Abstract
The objective of this paper is to evaluate an approach for CT liver image segmentation, to separate the liver, and segment it into a set of regions of interest (ROIs). The automated segmentation of liver is an essential phase in all liver diagnosis systems for different types of medical images. In this paper, the artificial bee colony optimization algorithm (ABC) aides to segment the whole liver. It is implemented as a clustering technique to achieve this mission. ABC calculates the centroid values of image clusters in CT images. Using the least distance between every pixel value and different centroids will result in a binary image for each cluster. Applying some morphological operations on every binary clustered image can help to remove small and thin objects. These objects represent parts of flesh tissues adjacent to the liver, sharp edges of other organs and tiny lesions spots inside the liver. This is followed by filling the large regions in each cluster binary image. Summation of the clusters’ binary images results in a reasonable image of segmented liver. Then, the segmented image of liver is enhanced using simple region growing technique (RG). Finally, one of ABC algorithm or watershed is applied once to extract the lesioned regions in the liver, which can be used by any classifier to determine the type of lesion. A set of 38 images, taken in pre-contrast phase, was used to segment the liver and test the proposed approach. Testing the results is handled using similarity index to validate the success of the approach. The experimental results showed that the overall accuracy offered by the proposed approach, results in 93.73 % accuracy.
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References
Motley, G., Dalrymple, N., Keesling, C., Fischer, J., Harmon, W.: Hounsfield unit density in the determination of urinary stone composition. Urology 58(2), 170–173 (2001). IEEE Trans. Syst. Man Cybern. SMC-9(1) (1979)
Sherlock, S., Summerfield, J.: A Colour Atlas of Liver Disease. Wolfe, London (1979)
Sherlock, S.: Diseases of the Liver and Biliary System, 5th edn. Blackwell, London (1973)
Chen, M.Y.M., Pope, T.L., Ott, D.J.: Basic Radiology, 2nd edn. McGrow Hill, US (2011)
Mostafa, A., Hefny, H., Ghali, N.I., Hassanien, A., Schaefer, G.: Evaluating the effects of image filters in CT Liver CAD system. In: IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI2012). The Chinese University of Hong Kong, Hong Kong SAR, on Jan 5–7 (2012)
Karaboga, D.: An Idea Based On Honey Bee Swarm For Numerical Optimization, Technical Report-TR06, Erciyes University, Engineering Faculty, Computer Engineering Department (2005)
Bitam, S., Batouche, M., Talbi, E.: A survey on bee colony algorithms. In: 2010 IEEE International Symposium on Parallel and Distributed Processing, Workshops and Phd Forum (IPDPSW), pp. 1–8 (2010)
Karaboga, D., Gorkemli, B., Ozturk, C., Karaboga, N.: A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artif. Intell. Rev. 42(1), 21–57 (2014)
Basturk, B., Karaboga, D.: An artificial bee colony (ABC) algorithm for numeric function optimization. In: IEEE Swarm Intelligence Symposium 2006, May 12– 14, Indianapolis, Indiana, USA (2006)
Horng, M.: Multilevel thresholding selection based on the artificial bee colony algorithm for image segmentation. Expert Syst. Appl. 38(11), 13785–13791 (2011)
Chen, E., Chung, P., Chen, C., Tsai, H., Chang, C.: An automatic diagnostic system for CT liver image classification. IEEE Trans. Biomed. Eng. 45(6), 783–794 (1998)
Xuechen, L., Luo, S., Jaming, L.: Liver segmentation from CT image using fuzzy clustering and level set. J.Signal Inf. Process. 4(3), 36–42 (2013)
Jiang, H., Ma, B., Zong, M., Zhou, X.: Liver segmentation based on snakes model and improved GrowCut algorithm in abdominal CT image. Computational and Mathematical Methods in Medicine (2013)
Alomari, R., Kompalli, S., Chaudhary, V.: Segmentation of the liver from abdominal CT using Markov random field model and GVF snakes. In: Complex, International Conference on Intelligent and Software Intensive Systems, CISIS, pp. 293–298 (2008)
Sharma, A., Kaur, P.: Optimized liver tumor detection and segmentation using neural network. Int. J. Recent Technol. Eng. (IJRTE) 2(5), 7–10 (2013)
Ali, A., Couceiro, M., Hassenian, A.: Towards an optimal number of clusters using a nested particle swarm approach for liver CT image segmentation. Adv. Mach. Learn. Technol. Appl. 488, 331–343 (2014)
Selvaraj, G., Janakiraman, S.: Improved feature selection based on particle swarm optimization for liver disease diagnosis. In: Swarm, Evolutionary, and Memetic Computing, vol. 8298, pp. 214–225. Springer International Publishing (2013)
Zidan, A., Ghali, N.I., Hassanien, A., Hefny, H.: Level set-based CT liver computer aided diagnosis system. Int. J. Imaging Robot. 9 (2013)
Vanhamel, I., Pratikakis, I., Sahli, H.: Multiscale gradient watersheds of color images. IEEE Trans. Image Process. 12(6), 617–626 (2003)
Kowalczyk, M., Koza, P., Kupidura, P., Marciniak, J.: Application of mathematical morphology operations for simplification and improvement of correlation of image in close-range photomography. In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XXXVII. Part B5. Beijing (2008)
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Mostafa, A., Fouad, A., Elfattah, M.A., Hassanien, A.E., Hefny, H. (2016). Artificial Bee Colony Based Segmentation for CT Liver Images. In: Dey, N., Bhateja, V., Hassanien, A. (eds) Medical Imaging in Clinical Applications. Studies in Computational Intelligence, vol 651. Springer, Cham. https://doi.org/10.1007/978-3-319-33793-7_18
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DOI: https://doi.org/10.1007/978-3-319-33793-7_18
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