A Local Statistics Based Region Growing Segmentation Method for Ultrasound Medical Images
Authors: Ashish Thakur, Radhey Shyam Anand
Abstract:
This paper presents the region based segmentation method for ultrasound images using local statistics. In this segmentation approach the homogeneous regions depends on the image granularity features, where the interested structures with dimensions comparable to the speckle size are to be extracted. This method uses a look up table comprising of the local statistics of every pixel, which are consisting of the homogeneity and similarity bounds according to the kernel size. The shape and size of the growing regions depend on this look up table entries. The algorithms are implemented by using connected seeded region growing procedure where each pixel is taken as seed point. The region merging after the region growing also suppresses the high frequency artifacts. The updated merged regions produce the output in formed of segmented image. This algorithm produces the results that are less sensitive to the pixel location and it also allows a segmentation of the accurate homogeneous regions.
Keywords: Local statistics, region growing, segmentation, ultrasound images.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1333096
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3113References:
[1]Hiransakolwong, N., Hua, K. A., Vu, K., and Windyga, P. S. (2003) Segmentation of ultrasound liver images: An automatic approach. IEEE Multimedia and Expo, 2003 ICME 03. Proceedings, 2003 International Conference, 1, 573 -576.
[2]Gonzalez, R. C. and Wintz, P. (2002) Digital Image Processing, 2nd ed., Pearson Education (Singapore) Pte. Ltd. Delhi, India.
[3]Pavlidis, T. (1977) Structural Pattern Recognition, Springer-Verlag, Berlin, Heidelberg.
[4]Zhu, S.C., and Yuille, A. (1996) Region competition: Unifying snakes, region growing, and bayes/MDL for multiband image segmentation, IEEE Transaction on Pattern Analysis and Machine Intelligence, 18, 884-900.
[5]Pavlidis, T., and Liow, Y. T. (1990) Integrating region growing and edge detection , IEEE Transaction on Pattern Analysis and Machine Intelligence, 12, 225-231.
[6]McInerney, T., and Terzopoulos, D. (1996) Deformable models in medical image analysis: a survey, Medical Image Analysis, 1, 91-108.
[7]Adams, R., and Bischof, L. (1994) Seeded region growing. IEEE Transaction on Pattern Analysis and Machine Vision, 16, 641-647.
[8]Vincent, L., and Soille, P. (1991) Watersheds in digital spaces: An efficient algorithm based on immersion simulation. IEEE Transaction on Pattern Analysis and Machine Intelligence, 13, 583-598.
[9]Hao, X., and Gao, S. (1999) A novel multi scale nonlinear thresholding method for ultrasonic speckle suppressing, IEEE Transactions on Medical Imaging, 18, 787-794.
[10] Loupas, T., McDicken, W. N., and Allan, P.L. (1989) An adaptive weighted median filter for speckle suppression in medical ultrasonic images, IEEE Transaction on Circuits Systems, 36, 129-135.
[11] Karaman, M., Kutay, M. A. and Bozdagi, G. (1995) adaptive speckle suppression filter for medical ultrasonic imaging. IEEE Transactions on Medical Imaging, 14, 283-292.
[12] Y. Chen, Y., Yin, R. Flynn, P., and Broschat, S. (2003) Aggressive region growing for speckle reduction in ultrasound images. Pattern Recognition Letters, 24, 677-691.
[13] Sonka, M., Hlavac, V., and Boyle, R. (1991) Image Processing, Analysis and Machine Vision. 2nd ed. Pacific Grove, CA: PWS.
[14] Efford, N. (2000) Digital Image Processing Using JAVA, 1st ed., Addison Wesley Professional.