INFORMATION CHANGE THE WORLD

International Journal of Image, Graphics and Signal Processing(IJIGSP)

ISSN: 2074-9074 (Print), ISSN: 2074-9082 (Online)

Published By: MECS Press

IJIGSP Vol.13, No.5, Oct. 2021

Segmentation of Medical X-ray Bone Image Using Different Image Processing Techniques

Full Text (PDF, 538KB), PP.27-40


Views:0   Downloads:0

Author(s)

Folasade Olubusola Isinkaye, Abiodun Gabriel Aluko, Olayinka Ayodele Jongbo

Index Terms

Image Processing, Image Segmentation, Thresholding, Edge-based, Region-based technique, Deformable Model

Abstract

Accurate medical image processing plays a crucial role in several clinical diagnoses by assisting physicians in timely treatment of wounds and mishaps. Medical doctors in the hospitals generally rely on examining bone x-ray images based on their expertise, knowledge and  past experiences in determining whether a fracture exist in bone or not. Nevertheless, majority of fractures identification methods using X-rays in the hospitals is beyond human understanding due to variation in different attributes of fracture and complication of bone organization thereby making it difficult for doctors to correctly diagnose and proffer adequate treatment to patient ailments. The need for robust diagnostic image processing techniques for image segmentation for different bone structures cannot be overemphasized. This research implemented different image segmentation techniques on a bone x-ray image in order to identify the most efficient for timely medical diagnosis. Also, the strength and weaknesses of the diverse segmentation techniques were also identified. This will empowered researchers with appropriate knowledge needed to improve and build better image segmentation models which doctors can use in handling complex medical image processing problems. Also, miss rate in bone X-rays that contains multiple abnormalities can be lowered by using appropriate image segmentation techniques thereby improving some of the labor intensive work of medical personnel during bone diagnosis.  MATLAB 9.7.0 programing tool was used for the implementation of the work. The results of X-ray bone segmentation revealed that active contour model using snake model showed the best performance in detecting boundaries and contours of regions of interest when used in segmenting Femur bone image than the other medical image segmentation approaches implemented in the work.

Cite This Paper

Folasade Olubusola Isinkaye, Abiodun Gabriel Aluko, Olayinka Ayodele Jongbo, " Segmentation of Medical X-ray Bone Image Using Different Image Processing Techniques", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.13, No.5, pp. 27-40, 2021.DOI: 10.5815/ijigsp.2021.05.03

Reference

[1]Saini, S., & Arora, K. (2014). A study analysis on the different image segmentation techniques. International Journal of Information & Computation Technology, 4(14), 1445-1452.

[2]Shiv Gehlot, John Deva Kumar,"The Image Segmentation Techniques", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.9, No.2, pp.9-18, 2017.DOI: 10.5815/ijigsp.2017.02.02.

[3]Kumar, S., Lenin , F., Muthukumar, s., Ajay , K., and Sebastian , V. (2018). A voyage on medical image segmentation algorithms. Computational Life Sciences and Smarter Technological Advancement, Special Issue: 75-87.

[4]L.Sankari,C.Chandrasekar,"A New Enhanced Semi Supervised Image Segmentation Using Marker as Prior Information", IJIGSP, vol.4, no.1, pp.51-56, 2012..

[5]Dorgham, O., Osama , M., Stephen , D., Laycock, O., and Mark, H. (2012). GPU accelerated generation of digitally reconstructed radiographs for 2-D/3-D image registration. IEEE Transactions on biomedical engineering, 59(9), 2594-2603.

[6]Mandeep , K., and Jindal, G. (2011). Medical Image Segmentation using Marker Controlled Watershed Transformation. International journal of Computer Science and Technology, 2(1), 1-6.

[7]Huiyu, Z., Jiahua, W., and Jianguo, Z. (2010). Digital Image Processing Part II, 2nd edition. London, United Kingdom: Ventus Publishing APS.

[8]Farzaneh Nikroorezaei, Somayeh Saraf Esmaili, " Application of Models based on Human Vision in Medical Image Processing: A Review Article", International Journal of Image, Graphics and Signal Processing (IJIGSP), Vol.11, No.12, pp. 23-28, 2019.DOI: 10.5815/ijigsp.2019.12.03.

[9]Osama , D., Mohammad , J., Mohammad , H., and Ammar , A. (2018). Proposed Method for Automatic Segmentation of Medical Images. International Arab Confrence on Infornation Technology, Lebanon: IEEE.pp. 1-9

[10]Zhao, C., Han, J., Jia, Y., Fan, L., and Gou, F. (2018). Versatile Framework for Medical Image Processing and Analysis with Application to Automatic Bone Age Assessment. Journal of Electrical and Computer Engineering, 1(2), 1-14.

[11]Senthilkumaran , N., and Vaithegi , S. (2016). Image Segmentation by Using Thresholding Techniques for Medical Images. International Journal of Computer Science and Engineering, 6(1), 1-13.

[12]Anithadevi, D., and Perumal, K. (2016). A Hybrid Approach Based Segmentation Technique for Brain Tumor in MRI Images. International Jpournal of Signal and Image Processing, 17(1), 1-10.

[13]Farmaha, I., Banas, M., Lukashchuk, V., and Farmaha, T. (2019). Wound image segmentation using clustering based algorithms. New Trends in Production Engineering, 2(1), 570-578.

[14]Bansal, S., Kaur, S., and Kaur, N. (2019). Enhancement in Brain image segmentation using Swarm Ant Lion Algorithm. International Journal of Innovative Technology and Exploring Engineerin, 8(10), 1-6.

[15]Tian, Z., Si, X., Zheng, Y., Chen, Z., and Li, X. (2020). Multi step medical image segmentation based on reinforcement learning. Journal of Ambient Intelligence and Humanized Computing, 1(2), 1-12.

[16]Kant, S., and Bala, S. (2020). Dense Dilate Inception Network for Medical Image Segmentation. international Journal of Advanced Computer Science and Applications, 11(11), 785-793.

[17]Ouyang, C., Biffi, C., Chen , C., Kart, T., Qiu, H., and Rueckert, D. (2020). Self-Supervision with Superpixels: Training Few-shot Medical Image Segmentation without Annotation. Computer Vision and Pattern Recognition, 2(1) 1-19.

[18]Haider, W., Malik, M., Raza, M., Wahab, A., Khan, I., Zia, U., et al. (2012). A hybrid method for edge continuity based on Pixel Neighbors Pattern Analysis (PNPA) for remote sensing satellite. International Journal of Communications, Network and System Sciences, 5(1), 624-630.

[19]Samet, R., Amrahov, E., and Ziroglu, A. (2012). Fuzzy rule-based image segmentation technique for rock thin section images. 3rd International Conference on Image Processing Theory, Tools and Applications (pp. 402-406). IEEE: Istanbul,Turkey.

[20]Khokher, M., Ghafoor, A., and Siddiqui, A. (2012). Image segmentation using fuzzy rule based system and graph cuts. 12th International Conference on Control Automation Robotics & Vision, Guangzhou, China : IEEE. 1148-1153.

[21]Li, Y., Bai, X., Jao, L., and Xue, Y. (2017). Partitioned-cooperative quantum-behaved particle swarm optimization based on multilevel thresholding applied to medical image segmentation. Journal of Applied Soft Computing, 56(1), 345-356.

[22]Rouhi, R., Jafari , M., Kasaei, S., and Keshavarzian, P. (2014). Benign and malignant breast tumors classification based on region growing and CNN segmentation. Expert Systems With Application, 42(3), 990-1002.

[23]Tyan, Y., Wu , M., Chin, C., Kuo, Y., Lee, M., and Chang, H. (2014). Ischemic stroke detection system with a computer-aided diagnostic ability using an unsupervised feature perception enhancement method. International Journal of Biomedical Imaging, 12(2), 1-6.

[24]Aganj, I., Harisinghani, M., Weisslede, R., and Fischi, B. (2018). Unsupervised Medical Image Segmentation Based on the Local Center of Mass. Journal of Scientific Report, 8(1), 1-8.

[25]Dar, S. A., and Padha, D. (2019). Medical Image Segmentation: A Review of Recent Techniques, Advancements and a Comprehensive Comparison. International Journal of sComputer Sciences and Engineering, 7(7), 114-124.

[26]Li, C., Liu, L., Sun, X., Zhao, J., and Yin, J. (2019). Image segmentation based on fuzzy clustering with cellular automata and features weighting. EURASIP Journal on Image and Video Processing, 2019(1), 1-11.

[27]Lei, T., Liu, P., Jia, X., Zhang, X., Meng, H., and Nandi, A. K. (2019). Automatic fuzzy clustering framework for image segmentation. IEEE Transactions on Fuzzy Systems, 28(9), 2078-2092 

[28]Qureshi, M. N., and Ahamad, M. V. (2018). An improved method for image segmentation using K-means clustering with neutrosophic logic. Procedia computer science, 132, 534-540.

[29]Bora, D. J., and Gupta, A. K. (2015). A novel approach towards clustering based image segmentation. arXiv preprint arXiv:1506.01710.

[30]Hassan, M. R., Ema, R. R., and Islam, T. (2017). Color image segmentation using automated K-means clustering with RGB and HSV color spaces. Global Journal of Computer Science and Technology.

[31]Saravanan, M., Kalaivani, B., and Geethamani, R. (2017). Image Segmentation Using K-means clustering based thresholding algorithm. International journal of advanced technology in Engineering and science, 5(2).

[32]Panda, S. (2015). Color Image Segmentation Using K-means Clustering and Thresholding Technique. Interntional journal of ESC, 1132-1136.

[33]Dubey, S. R., Dixit, P., Singh, N., and Gupta, J. P. (2013). Infected fruit part detection using K-means clustering segmentation technique.

[34]Inbarani H, Hannah, and Ahmad Taher Azar. "Leukemia image segmentation using a hybrid histogram-based soft covering rough k-means clustering algorithm." Electronics 9, no. 1 (2020): 188.

[35]Wang, Yun Shi Xuqi, and Shanwen Zhang. "Plant Disease Leaf Image Segmentation Using K-Means Clustering Based on Internet of Things." (2016).

[36]Shimodaira, H. (2017). Automatic color image segmentation using a square elemental region-based seeded region growing and merging method. arXiv preprint arXiv:1711.09352.

[37]Yuan, Y., Chen, D., and Yan, L. (2015). Interactive three-dimensional segmentation using region growing algorithms. Journal of Algorithms & Computational Technology, 9(2), 199-213.

[38]Kaur, G., and Jindal, S. Region growing image segmentation on large datasets using gpu. International journal, 15(14).

[39]Jaber, I. M., Jeberson, W., and Bajaj, H. K. (2006). Improved region growing based breast cancer image segmentation. International Journal of Computer Applications, 975, 888

[40]Kansal, S., and Jain, P. (2015). Automatic seed selection algorithm for image segmentation using region growing. International Journal of Advances in Engineering & Technology, 8(3), 362.

[41]Shewale, T. P., and Patil, S. B. (2016). Detection of brain tumor based on segmentation using region growing method. Int. J. Eng. Innov. Res, 5(2), 173-176.

[42]Biratu, E. S. S., Schwenker, F., Debelee, T. G. G., Kebede, S. R. R., Negera, W. G. G., and Molla, H. T. T. (2021). Enhanced Region Growing for Brain Tumor MR Image Segmentation. Journal of Imaging, 7(2), 22.

[43]Udayakumar, E., Santhi, S., Gowrishankar, R., Ramesh, C., and Gowthaman, T. (2016). Region growing image segmentation for newborn brain MRI. Biotechnology: An Indian Journal Trade Sci Inc, 12, 1-8.

[44]Jain, P. K., and Susan, S. (2013, December). An adaptive single seed based region growing algorithm for color image segmentation. In 2013 Annual IEEE India Conference (INDICON) (pp. 1-6). IEEE.

[45]Thukral, T., Verma, S., Gaur, S., and Tyagi, N. Segmentation of Lung Cancer using Mark Region Growing and Median Filter. International Journal of Computer Applications, 975, 8887.

[46]Padmapriya, B., Kesavamurthi, T., and Ferose, H. W. (2012). Edge based image segmentation technique for detection and estimation of the bladder wall thickness. Procedia engineering, 30, 828-835.

[47]Kharrofa, S. H. A. (2018). Segmentation of Images by Using Edges Detection Techniques. Iraqi Journal of Information Technology. V, 8(3).

[48]Cao, J., Chen, L., Wang, M., and Tian, Y. (2018). Implementing a parallel image edge detection algorithm based on the Otsu-canny operator on the Hadoop platform. Computational intelligence and neuroscience, 2018.

[49]Mittal, M., Verma, A., Kaur, I., Kaur, B., Sharma, M., Goyal, L. M., ... and Kim, T. H. (2019). An efficient edge detection approach to provide better edge connectivity for image analysis. IEEE Access, 7, 33240-33255

[50]Yao, Y. (2016). Image segmentation based on Sobel edge detection. In 2016 5th International Conference on Advanced Materials and Computer Science (ICAMCS 2016). Atlantis Press.

[51]Aslam, A., Khan, E., and Beg, M. S. (2015). Improved edge detection algorithm for brain tumor segmentation. Procedia Computer Science, 58, 430-437.

[52]Xu, Q., Varadarajan, S., Chakrabarti, C., and Karam, L. J. (2014). A distributed canny edge detector: algorithm and FPGA implementation. IEEE Transactions on Image Processing, 23(7), 2944-2960.

[53]Chithambaram, T., and Perumal, K. (2016). Edge detection algorithms using brain tumor detection and segmentation using artificial neural network techniques. International Research Journal of Advanced Engineering and Science, 1(3), 135-140.

[54]Asmaidi, A., Putra, D. S., and Risky, M. M. (2019). Implementation of sobel method based edge detection for flower image segmentation. Sinkron: Jurnal dan Penelitian Teknik Informatika, 3(2), 161-166.

[55]Ratnam, S. S., Kumar, D. S., and Madhukar, K. (2015). Implementation of Edge Detection Technique for Identification and Evaluation of Brain Tumor Metrics in MR Image through Lab VIEW.

[56]Dash, J., and Bhoi, N. (2018, January). Retinal blood vessel segmentation using Otsu thresholding with principal component analysis. In 2018 2nd International Conference on Inventive Systems and Control (ICISC) (pp. 933-937). IEEE

[57]Wang, W., Duan, L., and Wang, Y. (2017). Fast image segmentation using two-dimensional Otsu based on estimation of distribution algorithm. Journal of Electrical and Computer Engineering, 2017.

[58]Mapayi, T., Viriri, S., and Tapamo, J. R. (2015). Adaptive thresholding technique for retinal vessel segmentation based on GLCM-energy information. Computational and mathematical methods in medicine, 2015.

[59]Jang, J. W., Lee, S., Hwang, H. J., and Baek, K. R. (2013, October). Global thresholding algorithm based on boundary selection. In 2013 13th International Conference on Control, Automation and Systems (ICCAS 2013) (pp. 704-706). IEEE.

[60]Vijay, P. P., and Patil, N. C. (2016). Gray scale image segmentation using OTSU Thresholding optimal approach. Journal for Research, 2(05).

[61]Telgad, R., Siddiqui, A. M., and Deshmukh, P. D. (2014). Fingerprint image segmentation using global thresholding. International Journal of Current Engineering and Technology, 4(1), 216-9.

[62]Gurung, A., and Tamang, S. L. (2019). Image segmentation using multi-threshold technique by histogram sampling. arXiv preprint arXiv:1909.05084.

[63]Srinivas, C. H. V. V. S., Prasad, M. V. R. V., and Sirisha, M. Remote Sensing Image Segmentation using OTSU Algorithm. International Journal of Computer Applications, 975, 8887.

[64]Pai, P. Y., Chang, C. C., Chan, Y. K., Tsai, M. H., and Guo, S. W. (2012). An Image Segmentation-Based Thresholding Method. Journal of Imaging Science and Technology, 56(3), 30503-1.

[65]Patil, A. B., and Shaikh, J. A. (2016). OTSU thresholding method for flower image segmentation. International Journal of Computational Engineering Research (IJCER), 6(05).

[66]Hemalatha, R., Thamizhvani, T., Dhivya, J., Joseph, J., Babu, B., and Chandrasekaran, R. (2018). Active Contour Based Segmentation Techniques forMedical Image Analysis. Robert Koprowski, Intech Open. http://dx.doi.org/10.5772/intechopen.7457.