Abstract
An alteration of neuronal morphology is present in cognitive neurological diseases where learning or memory abilities are affected. The quantification of this alteration and its evolution by the study of microscopic images is essential. However, the use of advanced and automatic image processing techniques is currently very limited, focusing on the analysis of the morphology of isolated neurons. On this article we present a new methodology, based on texture analysis, to characterize the global distribution of different neural patterns in immunofluorescence images of brain tissue sections, where the neurons can be visualized as they are really distributed. We apply the technique to mice brain tissue section dividing them into two classes: Ts1Cje Down’s syndrome model and wild type, free of this neurodegenerative disease. Taking into account CA1 region of the hippocampus, we calculate and compare several state of the art texture descriptors that are subsequently classified using machine learning techniques. Achieving a 95% of accuracy, the assumption that texture characterization is relevant to quantify globally morphological alterations in the neurons, seems to be demonstrated.
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References
Sholl, D.A.: Dendritic organization in the neurons of the visual and motor cortices of the cat. J. Anat. 87(4), 387–406 (1953)
Troca-Marín, J.A., Alves-Sampaio, A., Montesinos, M.L.: An increase in basal BDNF provokes hyperactivation of the Akt-mammalian target of Rapamycin pathway and deregulation of local dendritic translation in a mouse model of Downs syndrome. J. Neurosci. 31, 9445–9455 (2011)
Alves-Sampaio, A., Troca-Marín, J.A., Montesinos, M.L.: NMDA-mediated regulation of DSCAM dendritic local translation is lost in a mouse model of downs syndrome. J. Neurosci. 30, 13537–13548 (2010)
Troca-Marín, J.A., Alves-Sampaio, A., Montesinos, M.L.: Deregulated mTOR-mediated translation in intellectual disability. Prog. Neurobiol. 96, 268–282 (2012)
Montesinos, M.L.: Roles for DSCAM and DSCAML1 in central nervous system development and disease. In: Berezin, V., Walmod, P.S. (eds.) Cell Adhesion Molecules. AN, vol. 8, pp. 249–270. Springer, New York (2014). doi:10.1007/978-1-4614-8090-7_11
Troca-Marín, J.A., Casañas, J.J., Benito, I., Montesinos, M.L.: The Akt-mTOR pathway in down’s syndrome: the potential use of rapamycin/rapalogs for treating cognitive deficits. CNS Neurol. Disord. Drug. Targets 13, 34–40 (2014)
González, R., Woods, R.E.: Digital Image Processing, 3rd edn. Prentice Hall, Nueva Jersey (2008)
Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. SMC 3(6), 610–621 (1973)
Boland, M.V., Murphy, R.F.: A neural network classifier capable of recognizing the patterns of all major subcellular structures in fluorescence microscope images of hela cells. Bioinformatics 17(12), 1213–1223 (2001)
Svoboda, D., Kozubek, M., Stejskal, S.: Generation of digital phantoms of cell nuclei and simulation of image formation in 3D image cytometry. Cytometry A 75(6), 494–509 (2009)
Al-Kadi, O.S.: Texture measures combination for improved meningioma classification of histopathological images. Pattern Recogn. 43, 2043–2053 (2010)
Xia, H., Tong, L., Zhou, X., Zhang, J., Zhou, Z., Liu, W.: Texture analysis and volumetry of hippocampus and medial temporal lobe in patients with Alzheimers disease. In: Proceedings of 2012 International Conference on Biomedical Engineering and Biotechnology, pp. 905–908 (2012)
Thibault, G., Angulo, J., Meyer, F.: Advanced statistical matrices for texture characterization: application to cell classification. IEEE Trans. Biomed. Eng. 61(3), 630–637 (2014)
Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)
Hafiane, A., Seetharaman, G., Zavidovique, B.: Median binary pattern for textures classification. In: Kamel, M., Campilho, A. (eds.) ICIAR 2007. LNCS, vol. 4633, pp. 387–398. Springer, Heidelberg (2007). doi:10.1007/978-3-540-74260-9_35
Ta, X., Triggs, B.: Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans. Image Process. 19(6), 1635–1650 (2010)
Rakotomamonjy, A., Petitjean, C., Salan, M., Thiberville, L.: Scattering features for lung cancer detection in fibered confocal fluorescence microscopy images. Artif. Intell. Med. 61, 105–118 (2014)
Fogel, I., Sagi, D.: Gabor filters as texture discriminator. Biol. Cybern. 61(2), 103–113 (1989)
Rellier, G., Descombes, X., Falzon, F., Zerubia, J.: Texture feature analysis using a Gauss-Markov model in hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 42(7), 1543–1551 (2004)
Wu, C., Schulte, J.J., Sepp, K.J., Littleton, J.T., Hong, P.: Automatic robust neurite detection and morphological analysis of neuronal cell cultures in high-content screening. Neuroinformatics 8(3), 83–100 (2010)
Budde, M.D., Frank, J.A.: Examining brain microstructure using structure tensor analysis of histological sections. NeuroImage 63(11), 1–10 (2012)
Bornemann, F., März, T.: Fast Image Inpainting Based on Coherence Transport. Springer Science+Business Media, USA (2007)
Contrast-Limited Adaptive Histogram Equalization (CLAHE): At MathWorks. http://es.mathworks.com/help/images/ref/adapthisteq.html. Accessed 20 Jan 2017
Costa, A.F., Humpire-Mamani, G., Traina, A.J.M.: An efficient algorithm for fractal analysis of textures. In: Proceedings of Graphics, Patterns and Images (SIBGRAPI), Brazil, pp. 1530–1834 (2012)
Sarmiento, A., Fondón, I., Velasco, M.M., Qasair, A., Aguilera, P.: Gaussian mixture model generalized for segmentation of melanomas. In: Annual Congress of the Spanish Society of Biomedical Engineering (CASEIB) (2014)
Scharfenberger, C.: Statistical textural distinctiveness for salient region detection in natural images. IEEE Conf. Comput. Vis. Pattern Recogn. (CVPR) 24(1), 457–470 (2013)
Local Binary Pattern. http://www.scholarpedia.org/article/Local_Binary_Patterns. Accessed 20 Jan 2017
Cortes, C., Vapnik, V.: Support-Vector Networks. AT & T Bell Labs, USA (1995)
Altman, N.S.: An introduction to kernel and nearest-neighbor nonparametric regression. Am. Stat. 46(3), 175–185 (1992)
Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123–140 (1996)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
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Sarmiento, A., Fernández-Granero, M.Á., Galán, B., Montesinos, M.L., Fondón, I. (2017). Neuronal Texture Analysis in Murine Model of Down’s Syndrome. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2017. Lecture Notes in Computer Science(), vol 10306. Springer, Cham. https://doi.org/10.1007/978-3-319-59147-6_2
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DOI: https://doi.org/10.1007/978-3-319-59147-6_2
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