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Robust segmentation and intelligent decision system for cerebrovascular disease

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Abstract

Segmentation and classification of low-quality and noisy ultrasound images is challenging task. In this paper, a new approach is proposed for robust segmentation and classification of carotid artery ultrasound images and consequently, detecting cerebrovascular disease. The proposed technique consists of two phases, in first phase; it refines the class labels selected by user using expectation maximization algorithm. Genetic algorithm is then employed to select discriminative features based on moments of gray-level histogram. The selected features and refined targets are fed as input to neuro-fuzzy classifier for performing segmentation. Finally, intima-media thickness values are measured from segmented images to segregate the normal and abnormal subjects. In second phase, an intelligent decision-making system based on support vector machine is developed to utilize the intima-media thickness values for detecting cerebrovascular disease. The proposed robust segmentation and classification technique for ultrasound images (RSC-US) has been tested on a dataset of 300 real carotid artery ultrasound images and yields accuracy, F-measure, and MCC scores of 98.84, 0.988, 0.9767 %, respectively, using jackknife test. The segmentation and classification performance of the proposed (RSC-US) has been also tested at several noise levels and may be used as secondary observation.

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References

  1. Abdel-Dayem AR, El-Sakka MR, Fenster A (2005) Watershed segmentation for carotid artery ultrasound images. In: The 3rd ACS/IEEE international conference on computer systems and applications, p 131

  2. Afridi T, Khan A, Lee Y (2012) Mito-GSAAC: mitochondria prediction using genetic ensemble classifier and split amino acid composition. Amino Acids 42:1443–1454. doi:10.1007/s00726-011-0888-0

    Article  CAS  PubMed  Google Scholar 

  3. Anders U, Korn O (1999) Model selection in neural networks. Neural Netw 12:309–323

    Article  PubMed  Google Scholar 

  4. Ayache N, Cinquin P, Cohen I, Cohen L, Leitner F, Monga O (1996) Segmentation of complex three dimensional medical objects: a challenge and a requirement for computer-assisted surgery planning and performance. In: Computer Integrated Surgery Technolology and Clinical Applications, MIT Press, pp 59–74

  5. Borman S (2009) The expectation maximization algorithm—a short tutorial. Unpublished paper (online). https://www.cs.utah.edu/~piyush/teaching/EM_algorithm.pdf

  6. Caldairou B, Passat N, Habas PA, Studholme C, Rousseau F (2011) A non-local fuzzy segmentation method: application to brain MRI. Pattern Recognit 44:1916–1927

    Article  Google Scholar 

  7. Chaudhry A, Hassan M, Khan A, Kim J, Tuan T (2012) Automatic segmentation and decision making of carotid artery ultrasound images. In: Lee S, Cho H, Yoon K-J, Lee J (eds) Intelligent autonomous systems 12, vol 194. Advances in intelligent systems and computing. Springer, Berlin, pp 185–196

  8. Chaudhry A, Hassan M, Khan A, Kim JY, Tuan TA (2012) Image clustering using improved spatial fuzzy C-means. In: Paper presented at the proceedings of the 6th international conference on ubiquitous information management and communication, Kuala Lumpur, Malaysia

  9. Chih-Chung C, Chih-Jen L (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2:1–27. doi:10.1145/1961189.1961199

    Article  Google Scholar 

  10. Christodoulou CI, Pattichis CS, Pantziaris M, Nicolaides A (2003) Texture-based classification of atherosclerotic carotid plaques. IEEE Trans Med Imaging 22:902–912

    Article  CAS  PubMed  Google Scholar 

  11. Chuang K-S, Tzeng H-L, Chen S, Wu J, Chen T-J (2006) Fuzzy c-means clustering with spatial information for image segmentaion. Comput Med Imaging Graph 30:9–15

    Article  PubMed  Google Scholar 

  12. Cristianini N, Taylor JS (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, Cambridge

    Book  Google Scholar 

  13. Davies DL, Bouldin DW (1979) A cluster separation measure. IEEE Trans Pattern Anal Mach Intell 1:224–227

    Article  CAS  PubMed  Google Scholar 

  14. Golemati S, Stoitsis J, Sifakis E, Balkizas T, Nikita KS (2007) Using the Hough transform to segment ultrasound images of longitudinal and transverse sections of the carotid artery. Ultrasound Med Biol 33:1918–1932

    Article  PubMed  Google Scholar 

  15. Hassan M, Chaudhry A, Khan A, Kim JY (2012) Carotid artery image segmentation using modified spatial fuzzy c-means and ensemble clustering. Comput Methods Programs Biomed. doi:10.1016/j.cmpb.2012.1008.1011

    PubMed  Google Scholar 

  16. Hayat M, Khan A, Yeasin M (2012) Prediction of membrane proteins using split amino acid and ensemble classification. Amino Acids 42:2447–2460. doi:10.1007/s00726-011-1053-5

    Article  CAS  PubMed  Google Scholar 

  17. Holmes G, Donkin A, Witten IH (1994) WEKA: a machine learning workbench. In: Proceedings of the second Australian and New Zealand conference on intelligent information systems, 29 November–2 December, pp 357–361

  18. Iscan Z, Yüksel A, Dokur Z, Korürek M, Ölmez T (2009) Medical image segmentation with transform and moment based features and incremental supervised neural network. Digit Signal Proc 19:890–901

    Article  Google Scholar 

  19. Kamel M, Campilho A, Abdel-Dayem A, El-Sakka M (2007) Fuzzy C-means clustering for segmenting carotid artery ultrasound images. In: Hutchison D, Kanade T, Kittler J, Kleinberg JM, Mattern F, Mitchell JC, Naor M, Pandu Rangan C, Steffen B, Terzopoulos D, Tygar D, Weikum G (eds) Image analysis and recognition, vol 4633. Lecture notes in computer science. Springer, Heidelberg, pp 935–948

  20. Kamel M, Campilho A, Abdel-Dayem AR, El-Sakka MR (2005) Carotid artery ultrasound image segmentation using fuzzy region growing. In: Hutchison D, Kanade T, Kittler J, Kleinberg JM, Mattern F, Mitchell JC, Naor M, Pandu Rangan C, Steffen B, Terzopoulos D, Tygar D, Weikum G (eds) Image analysis and recognition, vol 3656. Lecture Notes in Computer Science. Springer, Berlin, pp 869–878

  21. Karimi N, Samavi S, Shirani S, Behnamfar P (2010) Segmentation of DNA microarray images using an adaptive graph-based method. IET Image Process 4:19–27

    Article  Google Scholar 

  22. Khan A, Siddiqa A, Munib S, Malik SA (2014) A recent survey of reversible watermarking techniques. Inf Sci 279:251–272. doi:10.1016/j.ins.2014.03.118

    Article  Google Scholar 

  23. Li BN, Chui CK, Chang S, Ong SH (2011) Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation. Comput Biol Med 41:1–10

    Article  PubMed  Google Scholar 

  24. Li W, Ogunbona P, deSilva C, Attikiouzel Y (2011) Semi-supervised maximum a posteriori probability segmentation of brain tissues from dual-echo magnetic resonance scans using incomplete training data. IET Image Process 5:222–232

    Article  Google Scholar 

  25. Loizou C (2014) A review of ultrasound common carotid artery image and video segmentation techniques. Med Biol Eng Comput 52:1073–1093. doi:10.1007/s11517-014-1203-5

    Article  PubMed  Google Scholar 

  26. Loizou CP, Pattichis CS, Pantziaris M, Nicolaides A (2007) An integrated system for the segmentation of atherosclerotic carotid plaque. IEEE Trans Inf Technol Biomed 11:661–667

    Article  PubMed  Google Scholar 

  27. Vasantha M, Dr V, Bharathi Sunniah, Dhamodharan T (2010) Medical image features, extraction, selection and classification. Int J Eng Sci Technol 2:2071–2076

    Google Scholar 

  28. Mahmoud MS, Khalid HM (2013) Expectation maximization approach to data-based fault diagnostics. Inf Sci 235:80–96

    Article  Google Scholar 

  29. Mao F, Gill J, Downey D, Fenster A (2000) Segmentation of carotid artery in ultrasound images. In: Proceedings of the 22nd annual international conference of the IEEE engineering in medicine and biology society, pp 1734–1737

  30. Menchon-Lara R-M, Bastida-Jumilla M-C, Morales-Sánchez J, Sancho-Gómez J-L (2014) Automatic detection of the intima-media thickness in ultrasound images of the common carotid artery using neural networks. Med Biol Eng Comput 52:169–181. doi:10.1007/s11517-013-1128-4

    Article  PubMed  Google Scholar 

  31. Minghao P, Heon Gyu L, Couchol P, Keun Ho R (2010) A data mining approach for dyslipidemia disease prediction using carotid arterial feature vectors. In: IEEE 2nd international conference on computer engineering and technology (ICCET), Chengdu, April 16–18, pp V2-171–V172-175

  32. Mitra S, Basak J (2001) FRBF: a fuzzy radial basis function network. Neural Comput Appl 10:244–252

    Article  Google Scholar 

  33. Murphy SL, Xu J, Kochanek KD (2013) National vital statistics report. National Center for Health Statistics, Hyattsville

    Google Scholar 

  34. Ovchinnikov S (1991) Similarity relations, fuzzy partitions and fuzzy ordering. Fuzzy Sets Syst 40:107–126

    Article  Google Scholar 

  35. Park SH, Goo JM, Jo C-H (2004) Receiver operating characteristic (ROC) curve: practical review for radiologists. Korean J Radiol 5(1):11–18

    Article  PubMed  PubMed Central  Google Scholar 

  36. Rocha R, Al Campilho, Silva J, Azevedo E, Santos R (2011) Segmentation of ultrasound images of the carotid using RANSAC and cubic splines. Comput Methods Programs Biomed 101:94–106

    Article  PubMed  Google Scholar 

  37. Santhiyakumari N, Rajendran P, Madheswaran M (2011) Medical decision-making system of ultrasound carotid artery intima-media thickness using neural networks. J Digit Imaging 24:1112–1125

    Article  CAS  PubMed  Google Scholar 

  38. Smith SM, Fox PT, Miller KL, Glahn DC, Fox PM, Mackay CE, Filippini N, Watkins KE, Toro R, Laird AR, Beckmann CF (2009) Correspondence of the brain’s functional architecture during activation and rest. Proc Natl Acad Sci 106:13040–13045. doi:10.1073/pnas.0905267106

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Svozil D, Vr Kvasnicka, Pospichal JÃ (1997) Introduction to multi-layer feed-forward neural networks. Chemom Intell Lab Syst 39:43–62

    Article  CAS  Google Scholar 

  40. Tan J, Lim Joon D, Fitt G, Wada M, Lim Joon M, Mercuri A, Marr M, Chao M, Khoo V (2010) The utility of multimodality imaging with CT and MRI in defining rectal tumour volumes for radiotherapy treatment planning: a pilot study. J Med Imaging Radiat Oncol 54:562–568

    Article  CAS  PubMed  Google Scholar 

  41. Velakoulis D, Wood SJ, Wong MH (2006) Hippocampal and amygdala volumes according to psychosis stage and diagnosis: a magnetic resonance imaging study of chronic schizophrenia, first-episode psychosis, and ultra-high-risk individuals. Arch Gen Psychiatry 63:139–149

    Article  PubMed  Google Scholar 

  42. Xu X, Zhou Y, Cheng X, Song E, Li G (2012) Ultrasound intima-media segmentation using Hough transform and dual snake model. Comput Med Imaging Graph 36:248–258. doi:10.1016/j.compmedimag.2011.06.007

    Article  PubMed  Google Scholar 

  43. Zadeh LA (1996) Fuzzy sets and their application to pattern classification and clustering analysis. In: George JK, Bo Y (eds) Fuzzy sets, fuzzy logic, and fuzzy systems. World Scientific, Singapore, pp 355–393

    Chapter  Google Scholar 

Download references

Acknowledgments

This research work is supported by the Higher Education Commission of Pakistan under the indigenous Ph.D. scholarship program [17-5-4(Ps4-078)/HEC/Sch/2008/]. The images used and the technical support have been provided by the radiology department Shifa International Hospital, Islamabad. With the full permission and consent of Shifa International Hospital, we have been used these images for research purposes (via Ref# Shifa/ISB/MG/RD/753/). The authors would like to extend their thanks to Shifa International Hospital, Islamabad, for providing data and technical support to complete this research work.

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Authors and Affiliations

  1. DMIS, PAEC-HQ, Islamabad, Pakistan

    Asmatullah Chaudhry

  2. Pattern Recognition Lab, PIEAS, P.O. Nilore, Islamabad, 45650, Pakistan

    Mehdi Hassan & Asifullah Khan

  3. Air University, PAF Complex, Sector E-9, Islamabad, Pakistan

    Mehdi Hassan

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  1. Asmatullah Chaudhry
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  2. Mehdi Hassan
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  3. Asifullah Khan
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Correspondence to Mehdi Hassan.

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There are no conflicts of interest that could inappropriately influence this work.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards (via Hosptial Ref# Shifa/ISB/MG/RD/753/).

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Informed consent was obtained from all individual participants included in the study.

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Chaudhry, A., Hassan, M. & Khan, A. Robust segmentation and intelligent decision system for cerebrovascular disease. Med Biol Eng Comput 54, 1903–1920 (2016). https://doi.org/10.1007/s11517-016-1481-1

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