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Segmentation of regions of interest and post-segmentation edge location improvement in computer-aided bone age assessment

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Abstract

Segmentation of anatomical structures in radiological images is one of the important steps in the computerized approach to the bone age assessment. In this paper a method dealing with correct location of the borders in the epi-metaphyseal regions of interest is described. The well segmented bone structures are obtained utilizing the Gibbs random fields as the first segmentation step; however this method does not prove to be adequate in the correct outline of other tissues in the epi-metaphyseal area. In order to correct delineation of cartilage in this region, the second segmentation step utilizing the active contours serving as a post-segmentation edge location technique is applied. Controlling of tension and bending of the active contour requires a set of weights in the energy functional to be set. To adjust the weights and to initially test the methodology a model of region of interest containing three different anatomical structures corrupted with Gaussian noise has been designed. Combined methodology of Gibbs random fields and active contours with the final set of weights was applied to 200 regions of interest randomly selected from 1100 left hand radiographs. A meaningful improvement in terms of ultimate contour location and smoothing has been observed in regions with cartilage or bone convexity developed near the bottom region of the epiphysis.

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References

  1. Greulich WW, Pyle SI (1971) Radiographic atlas of skeletal development of hand wrist, 2nd edn. Stanford University Press, Stanford

  2. Tanner JM, Whitehouse RH (1975) Assessment of skeletal maturity and prediction of adult height (TW2 method). Academic, London

    Google Scholar 

  3. Piętka E, Gertych A, Pośpiech S, Huang HK, Cao F (2001) Computer assisted bone age assessment: image pre-processing and ROI extraction. IEEE Trans Med Imaging 20:715–729

    Article  Google Scholar 

  4. Piętka E, Pośpiech S, Gertych A, Cao F, Huang HK, Gilsanz V (2001) Computer automated approach to the extraction of epiphyseal regions in hand radiographs. J Digit Imaging 14:165–172

    Article  Google Scholar 

  5. Piętka E, Pośpiech-Kurkowska S, Gertych A, Cao F (2003) Integration of computer assisted bone age assessment with clinical PACS. Comput Med Imging Graph 27: 217–228

    Article  Google Scholar 

  6. Lee JS (1981) Refined filtering of image noise using local statistics. Comput Vis Graph Image Process 15:380–389

    Google Scholar 

  7. PappasT (1992) An adaptive clustering algorithm for image segmentation. IEEE Trans Signal Process 40:901–914

    Article  Google Scholar 

  8. Immerkaer I (1996) Fast noise variance estimation. Comput Vis Image Underst 64:300–302

    Article  Google Scholar 

  9. Besag J (1986) On the statistical analysis of dirty pictures. J R Stat Soc B 48:259–302

    MATH  MathSciNet  Google Scholar 

  10. Kass M, Witkin A, Terzopoulos D (1988) Snakes: active contour models. Int J Comput Vis 1:321–331

    Article  Google Scholar 

  11. Terzopoulos D, Fleisher K (1988) Deformable models. Vis Comput 4:306–331

    Article  Google Scholar 

  12. Sonka M, Hlavac V, Boyle R (1998) Image processing analysis and machine vision, 2nd edn. Thomson Learning Vocational Publishing, Florence 84:374

  13. Williams DJ, Shah M (1992) A fast algorithm for active contours and curvature estimation, CVGIP. Image Underst 55:14–26

    Google Scholar 

  14. MacQueen JB (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of 5th Berkley symposium on mathematical statistics and probability. University of California, Berkley 1:281–297

  15. Piętka E, Gertych A, Witko K (2005) Informatics infrastructure of CAD system, Comput Med Imaging Graph 29:157–169

    Article  Google Scholar 

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Correspondence to Arkadiusz Gertych.

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Gertych, A., Piętka, E. & Liu, B.J. Segmentation of regions of interest and post-segmentation edge location improvement in computer-aided bone age assessment. Pattern Anal Applic 10, 115–123 (2007). https://doi.org/10.1007/s10044-006-0056-4

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  • DOI: https://doi.org/10.1007/s10044-006-0056-4

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