Abstract
This article introduces an efficient method that combines the advantages of Fast Marching Method (FMM) in conjunction with Harris corner descriptor. An application of Dynamic Renal Scintigraphy imaging has been chosen and a new approach has been applied to see its ability to have a high accuracy of Region Of Interest (ROI) segmentation and tracking in scintigraphic images sequences. The introduced system starts with an image processing algorithm to enhance the contrast of the input images.
This is followed by the segmentation phase which consists of an automated algorithm. Finally, the ROI tracking phase was described.
To evaluate the performance of the presented approach, we present tests on synthetic and real images are presented.
The experimental results obtained show that the effectiveness and performance of the proposed system is satisfactory.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Pretorius, P.H., Fung, L.C.T., Schell, C.P., Nishinaka, K., Groiselle, C.J., Glick, S.J., Narayanan, M.V., King, M.A.: Dynamic and static tomographic renal coincidence imaging with a gamma camera using Rb-82: a feasibility study. IEEE Transactions on Nuclear Science 49, 5 (2002)
Caglar, M., Gedik, G.K., Karabulut, E.: Differential renal function estimation by dynamic renal scintigraphy: influence of background definition and radiopharmaceutical. Nuclear Medicine Communications, 1002–5 (November 2008)
Prigent, A., Cosgriff, P., Piepsz, A., Rehling, M., Rutland, M., Taylor, A.: Consensus report on quality control of quantitative measurements of renal function obtained from the renogram: International Consensus Committee from the Scientific Committee of Radionuclides in Nephrourology. Seminars in Nuclear Medicine, 146–159 (April 1999)
Hu, Grossberg and Mageras,Survey of Recent Volumetric Medical Image Segmentation Techniques, 2009
Khalifa, N., Ettaeib, S., Wahabi, Y., Hamrouni, K.: Left Ventricle Tracking in Isotopic Ventriculography Using Statistical Deformable Models. Int. Arab J. Inf. Technol, 213–222 (2010)
White, D.R.R., Sampson, W.F.D., Macleod, M.A., Pilkington, J.B.: An assessment of two methods for generating automatic regions of interest. Nuclear Medicine Communications, 1005 (1998)
Aribi, Y., Wali, A., Hamza, F., Alimi, A.M., Guermazi, F.: Analysis of scintigraphic renal dynamic studies: an image processing tool for the clinician and researcher. In: Hassanien, A.E., Salem, A.-B.M., Ramadan, R., Kim, T.-h. (eds.) AMLTA 2012. CCIS, vol. 322, pp. 267–275. Springer, Heidelberg (2012)
Rahmatpour, M., Rajabi, H., Sardari, D., Babapour, F., Ahmadi, S.: Semi-Automation of renal region of interest in renography images by thresholding and edge detection. Romanian Reports in Physics, 127–132 (2014)
Kaur, J., Jindal, A.: Segmentation Algorithms for Thyroid Scintigraphy Images. IJCST, 3 (2012)
Hraiech, N., Weinland, D., Hamrouni, K.: An active contour model based on splines and separating forces to detect the left ventricle in scintigraphic images. In: 2nd International Conference on Machine Intelligence, ACIDCA-ICMI 2005, November 2005
Kaur, J., Hannequin, P., Liehn, J.C., Valeyre, J.: Cluster analysis for automatic image segmentation in dynamic scintigraphies. Nucl. Med. Commun., 383–393 (1990)
Ståhl, D., Åström, K., Overgaard, N.C., Landgren, M., Sjöstrand, K., Edenbrandt, L.: Automatic compartment modelling and segmentation for dynamical renal scintigraphies. In: Heyden, A., Kahl, F. (eds.) SCIA 2011. LNCS, vol. 6688, pp. 557–568. Springer, Heidelberg (2011)
Siogkas, G.K., Dermatas, E.S.: Automating Medical Diagnosis in Renal Scintigrams using RBF Networks. First International Conference From Scientific Computing to Computational Engineering (2004)
Marcuzzo, M., Masiero, P.R., Scharcanski, J.: Quantitative parameters for the assessment of renal scintigraphic images. In: 29th Annual International Conference of the IEEE. EMBS 2007, pp. 3438–3441. Engineering in Medicine and Biology Society (2007)
Aribi, Y., Wali, A., Alimi, A.M.: An intelligent system for renal segmentation. In: IEEE 15th International Conference on e-Health Networking, Applications Services (Healthcom), pp. 11–15 (2013)
Sethian, J.A.: A fast marching level set method for monotonically advancing fronts. In: Proceedings of the National Academy of Science, pp. 1591–1595 (1996)
Moravec, H.P.: Obstacle avoidance and navigation in the real world by a seeing robot rover. Ph.D. thesis, Stanford, CA, USA (1980)
Sethian, J., Adalsteinsson, D.: An overview of level set methods for etching, deposition, and lithography development. IEEE Transactions on Semiconductors Manufacturing 10(1), 167–184 (1997)
Dice, L.R.: Measures of the Amount of Ecologic Association Between Species. Ecology 26(3), 297–302 (1945)
Huttenlocher, D.P., Klanderman, G.A., Rucklidge, W.J.: Comparing images using the Hausdorff distance. IEEE Transactions on Pattern Analysis and Machine Intelligence 15(9), 850–863 (1993)
Aribi, Y., Hamza, F., Wali, A., Alimi, M.A., Guermazi, F.: An Automated System for the Segmentation of Dynamic Scintigraphic Images. Applied Medical Informatics 34(2), 1–12 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Aribi, Y., Wali, A., Alimi, A.M. (2015). Automated Fast Marching Method for Segmentation and Tracking of Region of Interest in Scintigraphic Images Sequences. In: Azzopardi, G., Petkov, N. (eds) Computer Analysis of Images and Patterns. CAIP 2015. Lecture Notes in Computer Science(), vol 9257. Springer, Cham. https://doi.org/10.1007/978-3-319-23117-4_62
Download citation
DOI: https://doi.org/10.1007/978-3-319-23117-4_62
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-23116-7
Online ISBN: 978-3-319-23117-4
eBook Packages: Computer ScienceComputer Science (R0)