Abstract
Medical images have an undeniably integral role in the process of diagnosing and treating of a very large number of ailments. Processing such images (for different purposes) can significantly improve the efficiency and effectiveness of this process. The first step in many medical image processing applications is segmentation, which is used to extract the Region of Interest (ROI) from a given image. Due to its effectiveness, a very popular segmentation algorithm is the Fuzzy C-Means (FCM) algorithm. However, FCM takes a long processing time especially for 3D model. This problem can be solved by utilizing parallel programming using Graphics Processing Unit (GPU). In this paper, a hybrid parallel implementation of FCM for extracting volume object from medical DICOM files has been proposed. The proposed algorithm improves the performance 5× compared with the sequential version.
Similar content being viewed by others
Notes
References
Ahmed MN, Yamany SM, Mohamed N, Farag AA, Moriarty T (2002) A modified fuzzy c-means algorithm for bias field estimation and segmentation of mri data. IEEE Trans Med Imaging 21(3):193–199
Al-Ayyoub M, Al-Zghool D (2013) Determining the type of long bone fractures in x-ray images. WSEAS Trans Inf Sci Appl 10(8):261–270
Al-Ayyoub M, Husari G, Darwish O, Alabed-alaziz A (2012) Machine learning approach for brain tumor detection. In: Proceedings of the 3rd International Conference on Information and Communication Systems. ACM, p 23
Al-Ayyoub M, Alawad D, Al-Darabsah K, Aljarrah I (2013a) Automatic detection and classification of brain hemorrhages. WSEAS Trans Comput 12(10):395–405
Al-Ayyoub M, Hmeidi I, Rababah H (2013b) Detecting hand bone fractures in x-ray images. Journal of Multimedia Processing and Technologies 4(3):155–168
Al-Ayyoub M, Abu-Dalo AM, Jararweh Y, Jarrah M, Al Sa’d M (2015) A gpu-based implementations of the fuzzy c-means algorithms for medical image segmentation. J Supercomput 71(8):3149–3162
Al-Ayyoub M, Alzubi S, Jararweh Y, Alsmirat M (2016a) A gpu-based breast cancer detection system using fuzzy c-means clustering algorithm. In: In the 5th International Conference on Multimedia Computing and Systems (ICMCS)
Al-Ayyoub M, Oqaily A, Jarrah MI, Karajeh H (2016b) Automatically determining the location and length of coronary artery thrombosis using coronary angiography. Int J Comput Sci Inf Secur 14(3):10
Al-Darabsah K, Al-Ayyoub M (2013) Breast cancer diagnosis using machine learning based on statistical and texture features extraction. In: The 4th International Conference on Information and Communication Systems (ICICS)
Alawneh K, Al-dwiekat M, Alsmirat M, Al-Ayyoub M (2015) Computer-aided diagnosis of lumbar disc herniation. In: 2015 6th International Conference on Information and Communication Systems (ICICS), pp 286–291, doi:10.1109/IACS.2015.7103190, (to appear in print)
Alomari R, Corso JJ, Chaudhary V, Dhillon G (2011) Toward a clinical lumbar cad: herniation diagnosis. Int J CARS 6(1):119–126
Alsmirat M, Jararweh Y, Al-Ayyoub M, Shehab M, Gupta BB (2016) Accelerating compute intensive medical imaging segmentation algorithms using gpus. Multimedia Tools and Applications (MTAP) To appear
Althebyan Q, Yaseen Q, Jararweh Y, Al-Ayyoub M (2016) Cloud support for large scale e-healthcare systems. Ann Telecommun:1–13
AlZubi S, Islam N, Abbod M (2011) Multiresolution analysis using wavelet, ridgelet, and curvelet transforms for medical image segmentation. J Biomed Imaging:4
AlZubi S, Sharif MS, Abbod M (2011b) Efficient implementation and evaluation of wavelet packet for 3d medical image segmentation. In: 2011 IEEE International Workshop on IEEE, Medical Measurements and Applications Proceedings (MeMeA), pp 619–622
Arnoldi E, Gebregziabher M, Schoepf UJ, Goldenberg R, Ramos-Duran L, Zwerner PL, Nikolaou K, Reiser MF, Costello P, Thilo C (2010) Automated computer-aided stenosis detection at coronary ct angiography: initial experience. Eur Radiol 20(5):1160–1167
Cai W, Chen S, Zhang D (2007) Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation. Pattern Recogn 40 (3):825–838
Chan T (2007) Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Comput Med Imaging Graph 31(4):285–298
Chen Ch, Pau LF, Wang PSp (2010) Handbook of pattern recognition and computer vision, vol 27. World Scientific
Chen S, Zhang D (2004) Robust image segmentation using fcm with spatial constraints based on new kernel-induced distance measure. IEEE Trans Syst Man Cybern Part B Cybern 34(4):1907?-1916
Chen W, Giger ML, Bick U (2006) A fuzzy c-means (fcm)-based approach for computerized segmentation of breast lesions in dynamic contrast-enhanced mr images 1. Acad Radiol 13(1):63–72
Chuang KS, Tzeng HL, Chen S, Wu J, Chen TJ (2006) Fuzzy c-means clustering with spatial information for image segmentation. Comput Med Imaging Graph 30(1):9–15
Cook S (2012) CUDA programming: a developer’s guide to parallel computing with GPUs. Newnes
Doi K (2007) Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Comput Med Imaging Graph 31(4):198–211
Dunn JC (1973) A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. Taylor & Francis
Eklund A, Dufort P, Forsberg D, LaConte SM (2013) Medical image processing on the gpu–past, present and future. Med Image Anal 17(8):1073–1094
El-Dahshan ESA, Mohsen HM, Revett K, Salem ABM (2014) Computer-aided diagnosis of human brain tumor through mri: a survey and a new algorithm. Expert Syst Appl 41(11):5526–5545
Eschrich S, Ke J, Hall LO, Goldgof DB (2003) Fast accurate fuzzy clustering through data reduction. IEEE Trans Fuzzy Syst 11(2):262–270
Fulkerson B, Soatto S (2010) Really quick shift: Image segmentation on a gpu. In: Trends and Topics in Computer Vision. Springer, pp 350–358
Gletsos M, Mougiakakou SG, Matsopoulos GK, Nikita KS, Nikita AS, Kelekis D (2003) A computer-aided diagnostic system to characterize ct focal liver lesions: design and optimization of a neural network classifier. IEEE Trans Inf Technol Biomed 7(3):153–162
Hall LO, Goldgof DB (2011) Convergence of the single-pass and online fuzzy c-means algorithms. IEEE Trans Fuzzy Syst 19(4):792–794
Hore P, Hall LO, Goldgof DB (2007) Single pass fuzzy c means. In: 2007 IEEE International Fuzzy Systems Conference. IEEE, pp 1–7
Hwang C, Rhee FCH (2007) Uncertain fuzzy clustering: interval type-2 fuzzy approach to c-means. IEEE Trans Fuzzy Syst 15(1):107–120
Jarrah M, Al-Quraan M, Jararweh Y, Al-Ayyoub M (2016) Medgraph: a graph-based representation and computation to handle large sets of images. Multimedia Tools and Applications:1–17
Kim JH (2004) Computer-aided diagnosis for lung cancer. J Lung Cancer 3 (2):67–70
Klodt M, Cremers D (2011) A convex framework for image segmentation with moment constraints. In: 2011 IEEE International Conference on Computer vision (ICCV). IEEE, pp 2236-2243
Krinidis S, Chatzis V (2010) A robust fuzzy local information c-means clustering algorithm. IEEE Trans Image Process 19(5):1328–1337
Krishnapuram R, Keller JM (1996) The possibilistic c-means algorithm: insights and recommendations. IEEE Trans Fuzzy Syst 4(3):385–393
Lefohn AE, Cates JE, Whitaker RT (2003) Interactive, gpu-based level sets for 3d segmentation. In: Medical Image Computing and Computer-Assisted Intervention-MICCAI 2003. Springer, pp 564–572
Lehmann TM, Gönner C, Spitzer K (1999) Survey: Interpolation methods in medical image processing. IEEE Trans Med Imaging 18(11):1049–1075
Li C, Huang R, Ding Z, Gatenby JC, Metaxas DN, Gore JC (2011) A level set method for image segmentation in the presence of intensity inhomogeneities with application to mri. IEEE Trans Image Process 20(7):2007–2016
Liew AWC, Yan H (2003) An adaptive spatial fuzzy clustering algorithm for 3-d mr image segmentation. IEEE Trans Med Imaging 22(9):1063–1075
Lorensen WE, Cline HE (1987) Marching cubes: a high resolution 3d surface construction algorithm. In: ACM Siggraph computer graphics, vol 21. ACM, pp 163-169
Maintz JA, Viergever MA (1998) A survey of medical image registration. Med Image Anal 2(1):1–36
McAuliffe MJ, Lalonde FM, McGarry D, Gandler W, Csaky K, Trus BL (2001) Medical image processing, analysis and visualization in clinical research. In: Proceedings of the 14th IEEE Symposium on Computer-Based Medical Systems,2001. CBMS 2001. IEEE, pp 381–386
Mookiah MRK, Acharya UR, Chua CK, Lim CM, Ng E, Laude A (2013) Computer-aided diagnosis of diabetic retinopathy: a review. Comput Biol Med 43(12):2136–2155
Murugavalli S, Rajamani V (2006) A high speed parallel fuzzy c-mean algorithm for brain tumor segmentation. BIME journal 6(1):29–33
Nakamoto T, Taguchi A, Ohtsuka M, Suei Y, Fujita M, Tsuda M, Sanada M, Kudo Y, Asano A, Tanimoto K (2014) A computer-aided diagnosis system to screen for osteoporosis using dental panoramic radiographs
Ng H, Ong S, Foong K, Goh P, Nowinski W (2006) Medical image segmentation using k-means clustering and improved watershed algorithm. In: 2006 IEEE Southwest Symposium on Image Analysis and Interpretation. IEEE, pp 61-65
Oqaily A, Jarrah MI, Karajeh H, Al-Ayyoub M, Hmeidi I (2014) Localization of coronary artery thrombosis using coronary angiography. In: The 3rd International Conference on Informatics Engineering and Information Science (ICIEIS2014). The Society of Digital Information and Wireless Communication, pp 310–316
Ortiz A, Palacio AA, Górriz JM, Ramírez J, Salas-González D (2013) Segmentation of brain mri using som-fcm-based method and 3d statistical descriptors. Comput Math Methods Med 2013
Pratx G, Xing L (2011) Gpu computing in medical physics: a review. Med Phys 38(5):2685–2697
Qiu C, Xiao J, Yu L, Han L, Iqbal MN (2013) A modified interval type-2 fuzzy c-means algorithm with application in mr image segmentation. Pattern Recogn Lett 34(12):1329–1338
Rahimi S, Zargham M, Thakre A, Chhillar D (2004) A parallel fuzzy c-mean algorithm for image segmentation. In: IEEE Annual meeting of the Fuzzy information, 2004. Processing NAFIPS’04, vol 1. IEEE, pp 234–237
Rangayyan RM, Ayres FJ, Desautels JL (2007) A review of computer-aided diagnosis of breast cancer: Toward the detection of subtle signs. J Frankl Inst 344 (3):312–348
Rhee FCH, Hwang C (2001) A type-2 fuzzy c-means clustering algorithm. In: Joint 9th IFSA World Congress and 20th NAFIPS International Conference, 2001, vol 4. IEEE, pp 1926–1929
Rubio E, Castillo O (2014) Interval type-2 fuzzy clustering algorithm using the combination of the fuzzy and possibilistic c-mean algorithms. In: IEEE Conference on Norbert Wiener in the 21st Century (21CW), 2014. IEEE, pp 1–6
Ryoo S, Rodrigues CI, Baghsorkhi SS, Stone SS, Kirk DB, Hwu WmW (2008) Optimization principles and application performance evaluation of a multithreaded gpu using cuda. In: Proceedings of the 13th ACM SIGPLAN Symposium on Principles and practice of parallel programming. ACM, pp 73–82
Shehab M, Al-Ayyoub M, Jararweh Y, JarrahM(2016) Using gpus to improve the performance of fuzzy clustering algorithms. J. Supercomput. To appear
Shehab MA, Al-Ayyoub M, Jararweh Y (2015) Improving fcm and t2fcm algorithms performance using gpus for medical images segmentation. In: Proceedings of the 6th International Conference on Information and Communication Systems (ICICS
Ugarriza LG, Saber E, Vantaram SR, Amuso V, Shaw M, Bhaskar R (2009) Automatic image segmentation by dynamic region growth and multiresolution merging. IEEE Trans Image Process 18(10):2275–2288
Velthuizen RP, Hall LO, Clarke LP, Bensaid AM, Arrington J, Silbiger ML (1993) Unsupervised fuzzy segmentation of 3d magnetic resonance brain images. In: IS&T/SPIE’s Symposium on Electronic Imaging: Science and Technology. International Society for Optics and Photonics, pp 627-635
Walters JP, Balu V, Kompalli S, Chaudhary V (2009) Evaluating the use of gpus in liver image segmentation and hmmer database searches. In: IEEE International Symposium on Parallel & Distributed Processing, 2009. IPDPS 2009. IEEE, pp 1–12
Wang J, Kong J, Lu Y, Qi M, Zhang B (2008) A modified fcm algorithm for mri brain image segmentation using both local and non-local spatial constraints. Comput Med Imaging Graph 32(8):685– 698
Wang L, Yang B, Chen Y, Chen Z, Sun H (2014) Accelerating fcm neural network classifier using graphics processing units with cuda. Appl Intell 40(1):143–153
Winder J, Bibb R (2005) Medical rapid prototyping technologies: state of the art and current limitations for application in oral and maxillofacial surgery. J Oral Maxillofac Surg 63(7):1006–1015
Yoshida H, Nappi J (2001) Three-dimensional computer-aided diagnosis scheme for detection of colonic polyps. IEEE Trans Med Imaging 20(12):1261–1274
Acknowledgments
This work was supported in part by the Deanship of Research at the Jordan University of Science and Technology (Grant # 20160081).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Al-Ayyoub, M., AlZu’bi, S., Jararweh, Y. et al. Accelerating 3D medical volume segmentation using GPUs. Multimed Tools Appl 77, 4939–4958 (2018). https://doi.org/10.1007/s11042-016-4218-0
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-016-4218-0