Detection of dendritic spines using wavelet-based conditional symmetric analysis and regularized morphological shared-weight neural networks. (English) Zbl 1343.92269
Summary: Identification and detection of dendritic spines in neuron images are of high interest in diagnosis and treatment of neurological and psychiatric disorders (e.g., Alzheimer’s disease, Parkinson’s diseases, and autism). In this paper, we have proposed a novel automatic approach using wavelet-based conditional symmetric analysis and regularized morphological shared-weight neural networks (RMSNN) for dendritic spine identification involving the following steps: backbone extraction, localization of dendritic spines, and classification. First, a new algorithm based on wavelet transform and conditional symmetric analysis has been developed to extract backbone and locate the dendrite boundary. Then, the RMSNN has been proposed to classify the spines into three predefined categories (mushroom, thin, and stubby). We have compared our proposed approach against the existing methods. The experimental result demonstrates that the proposed approach can accurately locate the dendrite and accurately classify
the spines into three categories with the accuracy of 99.1% for “mushroom” spines, 97.6% for “stubby” spines, and 98.6% for “thin” spines.
MSC:
| 92C55 | Biomedical imaging and signal processing |
| 92B20 | Neural networks for/in biological studies, artificial life and related topics |
| 65T60 | Numerical methods for wavelets |
Keywords:
dendritic spines; Alzheimer’s disease; wavelet-based conditional symmetric analysis; regularized morphological shared-weight neural networksReferences:
| [1] | Krichmar, J. L.; Nasuto, S. J.; Scorcioni, R.; Washington, S. D.; Ascoli, G. A., Effects of dendritic morphology on CA3 pyramidal cell electrophysiology: a simulation study, Brain Research, 941, 1-2, 11-28 (2002) · doi:10.1016/s0006-8993(02)02488-5 |
| [2] | Johnston, D.; Wu, S. M.-S., Foundations of Cellular Neurophysiology (1995), Cambridge, Mass, USA: MIT Press, Cambridge, Mass, USA |
| [3] | Mainen, Z. F.; Sejnowski, T. J., Influence of dendritic structure on firing pattern in model neocortical neurons, Nature, 382, 6589, 363-366 (1996) · doi:10.1038/382363a0 |
| [4] | Keren, N.; Peled, N.; Korngreen, A., Constraining compartmental models using multiple voltage recordings and genetic algorithms, Journal of Neurophysiology, 94, 6, 3730-3742 (2005) · doi:10.1152/jn.00408.2005 |
| [5] | Van Calster, B.; Timmerman, D.; Lu, C.; Suykens, J. A. K.; Valentin, L.; Van Holsbeke, C.; Amant, F.; Vergote, I.; Van Huffel, S., Preoperative diagnosis of ovarian tumors using Bayesian kernel-based methods, Ultrasound in Obstetrics and Gynecology, 29, 5, 496-504 (2007) · doi:10.1002/uog.3996 |
| [6] | Stiefel, K. M.; Sejnowski, T. J., Mapping function onto neuronal morphology, Journal of Neurophysiology, 98, 1, 513-526 (2007) · doi:10.1152/jn.00865.2006 |
| [7] | Wang, S.; Pan, H.; Zhang, C.; Tian, Y., RGB-D image-based detection of stairs, pedestrian crosswalks and traffic signs, Journal of Visual Communication and Image Representation, 25, 2, 263-272 (2014) · doi:10.1016/j.jvcir.2013.11.005 |
| [8] | Schmitz, S. K.; Hjorth, J. J. J.; Joemai, R. M. S.; Wijntjes, R.; Eijgenraam, S.; de Bruijn, P.; Georgiou, C.; de Jong, A. P. H.; van Ooyen, A.; Verhage, M.; Cornelisse, L. N.; Toonen, R. F.; Veldkamp, W., Automated analysis of neuronal morphology, synapse number and synaptic recruitment, Journal of Neuroscience Methods, 195, 2, 185-193 (2011) · doi:10.1016/j.jneumeth.2010.12.011 |
| [9] | Liu, T. M.; Li, G.; Nie, J. X.; Tarokh, A.; Zhou, X. B.; Guo, L.; Malicki, J.; Xia, W.; Wong, S. T. C., An automated method for cell detection in zebrafish, Neuroinformatics, 6, 1, 5-21 (2008) · doi:10.1007/s12021-007-9005-7 |
| [10] | Yu, W.; Lee, H. K.; Hariharan, S.; Bu, W.; Ahmed, S., Evolving generalized voronoi diagrams for accurate cellular image segmentation, Cytometry Part A, 77, 4, 379-386 (2010) · doi:10.1002/cyto.a.20876 |
| [11] | Bashar, M. K.; Komatsu, K.; Fujimori, T.; Kobayashi, T. J., Automatic extraction of nuclei centroids of mouse embryonic cells from fluorescence microscopy images, PLoS ONE, 7, 5 (2012) · doi:10.1371/journal.pone.0035550 |
| [12] | Martiel, J. L.; Leal, A.; Kurzawa, L.; Balland, M.; Wang, I.; Vignaud, T.; Tseng, Q. Z.; Théry, M.; Paluch, E. K., Measurement of cell traction forces with ImageJ, Methods in Cell Biology, 125, 269-287 (2015), Academic Press |
| [13] | Dickstein, D. L.; Rodriguez, A.; Rocher, A. B.; Wearne, S. L.; Luebke, J. I.; Weaver, C. M.; Hof, P. R., NeuronStudio: an automated quantitative software to assess changes in spine pathology in Alzheimer models, Alzheimer’s & Dementia, 6, 4, article S410 (2010) · doi:10.1016/j.jalz.2010.05.1382 |
| [14] | Meijering, E.; Jacob, M.; Sarria, J.-C. F.; Steiner, P.; Hirling, H.; Unser, M., Design and validation of a tool for neurite tracing and analysis in fluorescence microscopy images, Cytometry Part A, 58, 2, 167-176 (2004) |
| [15] | Cheng, J.; Zhou, X. B.; Sabatini, B. L.; Wong, S. T. C., NeuronIQ: a novel computational approach for automatic dendrite spines detection and analysis, Proceedings of the IEEE/NIH Life Science Systems and Applications Workshop (LISA ’07), IEEE · doi:10.1109/lssa.2007.4400911 |
| [16] | Koh, I. Y. Y.; Lindquist, W. B.; Zito, K.; Nimchinsky, E. A.; Svoboda, K., An image analysis algorithm for dendritic spines, Neural Computation, 14, 6, 1283-1310 (2002) · Zbl 0995.92014 · doi:10.1162/089976602753712945 |
| [17] | Xu, X. Y.; Cheng, J.; Witt, R. M.; Sabatini, B. L.; Wong, S. T. C., A shape analysis method to detect dendritic spine in 3D optical microscopy image, Proceedings of the 3rd IEEE International Symposium on Biomedical Imaging: From Nano to Macro |
| [18] | Cheng, J.; Zhou, X.; Miller, E.; Witt, R. M.; Zhu, J.; Sabatini, B. L.; Wong, S. T. C., A novel computational approach for automatic dendrite spines detection in two-photon laser scan microscopy, Journal of Neuroscience Methods, 165, 1, 122-134 (2007) · doi:10.1016/j.jneumeth.2007.05.020 |
| [19] | Fan, J.; Zhou, X.; Dy, J. G.; Zhang, Y.; Wong, S. T. C., An automated pipeline for dendrite spine detection and tracking of 3D optical microscopy neuron images of in vivo mouse models, Neuroinformatics, 7, 2, 113-130 (2009) · doi:10.1007/s12021-009-9047-0 |
| [20] | Zhang, Y.; Zhou, X. B.; Witt, R. M.; Sabatini, B. L.; Adjeroh, D.; Wong, S. T. C., Dendritic spine detection using curvilinear structure detector and LDA classifier, NeuroImage, 36, 2, 346-360 (2007) · doi:10.1016/j.neuroimage.2007.02.044 |
| [21] | Janoos, F.; Mosaliganti, K.; Xu, X.; Machiraju, R.; Huang, K.; Wong, S. T. C., Robust 3D reconstruction and identification of dendritic spines from optical microscopy imaging, Medical Image Analysis, 13, 1, 167-179 (2009) · doi:10.1016/j.media.2008.06.019 |
| [22] | He, T.; Xue, Z.; Wong, S. T. C., A novel approach for three dimensional dendrite spine segmentation and classification, Medical Imaging 2012: Image Processing · doi:10.1117/12.911693 |
| [23] | Shi, P.; Huang, Y.; Hong, J., Automated three-dimensional reconstruction and morphological analysis of dendritic spines based on semi-supervised learning, Biomedical Optics Express, 5, 5, 1541-1553 (2014) · doi:10.1364/boe.5.001541 |
| [24] | Reid, S.; Lu, C.; Casikar, I.; Reid, G.; Abbott, J.; Cario, G.; Chou, D.; Kowalski, D.; Cooper, M.; Condous, G., Prediction of pouch of Douglas obliteration in women with suspected endometriosis using a new real-time dynamic transvaginal ultrasound technique: the sliding sign, Ultrasound in Obstetrics & Gynecology, 41, 6, 685-691 (2013) · doi:10.1002/uog.12305 |
| [25] | Reid, S.; Lu, C.; Casikar, I.; Mein, B.; Magotti, R.; Ludlow, J.; Benzie, R.; Condous, G., The prediction of pouch of Douglas obliteration using offline analysis of the transvaginal ultrasound ‘sliding sign’ technique: inter-and intra-observer reproducibility, Human Reproduction, 28, 5, 1237-1246 (2013) · doi:10.1093/humrep/det044 |
| [26] | Wang, Y.-H.; Liu, W.-N.; Chen, A.-H.; Wang, Y., Nonlinear dim target enhancement algorithm based on partial differential equation, Journal of Dalian Maritime University, 34, 2, 57-60 (2008) |
| [27] | Chen, L.; Zhang, J. H.; Chen, S. Y.; Lin, Y.; Yao, C. Y.; Zhang, J. W., Hierarchical mergence approach to cell detection in phase contrast microscopy images, Computational and Mathematical Methods in Medicine, 2014 (2014) · Zbl 1423.92141 · doi:10.1155/2014/758587 |
| [28] | Otsu, N., A threshold selection method from gray-level histograms, IEEE Transactions on Systems, Man and Cybernetics, 9, 1, 62-66 (1979) · doi:10.1109/tsmc.1979.4310076 |
| [29] | Liao, P.-S.; Chen, T.-S.; Chung, P.-C., A fast algorithm for multilevel thresholding, Journal of Information Science and Engineering, 17, 5, 713-727 (2001) |
| [30] | Yang, L. H.; You, X.; Haralick, R. M.; Phillips, I. T.; Tang, Y. Y., Characterization of Dirac edge with new wavelet transform, Proceedings of the 2nd International Conference on Wavelets and Applications · Zbl 1053.68794 |
| [31] | Tang, Y. Y.; You, X. G., Skeletonization of ribbon-like shapes based on a new wavelet function, IEEE Transactions on Pattern Analysis and Machine Intelligence, 25, 9, 1118-1133 (2003) · doi:10.1109/tpami.2003.1227987 |
| [32] | Zhang, Y. D.; Wang, S. H.; Ji, G. L.; Phillips, P., Fruit classification using computer vision and feedforward neural network, Journal of Food Engineering, 143, 167-177 (2014) · doi:10.1016/j.jfoodeng.2014.07.001 |
| [33] | Wang, S.; Zhang, Y.; Dong, Z.; Du, S.; Ji, G.; Yan, J.; Yang, J.; Wang, Q.; Feng, C.; Phillips, P., Feed-forward neural network optimized by hybridization of PSO and ABC for abnormal brain detection, International Journal of Imaging Systems and Technology, 25, 2, 153-164 (2015) · doi:10.1002/ima.22132 |
| [34] | Yang, G.; Zhang, Y.; Yang, J.; Ji, G.; Dong, Z.; Wang, S.; Feng, C.; Wang, Q., Automated classification of brain images using wavelet-energy and biogeography-based optimization, Multimedia Tools and Applications (2015) · doi:10.1007/s11042-015-2649-7 |
| [35] | Guo, D.; Zhang, Y.; Xiang, Q.; Li, Z., Improved radio frequency identification indoor localization method via radial basis function neural network, Mathematical Problems in Engineering, 2014 (2014) · doi:10.1155/2014/420482 |
| [36] | Jin, X.; Davis, C. H., Vehicle detection from high-resolution satellite imagery using morphological shared-weight neural networks, Image and Vision Computing, 25, 9, 1422-1431 (2007) · doi:10.1016/j.imavis.2006.12.011 |
| [37] | Chen, Z.; Molloi, S., Automatic 3D vascular tree construction in CT angiography, Computerized Medical Imaging and Graphics, 27, 6, 469-479 (2003) · doi:10.1016/s0895-6111(03)00039-9 |
| [38] | Zhang, Y.; Dong, Z.; Wang, S.; Ji, G.; Yang, J., Preclinical diagnosis of magnetic resonance (MR) brain images via discrete wavelet packet transform with tsallis entropy and generalized eigenvalue proximate support vector machine (GEPSVM), Entropy, 17, 4, 1795-1813 (2015) · doi:10.3390/e17041795 |
| [39] | Zhang, Y.; Wang, S.; Sun, P.; Phillips, P.; Liu, F.; Lee, D.; Lagoa, R.; Kumar, S., Pathological brain detection based on wavelet entropy and Hu moment invariants, Bio-Medical Materials and Engineering, 26, S1283-S1290 (2015) · doi:10.3233/bme-151426 |
| [40] | Zhang, Y.; Wang, S.; Phillips, P.; Dong, Z.; Ji, G.; Yang, J., Detection of Alzheimer’s disease and mild cognitive impairment based on structural volumetric MR images using 3D-DWT and WTA-KSVM trained by PSOTVAC, Biomedical Signal Processing and Control, 21, 58-73 (2015) · doi:10.1016/j.bspc.2015.05.014 |
| [41] | Wang, S.; Zhang, Y.; Ji, G.; Yang, J.; Wu, J.; Wei, L., Fruit classification by wavelet-entropy and feedforward neural network trained by fitness-scaled chaotic ABC and biogeography-based optimization, Entropy, 17, 8, 5711-5728 (2015) · doi:10.3390/e17085711 |
| [42] | Zhang, Y.; Dong, Z.; Phillips, P.; Wang, S.; Ji, G.; Yang, J.; Yuan, T., Detection of subjects and brain regions related to Alzheimer’s disease using 3D MRI scans based on eigenbrain and machine learning, Frontiers in Computational Neuroscience, 9, article 66 (2015) · doi:10.3389/fncom.2015.00066 |
| [43] | Wang, S.; Yang, X.; Zhang, Y.; Phillips, P.; Yang, J.; Yuan, T.-F., Identification of green, oolong and black teas in China via wavelet packet entropy and fuzzy support vector machine, Entropy, 17, 10, 6663-6682 (2015) · doi:10.3390/e17106663 |
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.
