Abstract
Image segmentation, a technology that divides images into parts, is an important technique in image processing, and is used in various disciplines, such as remote sensing, medical imaging, and industrial fields. Several methods and known algorithms are used for image segmentation. This paper proposes a new algorithm for image segmentation that is based on statistical properties. The new algorithm depends on colour contrast between regions of the image, and uses a mathematical equation based on statistical properties (the maximum value, minimum value, and standard deviation) to automatically calculate a global threshold. We find that the algorithm successfully performs image segmentation when applied to two different test images.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern Syst 9:62–66
Chouhan SS, Kaul A, Singh UP (2019) Image segmentation using computational intelligence techniques: review. Arch Comput. Method E 26:533–596
Rangel-Fonseca P, Vieyra AG, Hernández DM, Wilson MC, Williams DR, Rossi EA (2013) Automated segmentation of retinal pigment epithelium cells in fluorescence adaptive optics images. J Opt Soc Am A 30:2595–2604
Hesamian MH, Jia W, He X, Kennedy P (2019) Deep learning techniques for medical image segmentation: achievements and challenges. J Digit Imaging 32:582–596
Rui T, Huang TS, Chang S (1999) Image retrieval: current techniques, promising directions and open issues. J Vis Commmun Image R 10:39–62
Chakraborty D, Sen GK, Hazra S (2009) High-resolution satellite image segmentation using Hölder exponents. J Earth Syst Sci 118:609–617
Brill MH (1990) Image segmentation by object color: a unifying framework and connection to color constancy. J Opt Soc Am A 7:2041–2047
Rignot E, Chellappa R (1991) Image segmentation by object color: a unifying framework and connection to color constancy. J Opt Soc Am A 8:1499–1509
Zaitoun NM, Aqel MJ (2015) High-resolution satellite image segmentation using Hölder exponents. Procedia Comput Sci 65:797–806
Mal S, Kumar A (2018) Heuristic approach for finding threshold value in image segmentation. Adv Intell Syst Comput 937:45–53
Cheng HD, Jiang XH, Wang J (2002) Color image segmentation based on homogram thresholding and region merging. Pattern Recognit 25:373–393
Liao PS, Chen TS, Chung PC (2001) A fast algorithm for multilevel thresholding. J Inf Sci Eng 17:713–727
Yang J, Chen Y, Hsu W (1994) Adaptive thresholding algorithm and its hardware implementation. Pattern Recogn Lett 15:141–150
Kittler J, Illingworth J (1986) Minimum error thresholding. Pattern Recogn 19:41–47
Xiao Y, Cao ZG, Zhang TX (2008) Entropic thresholding based on gray-level spatial correlation histogram. Presented at the Nineteenth International Conference on Pattern Recognition, Tampa- Florida, USA, 8–11 Dec 2008
Chatterjee RK, Kar A (2017) Optimal global threshold estimation using statistical change-point detection. Presented at the International MultiConference of Engineers and Computer Scientists, Hong Kong, 15–17 Mar 2017
Telgad R, Siddiqui AM (2016) Fingerprint image segmentation using global thresholding. Int J Curr Eng Technol Lett 4:165–171
Song R, Zhang Z, Liu H (2017) Edge connection based Canny edge detection algorithm. Pattern Recogn Image Anal 27:740–747
Jang JW, Lee S, Hwang HJ, Baek KR (2013) Global thresholding algorithm based on boundary selection. Presented at the thirteenth international conference on control, automation and systems, Gwangju, Korea, 20–23 Oct 2013
Malakar S, Mohanta D, Sarkar R, Das N, Basu DK (2012) A new global thresholding approach for document image binarization. Int J Inf Process 6:48–59
Al-Saleh M, Yousif A (2009) Properties of the standard deviation that are rarely mentioned in classrooms. Austr J Stat 38:48–59
Bataineh B, Abdullah SNHS, Omar K (2011) An adaptive local binarization method for document images based on a novel thresholding method and dynamic windows. Pattern Recogn Lett 32:1805–1813
Alwazzan MJ, Ismael MA, Hussain MK (2019) Brain tumour isolation in MRI images based on statistical properties and morphological process techniques. J Phys: Conf Ser 1279:012018
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Alwazzan, M.J., Alkhfagi, A.O., Alattar, A.M. (2021). Image Segmentation Algorithm Based on Statistical Properties. In: Kumar, R., Quang, N.H., Kumar Solanki, V., Cardona, M., Pattnaik, P.K. (eds) Research in Intelligent and Computing in Engineering. Advances in Intelligent Systems and Computing, vol 1254. Springer, Singapore. https://doi.org/10.1007/978-981-15-7527-3_32
Download citation
DOI: https://doi.org/10.1007/978-981-15-7527-3_32
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-7526-6
Online ISBN: 978-981-15-7527-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)