A Local Statistics Based Region Growing Segmentation Method for Ultrasound Medical Images

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.





References:
[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.