustering based on limited neighborhood sets. Pattern Recognition, 15(3), 173–187 (1982).

    Article  Google Scholar 

  23. Gdalyahu, Y., Weinshall, D., Werman, M.: Self-organization in vision: stochastic clustering for image segmentation, perceptual grouping, and image database organization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(10), 1053–1074. (2001)

    Article  Google Scholar 

  24. Jermyn, I., Ishikawa, H.: Globally optimal regions and boundaries as minimum ratio weight cycles. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(8), 1075–1088 (2001)

    Article  Google Scholar 

  25. Cooper, M.: The tractibility of segmentation and scene analysis. International Journal of Computer Vision, 30(1), 27–42 (1998)

    Article  Google Scholar 

  26. Pavlidis, T.: Structural Pattern Recognition. New York: Springer Verlag (1977)

    MATH  Google Scholar 

  27. Malik, J., Belongie, S., Leung, T., Shi, J.: Contour and texture analysis for image segmentation. International Journal of Computer Vision, 43(1), 7–27(2001)

    Article  MATH  Google Scholar 

  28. Comaniciu, D., Meer, P.: Robust analysis of feature spaces: color image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(5), 603–619 (2002)

    Article  Google Scholar 

  29. Jain, A., Dubes, R.: Algorithms for clustering data. Englewood, NJ.: Prentice Hall (1988)

    MATH  Google Scholar 

  30. Comaniciu, D., Meer, P.: Mean shift analysis and applications. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Madison, Wisconsin, 1197–1203 (1999)

    Google Scholar 

  31. Garrido, L., Salembier, P.: Region based analysis of video sequences with a general merging algorithm. Proceedings of the European Signal Processing Conference (EUSIPCO), Rhodes, Greece, 1697–1700 (1998)

    Google Scholar 

  32. Bennstrom, C., Casas, J.: Binary-partition-tree creation using a quasi-inclusion criterion. In Proceedings of the Eighth International Conference on Information Visualization, London, UK, pp. 259–294 (2004)

    Google Scholar 

  33. Huang, T.-H., Cheng, K.-Y., Chuang, Y.-Y.: A collaborative benchmark for region of interest detection algorithms. In Proceedings of the International Conference on Computer Vision and Pattern Recognition, Miami Beach, USA,  296–303 (2009)

    Google Scholar 

  34. Achanta, R.: Frequency-tuned Salient Region Detection - Ground Truth Used for Comparison (2009)  http://ivrg.epfl.ch/supplementary_material/RK_. Acessed 26 July 2011

  35. Cao, G., Cheikh, F.-A.: Salient Region Detection with Opponent Color Boosting. Processings of the European Workshop on Visual Information, Paris, France, 13–18 (2010)

    Google Scholar 

  36. Donoser, M., Bischof, H. ROI-SEG: Unsupervised Color Segmentation by Combining Differently Focused Sub Results. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, SUA, 1–8 (2007).

    Google Scholar 

  37. Liu, T., Sun, J., Zheng, N.-N., Tang, X., Shum, H.-Y.: Learning to Detect A Salient Object. Proceedings of the IEEE International Conference on Computer Vision and pattern Recognition, Minneapolis, Minnesota, 1–8 (2007)

    Google Scholar 

  38. Burdescu, D.D., Brezovan M., Ganea, E., Stanescu L.: A New Method for Segmentation of Images Represented in a HSV Color Space. ACIVS 2009, 606-617(2009)

    Google Scholar 

  39. Burdescu, D.D., Brezovan M., Ganea E., Stanescu L.: New Algorithm for Segmentation of Images Represented as Hypergraph Hexagonal-Grid. IbPRIA 2011, 395-402 (2011)

    MathSciNet  Google Scholar 

  40. Brezovan, M., Burdescu, D., Ganea, E., Stanescu, L.: An Adaptive Method for Efficient Detection of Salient Visual Object from Color Images. Proceedings of the 20th International Conference on Pattern Recognition, Istambul, Turkey, 2346–2349 (2010)

    Google Scholar 

  41. Brezovan, M., Burdescu, D., Ganea, E., Stanescu, L.: A new method for segmentation of images represented in a HSV color space. Lecture Notes in Computer Science, 5807, 606–616 (2009)

    Article  Google Scholar 

  42. Adamek, T., O’Connor, N.,Murphy, N.:  Region-based segmentation of images using syntactic visual features. Proceedings of the International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS ’05), Montreux, Switzerland (2005)

    Google Scholar 

  43. Gonzales, R., Wintz, P.: Digital Image Processing. Reading, MA: Addison-Wesley (1987).

    Google Scholar 

  44. He, X., jia, W.: Hexagonal Structure for Intelligent Vision. Proceedings of The First International Conference on Information and Technologies, Karachi, Pakistan, 52-64 (2005)

    Google Scholar 

  45. Middleton, L., Sivaswamy, J.: Hexagonal Image Processing; A Practical Approach (Advances in Pattern Recognition). Springer-Verlag (2005)

    Google Scholar 

  46. Staunton, R.C.: The design of hexagonal sampling structures for image digitization and their use with local operators. Image Vision Computing, 7(3), 162–166 (1989)

    Article  Google Scholar 

  47. Billmeyer, F., Salzman,M.: Principles of color technology. New York: Wiley (1981)

    Google Scholar 

  48. Cormen, T., Leiserson, C., Rivest, R.: Introduction to algorithms. Cambridge, MA: MIT Press (1990)

    MATH  Google Scholar 

  49. Smith, J.R., Chang, S.F.: Tools and Techniques for Color Image Retrieval.  Science and Technology - Storage & Retrieval for Image and Video Databases IV, 2670, San Jose, CA, February 1996. IS&T/SPIE (1996)

    Google Scholar 

  50. Pantofaru C., Hebert, M.: A Comparison of Image Segmentation Algorithms. Technical Report CMU-RI-TR-05-40, Robotics Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania (2005)

    Google Scholar 

  51. Martin D., Fowlkes, C., Tal, D., Malik, J.: A Database of Human Segmented Natural Images and its Application to Evaluating Segmentation Algorithms and Measuring Ecological Statistics, IEEE (ed.), Proceedings of the Eighth International Conference On Computer Vision (ICCV-01), Vancouver, British Columbia, Canada, vol. 2, July 2001, 416–425

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Software Engineering, University of Craiova, Craiova, 200585, Romania

    Liana Stanescu, Dumitru Dan Burdescu, Marius Brezovan & Cristian Gabriel Mihai

Authors
  1. Liana Stanescu
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  2. Dumitru Dan Burdescu
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  3. Marius Brezovan
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  4. Cristian Gabriel Mihai
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Correspondence to Liana Stanescu .

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Stanescu, L., Burdescu, D.D., Brezovan, M., Mihai, C.G. (2012). Medical Images Segmentation. In: Creating New Medical Ontologies for Image Annotation. SpringerBriefs in Electrical and Computer Engineering(). Springer, New York, NY. https://doi.org/10.1007/978-1-4614-1909-9_3

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  • DOI: https://doi.org/10.1007/978-1-4614-1909-9_3

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