Local contextual information and Gaussian function induced fuzzy clustering algorithm for brain MR image segmentation and intensity inhomogeneity estimation
•A Fuzzy clustering algorithm using local contextual information and Gaussian function is devised for bias field and brain MR image segmentation.•The algorithm works directly on the MR signal model without transforming it into a log-transformed domain.•We have used Gaussian surface to compensate the...
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| Published in: | Applied soft computing Vol. 68; pp. 586 - 596 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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01.07.2018
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| ISSN: | 1568-4946, 1872-9681 |
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| Abstract | •A Fuzzy clustering algorithm using local contextual information and Gaussian function is devised for bias field and brain MR image segmentation.•The algorithm works directly on the MR signal model without transforming it into a log-transformed domain.•We have used Gaussian surface to compensate the effect of bias field and the local contextual information for final labeling of pixels.•We have introduced global and local membership functions for each pixel to define its belongingness into a tissue region.•Simulation results on real-patient and simulated brain MR images demonstrate its effectiveness and superiority over other similar methods.
This paper presents a fuzzy clustering algorithm, where local contextual information and a Gaussian function are incorporated into the objective function, for simultaneous brain MR image segmentation and intensity inhomogeneity estimation. In doing so, for each pixel, we define a local contextual information, which actually defines its association among the other neighboring pixels based on intensity distribution. In particular, this information defines the possibility of the pixel to belong into a specific tissue type. Whereas, for each tissue region, a Gaussian surface is defined to estimate the intensity inhomogeneity (IIH) using the local image gradients, which are believed to be caused by the IIH. We use this Gaussian surface to compensate the effect of IIH. In addition, for each pixel, we have introduced global and local membership functions, which in combined in association with the other parameters are responsible for generation of cluster prototypes. The IIH of the entire image region is iteratively removed from the image and the final segmentation result is obtained based on the global membership values. The simulation results on two benchmarks brain MR image databases and four volumes of real-patient brain MR image data show its efficiency and superiority over other fuzzy-based clustering algorithms. |
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| AbstractList | •A Fuzzy clustering algorithm using local contextual information and Gaussian function is devised for bias field and brain MR image segmentation.•The algorithm works directly on the MR signal model without transforming it into a log-transformed domain.•We have used Gaussian surface to compensate the effect of bias field and the local contextual information for final labeling of pixels.•We have introduced global and local membership functions for each pixel to define its belongingness into a tissue region.•Simulation results on real-patient and simulated brain MR images demonstrate its effectiveness and superiority over other similar methods.
This paper presents a fuzzy clustering algorithm, where local contextual information and a Gaussian function are incorporated into the objective function, for simultaneous brain MR image segmentation and intensity inhomogeneity estimation. In doing so, for each pixel, we define a local contextual information, which actually defines its association among the other neighboring pixels based on intensity distribution. In particular, this information defines the possibility of the pixel to belong into a specific tissue type. Whereas, for each tissue region, a Gaussian surface is defined to estimate the intensity inhomogeneity (IIH) using the local image gradients, which are believed to be caused by the IIH. We use this Gaussian surface to compensate the effect of IIH. In addition, for each pixel, we have introduced global and local membership functions, which in combined in association with the other parameters are responsible for generation of cluster prototypes. The IIH of the entire image region is iteratively removed from the image and the final segmentation result is obtained based on the global membership values. The simulation results on two benchmarks brain MR image databases and four volumes of real-patient brain MR image data show its efficiency and superiority over other fuzzy-based clustering algorithms. |
| Author | Adhikari, Sudip Kumar Mahata, Nabanita Sing, Jamuna Kanta Kahali, Sayan |
| Author_xml | – sequence: 1 givenname: Nabanita surname: Mahata fullname: Mahata, Nabanita email: mahatanabanita1990@gmail.com organization: Dept. of Computer Science & Engineering, Jadavpur University, Kolkata 700032, India – sequence: 2 givenname: Sayan surname: Kahali fullname: Kahali, Sayan email: sayankahaliiway@gmail.com organization: Dept. of Computer Science & Engineering, Jadavpur University, Kolkata 700032, India – sequence: 3 givenname: Sudip Kumar surname: Adhikari fullname: Adhikari, Sudip Kumar email: sudipadhikari@ieee.org organization: Cooch Behar Govt. Engineering College, Ghughumari, Cooch Behar, India – sequence: 4 givenname: Jamuna Kanta orcidid: 0000-0003-1006-6006 surname: Sing fullname: Sing, Jamuna Kanta email: jksing@ieee.org organization: Dept. of Computer Science & Engineering, Jadavpur University, Kolkata 700032, India |
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| Cites_doi | 10.1016/j.eswa.2014.01.021 10.1109/TMI.2006.891486 10.1007/s11760-014-0689-5 10.1016/j.cviu.2013.05.001 10.1016/j.patcog.2011.07.012 10.1109/42.996338 10.1016/j.patrec.2013.04.021 10.1016/j.compmedimag.2005.10.001 10.1002/cem.2825 10.1002/cem.2728 10.1016/j.patcog.2006.07.011 10.1109/TFUZZ.2004.840099 10.1016/j.procs.2016.09.407 10.1016/j.asoc.2017.07.001 10.1109/TMI.2003.816956 10.1109/TST.2014.6961028 10.1016/j.asoc.2015.05.038 10.1016/j.patrec.2008.03.012 10.1016/j.media.2005.09.004 10.1155/IJBI/2006/49515 10.1016/j.dsp.2013.07.005 10.1080/02564602.2014.906861 |
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| References | Balafar, Ramli, Saripan, Mashohor (bib0015) 2010; 33 Wu (bib0115) 2012; 45 Adhikari, Sing, Basu, Nasipuri (bib0080) 2015; 34 Adhikari, Sing, Basu, Nasipuri, Saha (bib0025) 2015; 9 Cocosco, Kollokian, Kwan, Evans (bib0120) 1997; 5 Liew, Yan (bib0045) 2003; 22 Cai, Chen, Zhang (bib0055) 2007; 40 Vovk, Pernujs, Likar (bib0005) 2007; 26 Benaichouche, Oulhadj, Siarry (bib0070) 2013; 23 Hou (bib0010) 2006; 2006 Pal, Pal, Keller (bib0125) 2005; 13 Wang, Song, Soh, Sim (bib0065) 2013; 117 Kahali, Adhikari, Sing (bib0030) 2016; 30 El-Dahshan, Mohsen, Revett, Salem (bib0105) 2014; 41 Liu, Li, Wang, Wu, Liu, Pan (bib0100) 2014; 19 Kahali, Adhikari, Sing (bib0085) 2017; 60 Ahmed, Yamany, Mohamed, Farag, Moriarty (bib0040) 2002; 21 Qiu, Xiao, Yu, Han, Iqbal (bib0060) 2013; 34 Iṣin, Direkoğlu, Şah (bib0110) 2016; 102 Sing, Adhikari, Basu (bib0075) 2015; 29 The Internet Brain Segmentation Repository (IBSR) Database. Available at Norouzi, Rahim, Altameen, Saba, Rad, Rehman, Uddin (bib0095) 2014; 5 . Belaroussia, Millesb, Carmec, Zhua, Benoit-Cattin (bib0020) 2006; 10 Bezdek (bib0035) 1981 Chuang, Tzeng, Chen, Wu, Chen (bib0050) 2006; 30 Liao, Lin, Li (bib0090) 2008; 29 Adhikari (10.1016/j.asoc.2018.04.031_bib0025) 2015; 9 Liu (10.1016/j.asoc.2018.04.031_bib0100) 2014; 19 Norouzi (10.1016/j.asoc.2018.04.031_bib0095) 2014; 5 Ahmed (10.1016/j.asoc.2018.04.031_bib0040) 2002; 21 Bezdek (10.1016/j.asoc.2018.04.031_bib0035) 1981 Cai (10.1016/j.asoc.2018.04.031_bib0055) 2007; 40 Iṣin (10.1016/j.asoc.2018.04.031_bib0110) 2016; 102 Pal (10.1016/j.asoc.2018.04.031_bib0125) 2005; 13 El-Dahshan (10.1016/j.asoc.2018.04.031_bib0105) 2014; 41 Vovk (10.1016/j.asoc.2018.04.031_bib0005) 2007; 26 Hou (10.1016/j.asoc.2018.04.031_bib0010) 2006; 2006 Chuang (10.1016/j.asoc.2018.04.031_bib0050) 2006; 30 Benaichouche (10.1016/j.asoc.2018.04.031_bib0070) 2013; 23 10.1016/j.asoc.2018.04.031_bib0130 Liew (10.1016/j.asoc.2018.04.031_bib0045) 2003; 22 Wang (10.1016/j.asoc.2018.04.031_bib0065) 2013; 117 Qiu (10.1016/j.asoc.2018.04.031_bib0060) 2013; 34 Liao (10.1016/j.asoc.2018.04.031_bib0090) 2008; 29 Kahali (10.1016/j.asoc.2018.04.031_bib0030) 2016; 30 Cocosco (10.1016/j.asoc.2018.04.031_bib0120) 1997; 5 Belaroussia (10.1016/j.asoc.2018.04.031_bib0020) 2006; 10 Kahali (10.1016/j.asoc.2018.04.031_bib0085) 2017; 60 Sing (10.1016/j.asoc.2018.04.031_bib0075) 2015; 29 Adhikari (10.1016/j.asoc.2018.04.031_bib0080) 2015; 34 Balafar (10.1016/j.asoc.2018.04.031_bib0015) 2010; 33 Wu (10.1016/j.asoc.2018.04.031_bib0115) 2012; 45 |
| References_xml | – volume: 13 start-page: 517 year: 2005 end-page: 530 ident: bib0125 article-title: A possibilistic fuzzy c-means clustering algorithm publication-title: IEEE Trans. Fuzzy Systems – volume: 26 start-page: 405 year: 2007 end-page: 421 ident: bib0005 article-title: A review of methods for correction of intensity inhomogeneity in MRI publication-title: IEEE Trans. Med. Imaging – volume: 45 start-page: 407 year: 2012 end-page: 415 ident: bib0115 article-title: Analysis of parameter selections for fuzzy c-means publication-title: Pattern Recogn. – volume: 22 start-page: 1063 year: 2003 end-page: 1075 ident: bib0045 article-title: An adaptive spatial fuzzy clustering algorithm for 3-D MR image segmentation publication-title: IEEE Trans. Med. Imaging – volume: 5 year: 1997 ident: bib0120 article-title: Brainweb online interface to a 3D MRI simulated brain database publication-title: Neuroimage – volume: 9 start-page: 1945 year: 2015 end-page: 1954 ident: bib0025 article-title: A nonparametric method for intensity inhomogeneity correction in MRI brain images by fusion of Gaussian surfacesl publication-title: Signal Image Video Process. – volume: 30 start-page: 602 year: 2016 end-page: 620 ident: bib0030 article-title: On estimation of bias field in MRI images: polynomial vs Gaussian surface fitting method publication-title: J. Chemom. – reference: The Internet Brain Segmentation Repository (IBSR) Database. Available at: – year: 1981 ident: bib0035 article-title: Pattern Recognition with Fuzzy Objective Function Algorithms – volume: 2006 start-page: 1 year: 2006 end-page: 11 ident: bib0010 article-title: A review on MR image intensity inhomogeneity correction publication-title: Int. J. Biomed. Imaging – volume: 21 start-page: 193 year: 2002 end-page: 199 ident: bib0040 article-title: A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data publication-title: IEEE Trans. Med. Imaging – volume: 30 start-page: 9 year: 2006 end-page: 15 ident: bib0050 article-title: Fuzzy C-means clustering with spatial information for image Segmentation publication-title: Comput. Med. Imaging Graph. – volume: 19 start-page: 578 year: 2014 end-page: 595 ident: bib0100 article-title: A survey of MRI-based brain tumor segmentation methods publication-title: Tsinghua Sci. Technol. – volume: 117 start-page: 1412 year: 2013 end-page: 1420 ident: bib0065 article-title: An adaptive spatial information-theoretic fuzzy clustering algorithm for image segmentation publication-title: Comput. Vision Image Underst. – volume: 5 start-page: 199 year: 2014 end-page: 213 ident: bib0095 article-title: Medical image segmentation methods, algorithms, and applications publication-title: IETE Tech. Rev. – volume: 34 start-page: 758 year: 2015 end-page: 769 ident: bib0080 article-title: Conditional spatial fuzzy C-means clustering algorithm for segmentation of MRI images publication-title: Appl. Soft Comput. – volume: 10 start-page: 234 year: 2006 end-page: 246 ident: bib0020 article-title: Intensity non-uniformity correction in MRI: Existing methods and their validation publication-title: Med. Image Anal. – reference: . – volume: 41 start-page: 5526 year: 2014 end-page: 5545 ident: bib0105 article-title: Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm publication-title: Expert Syst. Appl. – volume: 29 start-page: 1580 year: 2008 end-page: 1588 ident: bib0090 article-title: MRI brain image segmentation and bias field correction based on fast spatially constrained kernel clustering approach publication-title: Pattern Recognit. Lett. – volume: 34 start-page: 1329 year: 2013 end-page: 1338 ident: bib0060 article-title: A modified interval type-2 fuzzy C-means algorithm with application in MR image segmentation publication-title: Pattern Recognit. Lett. – volume: 23 start-page: 390 year: 2013 end-page: 1400 ident: bib0070 article-title: Improved spatial fuzzy c-means clustering for image segmentation using PSO initialization, Mahalanobis distance and post-segmentation correction publication-title: Digital Signal Process. – volume: 33 start-page: 261 year: 2010 end-page: 274 ident: bib0015 article-title: Review of brain MRI image segmentation methods publication-title: J. Artif. Intell. – volume: 60 start-page: 312 year: 2017 end-page: 327 ident: bib0085 article-title: A two-stage fuzzy multi-objective framework for segmentation of 3D MRI brain image data publication-title: Appl. Soft Comput. – volume: 40 start-page: 835 year: 2007 end-page: 838 ident: bib0055 article-title: Fast and robust fuzzy C-means clustering algorithms incorporating local information for image segmentation publication-title: Pattern Recognit. – volume: 29 start-page: 492 year: 2015 end-page: 505 ident: bib0075 article-title: A modified fuzzy C-means algorithm using scale control spatial information for MRI image segmentation in the presence of noise publication-title: J. Chemom. – volume: 102 start-page: 317 year: 2016 end-page: 324 ident: bib0110 article-title: Review of MRI-based brain tumor image segmentation using deep learning methods publication-title: Procedia Comput. Sci. – volume: 41 start-page: 5526 year: 2014 ident: 10.1016/j.asoc.2018.04.031_bib0105 article-title: Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2014.01.021 – volume: 26 start-page: 405 year: 2007 ident: 10.1016/j.asoc.2018.04.031_bib0005 article-title: A review of methods for correction of intensity inhomogeneity in MRI publication-title: IEEE Trans. Med. Imaging doi: 10.1109/TMI.2006.891486 – volume: 9 start-page: 1945 year: 2015 ident: 10.1016/j.asoc.2018.04.031_bib0025 article-title: A nonparametric method for intensity inhomogeneity correction in MRI brain images by fusion of Gaussian surfacesl publication-title: Signal Image Video Process. doi: 10.1007/s11760-014-0689-5 – volume: 5 year: 1997 ident: 10.1016/j.asoc.2018.04.031_bib0120 article-title: Brainweb online interface to a 3D MRI simulated brain database publication-title: Neuroimage – volume: 117 start-page: 1412 year: 2013 ident: 10.1016/j.asoc.2018.04.031_bib0065 article-title: An adaptive spatial information-theoretic fuzzy clustering algorithm for image segmentation publication-title: Comput. Vision Image Underst. doi: 10.1016/j.cviu.2013.05.001 – volume: 45 start-page: 407 year: 2012 ident: 10.1016/j.asoc.2018.04.031_bib0115 article-title: Analysis of parameter selections for fuzzy c-means publication-title: Pattern Recogn. doi: 10.1016/j.patcog.2011.07.012 – volume: 21 start-page: 193 year: 2002 ident: 10.1016/j.asoc.2018.04.031_bib0040 article-title: A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data publication-title: IEEE Trans. Med. Imaging doi: 10.1109/42.996338 – volume: 34 start-page: 1329 year: 2013 ident: 10.1016/j.asoc.2018.04.031_bib0060 article-title: A modified interval type-2 fuzzy C-means algorithm with application in MR image segmentation publication-title: Pattern Recognit. Lett. doi: 10.1016/j.patrec.2013.04.021 – volume: 30 start-page: 9 year: 2006 ident: 10.1016/j.asoc.2018.04.031_bib0050 article-title: Fuzzy C-means clustering with spatial information for image Segmentation publication-title: Comput. Med. Imaging Graph. doi: 10.1016/j.compmedimag.2005.10.001 – volume: 30 start-page: 602 year: 2016 ident: 10.1016/j.asoc.2018.04.031_bib0030 article-title: On estimation of bias field in MRI images: polynomial vs Gaussian surface fitting method publication-title: J. Chemom. doi: 10.1002/cem.2825 – volume: 29 start-page: 492 year: 2015 ident: 10.1016/j.asoc.2018.04.031_bib0075 article-title: A modified fuzzy C-means algorithm using scale control spatial information for MRI image segmentation in the presence of noise publication-title: J. Chemom. doi: 10.1002/cem.2728 – volume: 40 start-page: 835 year: 2007 ident: 10.1016/j.asoc.2018.04.031_bib0055 article-title: Fast and robust fuzzy C-means clustering algorithms incorporating local information for image segmentation publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2006.07.011 – volume: 13 start-page: 517 year: 2005 ident: 10.1016/j.asoc.2018.04.031_bib0125 article-title: A possibilistic fuzzy c-means clustering algorithm publication-title: IEEE Trans. Fuzzy Systems doi: 10.1109/TFUZZ.2004.840099 – volume: 33 start-page: 261 year: 2010 ident: 10.1016/j.asoc.2018.04.031_bib0015 article-title: Review of brain MRI image segmentation methods publication-title: J. Artif. Intell. – volume: 102 start-page: 317 year: 2016 ident: 10.1016/j.asoc.2018.04.031_bib0110 article-title: Review of MRI-based brain tumor image segmentation using deep learning methods publication-title: Procedia Comput. Sci. doi: 10.1016/j.procs.2016.09.407 – volume: 60 start-page: 312 year: 2017 ident: 10.1016/j.asoc.2018.04.031_bib0085 article-title: A two-stage fuzzy multi-objective framework for segmentation of 3D MRI brain image data publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2017.07.001 – volume: 22 start-page: 1063 year: 2003 ident: 10.1016/j.asoc.2018.04.031_bib0045 article-title: An adaptive spatial fuzzy clustering algorithm for 3-D MR image segmentation publication-title: IEEE Trans. Med. Imaging doi: 10.1109/TMI.2003.816956 – ident: 10.1016/j.asoc.2018.04.031_bib0130 – volume: 19 start-page: 578 year: 2014 ident: 10.1016/j.asoc.2018.04.031_bib0100 article-title: A survey of MRI-based brain tumor segmentation methods publication-title: Tsinghua Sci. Technol. doi: 10.1109/TST.2014.6961028 – volume: 34 start-page: 758 year: 2015 ident: 10.1016/j.asoc.2018.04.031_bib0080 article-title: Conditional spatial fuzzy C-means clustering algorithm for segmentation of MRI images publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2015.05.038 – volume: 29 start-page: 1580 year: 2008 ident: 10.1016/j.asoc.2018.04.031_bib0090 article-title: MRI brain image segmentation and bias field correction based on fast spatially constrained kernel clustering approach publication-title: Pattern Recognit. Lett. doi: 10.1016/j.patrec.2008.03.012 – volume: 10 start-page: 234 year: 2006 ident: 10.1016/j.asoc.2018.04.031_bib0020 article-title: Intensity non-uniformity correction in MRI: Existing methods and their validation publication-title: Med. Image Anal. doi: 10.1016/j.media.2005.09.004 – volume: 2006 start-page: 1 year: 2006 ident: 10.1016/j.asoc.2018.04.031_bib0010 article-title: A review on MR image intensity inhomogeneity correction publication-title: Int. J. Biomed. Imaging doi: 10.1155/IJBI/2006/49515 – volume: 23 start-page: 390 year: 2013 ident: 10.1016/j.asoc.2018.04.031_bib0070 article-title: Improved spatial fuzzy c-means clustering for image segmentation using PSO initialization, Mahalanobis distance and post-segmentation correction publication-title: Digital Signal Process. doi: 10.1016/j.dsp.2013.07.005 – volume: 5 start-page: 199 year: 2014 ident: 10.1016/j.asoc.2018.04.031_bib0095 article-title: Medical image segmentation methods, algorithms, and applications publication-title: IETE Tech. Rev. doi: 10.1080/02564602.2014.906861 – year: 1981 ident: 10.1016/j.asoc.2018.04.031_bib0035 |
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| SubjectTerms | Bias field Brain MR image segmentation Fuzzy clustering algorithm Intensity inhomogeneity |
| Title | Local contextual information and Gaussian function induced fuzzy clustering algorithm for brain MR image segmentation and intensity inhomogeneity estimation |
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