Abstract
Magnetic resonance images are relevant sources of information for detecting and diagnosing a large number of illnesses and abnormalities. Segmentation of digital images helps to classify the pixels in the different regions according to their intensity level. Segmentation of digital images implemented on magnetic resonance images can help experts to improve the performance of evaluations and make a correct differential diagnosis to indicate the appropriate treatment. This chapter proposes the use of MFO, SCA and SFO algorithms to search for 2, 3, 4, 5, 8, 16 and 32 threshold values using minimum cross entropy function to segment prostatic magnetic resonance images, and implementing statistical metrics like PSNR, SSIM, and FSIM to measure the quality of a segmented image, quantified and establish the proper comparison frame, so visual comparison is not enough. The approach is tested on a set of benchmark images to demonstrate that the segmentation of digital images can improve the detection of prostatic abnormalities or illnesses.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bezdek, J., Hall, L., Clarke, L, Review of MR image segmentation techniques using pattern recognition. Med. Phys. 1033–1048 (1993)
Sociedad Mexicana de Urología Colegio de Profesionistas AC, Sociedad Mexicana de Urología (2020). Recuperado el 25 de Abril de 2020, de La prostata y sus enfermedades: https://www.smu.mx/Pacientes.php
Suzukii, H., Toriwakii, J.-I., automatic segmentation of head mri images by knowledge guided thresholding, in Compurerized Medrcol Imaging and Graphics, vol. 15, issue No. 4, pp. 233–240 [8]
Hinojosa, S., Pajares, G., Cuevas, E., Ortega-Sanchez, N., Thermal image segmentation using evolutionary computation techniques, in Advances in Soft Computing and Machine Learning in Image Processing, pp 63–88 , 26 (2017)
Yang, X.-S., A new metaheuristic bat-inspired algorithm, in Nature Inspired Cooperative Strategies for Optimization, vol. 10 (2010), pp 65–74
D.E. Goldberg, J.H. Holland, Genetic algorithms and machine learning. Mach. Learn. 3(2), 95–99 (1988). https://doi.org/10.1023/A:1022602019183
Mirjalili, S. (2015). Moth-Flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl. Based Syst. 45
J.H. Holland, Outline for a Logical theory of adaptive systems. J. ACM 9, 297–314 (1962)
Mirjalili, S. SCA: A sine cosine algorithm for solving optimization problems. Knowl. Based Syst. (2016)
S. Hinojosa, O. Avalos, D. Oliva, E. Cuevas, G. Pajares, D. Zaldivar et al., Unassisted Thresholding based on multi-objective evolutionary algorithms. Knowl.-Based Syst. 159, 221–232 (2018)
Mirjalili, S. (22 de 05 de 2018). Moth-flame Optimization (MFO) Algorithm. Recuperado el 20 de 03 de 2020. de https://la.mathworks.com/matlabcentral/fileexchange/52269-moth-flame-optimization-mfo-algorithm?s_tid=srchtitle
S. Arora, Multilevel thresholding for image segmentation through a fast statistical recursive algorithm. Pattern Recogn. Lett. 29(2), 119–125 (2008)
Gomes, G. (10 de 10 de 2018). Sunflower optimization (sfo) algorithm. Recuperado el 28 de 03 de 2020. de https://la.mathworks.com/matlabcentral/fileexchange/69076-sunflower-optimization-sfo-algorithm?s_tid=srchtitle
Ferreira Gomes, G., Simões da Cunha Jr., S., Ancelotti Jr., A.C., A Sunflower Optimization (SFO) Algorithm Applied to Damage, vol. 8 (Springer Nature, 2018)
C.H. Li, C.K. Lee, Minimum cross entropy thresholding. Pattern Recogn. 26(4), 617–625 (1993)
S. Kullback, Information Theory and Statistics (Wiley, New York, 1959).
R.C. Gonzalez, R.E. Woods, Digital Image Processing (Pearson, Prentice-Hall, New Jersey, 1992).
Diego, O., Salvador, H., Osuna-Enciso, V., Cuevas, E., Pérez-Cisneros, M., Sanchez-Ante, G., Image segmentation by minimum cross entropy using evolutionary methods. Soft Comput. 1–20 (2017).https://doi.org/10.1007/s00500-017-2794-1
The Ferenc Jolesz National Center for Image Guided Therapy, Harvard Medical School, Brighman Health Hospital, Prostate MR Image Database (2020). Retrieved 28 Feb 2020 from https://prostatemrimagedatabase.com/Database/000004/00002/009/index.html
P. Ghamisi, M.S. Couceiro, J.A. Benediktsson, N.M. Ferreira, An efficient method for segmentation of images based on fractional calculus and natural selection. Expert Syst. Appl. 39(16), 12407–12417 (2012)
O. Il-Seok, L. J.-S.-R. , Hybrid genetic algorithms for feature selection. IEEE Trans Pattern Anal Mach Intell 26, 1424–1437 (2004). https://doi.org/10.1109/TPAMI.2004.105
Z. Wang, A.C. Bovik, H.R. Sheikh, E.P. Simoncelli, Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004). https://doi.org/10.1109/TIP.2003.819861
D. Zhang, A feature similarity index for image quality assessment. IEEE Trans. Image Process. 20(8), 2378–2386 (2011). https://doi.org/10.1109/TIP.2011.2109730
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Appendix: MRIs Detailed Series Header Information
Appendix: MRIs Detailed Series Header Information
-
sh.ExamDescription = ‘PROSTATE STAGING’;
-
sh.SeriesDescription = ‘T1 AXIAL (COIL OUT) 2 STACKS’;
-
sh.SequenceName = ‘MEMP’;
-
sh.PatientAge = 60;
-
sh.PatientWeight = 80,000;
-
sh.FieldStrength = 15,000;
-
sh.SeriesDate = ‘Day 1′;
-
sh.RepetitionTime = 700,000;
-
sh.EchoTime = 8000;
-
sh.Excitations = 2;
-
sh.FlipAngle = 90;
-
sh.FrequencyDirection = ‘Row’;
-
sh.SliceThickness = 5;
-
sh.MatrixSize = [256 256];
-
sh.PixelSize = [1.093750 1.093750];
-
sh.Fov = [280 280];
-
sh.PixelValueOffset = 0;
-
sh.NumberOfImages = 46;
-
sh.Gamma = 1;
-
sh.ImagePositionPatient1 = [−139.453125 − 156.053131 211.800003];
-
sh.ImageOrientationPatient1 = [1 0 0 0 1 0];
-
sh.ImagePositionPatientN = [−139.453125 − 156.053131 − 58.200001];
-
sh.ImageOrientationPatientN = [1 0 0 0 1 0];
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Zárate, O., Záldivar, D. (2021). Cross Entropy Based Thresholding Segmentation of Magnetic Resonance Prostatic Images Using Metaheuristic Algorithms. In: Oliva, D., Houssein, E.H., Hinojosa, S. (eds) Metaheuristics in Machine Learning: Theory and Applications. Studies in Computational Intelligence, vol 967. Springer, Cham. https://doi.org/10.1007/978-3-030-70542-8_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-70542-8_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-70541-1
Online ISBN: 978-3-030-70542-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)