Abstract
This study proposes a new method on “detecting brain region in MRI data”. This task is generally named as “skull stripping” in the literature. The algorithm is developed by using the cellular neural networks (CNNs) and multistable CNN structures. It also includes a contrast enhancement and noise reduction algorithm. The algorithm is named as multistable cellular neural network on MRI for skull stripping (mCNN-MRI-SS). Three different case studies are performed for measuring the success of the algorithm. Also a fourth case study is performed to evaluate the supporting algorithm, the CEULICA. First two evaluations are performed by using well-known MIDAS-NAMIC and Brainweb databases, which are properly organized Talairach-compatible databases. The third database was obtained from the research and application hospital of Necmettin Erbakan University Meram Faculty of Medicine. These MRI data were not Talairach-compatible and less sampled. The algorithm achieved 0.595 Jaccard, 0.744 Dice, 0.0344 TPF and 0.383 TNF mean values with the Brainweb T1-weighted images and 0.837 Jaccard, 0.898 Dice, 0.0124 TPF and 0.1511 TNF mean values with the MIDAS-NAMIC T2-weighted images. The algorithm achieved 0.8297 Jaccard, 0.9012 Dice, 0.0951 TPF and 0.1225 TNF mean values and achieved with the obtained data the best values among the other algorithms. As a result, it can be claimed that algorithm performs best with the non-Talairach-compatible MRI data due to its nature of performing at cellular level.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Change history
15 May 2024
This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s00521-024-09974-7
References
Somasundaram K, Kalaiselvi T (2011) Automatic brain extraction methods for T1 magnetic resonance images using region labeling and morphological operations. Comput Biol Med 41:716–725
El-Dahshan E-SA, Mohsen HM, Revett K, Salem A-BM (2014) Computer-aided diagnosis of human brain tumor through MRI: a survey and a new algorithm. Expert Syst Appl 41:5526–5545
Demirhan A, Güler İ (2011) Combining stationary wavelet transform and self-organizing maps for brain MR image segmentation. Eng Appl Artif Intell 24:358–367
Tanoori B, Azimifar Z, Shakibafar A, Katebi S (2011) Brain volumetry: an active contour model-based segmentation followed by SVM-based classification. Comput Biol Med 41:619–632
Rajendran A, Dhanasekaran R (2012) Fuzzy clustering and deformable model for tumor segmentation on MRI brain image: a combined approach. Proced Eng 30:327–333
Oishi K, Faria A, Jiang H, Li X, Akhter K, Zhang J, Hsu JT, Miller MI, van Zijl PCM, Albert M, Lyketsos CG, Woods R, Toga AW, Pike GB, Rosa-Neto P, Evans A, Mazziotta J, Mori S (2009) Atlas-based whole brain white matter analysis using large deformation diffeomorphic metric mapping: application to normal elderly and Alzheimer’s disease participants. Neuroimage 46:486–499
Xue JH, Pizurica A, Philips W, Kerre E, Van De Walle R, Lemahieu I (2003) An integrated method of adaptive enhancement for unsupervised segmentation of MRI brain images. Pattern Recognit Lett 24:2549–2560
Khademi A, Venetsanopoulos A, Moody A (2009) Automatic contrast enhancement of white matter lesions in flair MRI. In: Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009, pp 322–325
Yilmaz B, Özbay Y (2014) Contrast enhancement using linear image combinations algorithm (CEULICA) for enhancing brain magnetic resonance images. Turk J Electron Eng Comput Sci 22:1540–1563
Panetta KA, Wharton EJ, Agaian SS (2008) Human visual system-based image enhancement and logarithmic contrast measure. IEEE Trans Syst Man Cybern Part B Cybern 38:174–188
Smathers RL, Bush E, Drace J, Stevens M, Sommer FG, Brown BW, Karras B (1986) Mammographic microcalcifications: detection with xerography, screen-film, and digitized film display. Radiology 159:673–677
Chen ZY, Abidi BR, Page DL, Abidi MA (2006) Gray-level grouping (GLG): an automatic method for optimized image contrast enhancement—Part II: The variations. IEEE Trans Image Process 15:2303–2314
Vidaurrazaga M, Diago LA, Cruz A (2000) Contrast enhancement with wavelet transform in radiological images. In: Proceedings of 22nd Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. (Cat. No.00CH37143), vol 3
Perfetti R, Ricci E, Casali D, Costantini G (2007) Cellular neural networks with virtual template expansion for retinal vessel segmentation. IEEE Trans Circuits Syst II Express Briefs 54:141–145
Hernandez JAM, Castaeda FG, Cadenas JAM (2009) Multistable cellular neural networks and their application to image decomposition. In: 2009 52nd IEEE Int. Midwest Symp. Circuits System
Zamparelli M (1997) Genetically trained cellular neural networks. Neural Netw 10:1143–1151
Karaboga D, Gorkemli B, Ozturk C, Karaboga N (2012) A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artificial Intelligence Review, 42(1):21-57.
Woods RP, Dapretto M, Sicotte NL, Toga AW, Mazziotta JC (1999) Creation and use of a Talairach-compatible atlas for accurate, automated, nonlinear intersubject registration, and analysis of functional imaging data. Hum Brain Mapp 8:73–79
Napadow V, Dhond R, Kennedy D, Hui KKS, Makris N (2006) Automated brainstem co-registration (ABC) for MRI. Neuroimage 32:1113–1119
Boesen K, Rehm K, Schaper K, Stoltzner S, Woods R, Lüders E, Rottenberg D (2004) Quantitative comparison of four brain extraction algorithms. Neuroimage 22:1255–1261
Aubert-Broche B, Evans AC, Collins L (2006) A new improved version of the realistic digital brain phantom. Neuroimage 32(1):138–145
Aubert-Broche B, Griffin M, Pike GB, Evans AC, Collins DL (2006) Twenty new digital brain phantoms for creation of validation image data bases. IEEE Trans Med Imaging 25:1410–1416
Chua LO, Yang L (1988) Cellular neural networks: theory. IEEE Trans Circuits Syst 35:1257–1272
Chua LO, Yang L (1988) Cellular neural networks: applications. IEEE Trans Circuits Syst 35:1273–1290
Kawahara M, Inoue T, Nishio Y (2009) Cellular neural network with dynamic template and its output characteristics. In: Proceedings of the International Joint Conference on Neural Networks, pp 1552–1558
Kozek T, Roska T, Chua LO (1993) Genetic algorithm for CNN template learning. IEEE Trans Circuits Syst I Fundam Theory Appl 40:392–402
Cerasa A, Bilotta E, Augimeri A, Cherubini A, Pantano P, Zito G, Lanza P, Valentino P, Gioia MC, Quattrone A (2012) A cellular neural network methodology for the automated segmentation of multiple sclerosis lesions. J Neurosci Methods 203(1):193–199
Yokosawa K, Nakaguchi T, Tanji Y, Tanaka M (2003) Cellular neural networks with output function having multiple constant regions. IEEE Trans Circuits Syst I Fundam Theory Appl 50:847–857
Roska T, Chua LO (1993) CNN universal machine. An analogic array computer. IEEE Trans Circuits Syst II Analog Digit Signal Process 40(3):163–173
Bullitt E, Zeng D, Gerig G, Aylward S, Joshi S, Smith JK, Lin W, Ewend MG (2005) Vessel tortuosity and brain tumor malignancy: a blinded study. Acad Radiol 12:1232–1240
MIDAS - Collection NAMIC: Brain Mutlimodality. [Online]. http://www.insight-journal.org/midas/collection/view/190. Accessed 15 Dec 2014
Cocosco CA, Kollokian V, Kwan RK, Pike GB, Evans AC (1996) BrainWeb: Online Interface to a 3D MRI Simulated Brain Database. In: 3-rd International Conference on Functional Mapping of the Human Brain, 1996, vol 1131, p 1996
Klauschen F, Goldman A, Barra V, Meyer-Lindenberg A, Lundervold A (2009) Evaluation of automated brain MR image segmentation and volumetry methods. Hum Brain Mapp 30(4):1310–1327
Shattuck DW, Leahy RM (2002) Brainsuite: an automated cortical surface identification tool. Med Image Anal 6:129–142
Kasiri K, Dehghani M (2010) Comparison evaluation of three brain MRI segmentation methods in software tools. In: ICBME, 2010 17th …, no. November, pp 3–4
Jenkinson M, Beckmann CF, Behrens TEJ, Woolrich MW, Smith SM (2012) Fsl. Neuroimage 62(2):782–790
Somasundaram K, Kalaiselvi T (2010) Fully automatic brain extraction algorithm for axial T2-weighted magnetic resonance images. Comput Biol Med 40(10):811–822
Chiverton J, Wells K, Lewis E, Chen C, Podda B, Johnson D (2007) Statistical morphological skull stripping of adult and infant MRI data. Comput Biol Med 37(3):342–357
Balan AGR, Traina AJM, Ribeiro MX, Marques PMA, Traina C (2012) Smart histogram analysis applied to the skull-stripping problem in T1-weighted MRI. Comput Biol Med 42(5):509–522
Park JG, Lee C (2009) Skull stripping based on region growing for magnetic resonance brain images. Neuroimage 47(4):1394–1407
Acknowledgements
The MRI brain images of the NAMIC database used in this paper were collected and made available by the CASILab at the University of North Carolina at Chapel Hill and were distributed by the MIDAS Data Server at Kitware, Inc. The data were obtained from Necmettin Erbakan University Meram Faculty of Medicine, according to ethical committee of clinical researches decision No. 2011/235.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
None.
About this article
Cite this article
Yilmaz, B., Durdu, A. & Emlik, G.D. RETRACTED ARTICLE: A new method for skull stripping in brain MRI using multistable cellular neural networks. Neural Comput & Applic 29, 79–95 (2018). https://doi.org/10.1007/s00521-016-2834-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-016-2834-2