Abstract
This paper proposes a novel deep learning architecture WaveM-CNN for efficient recognition of sub-cellular organelles in microscopic images. Essentially, multi-resolution analysis based on wavelet decomposition and convolution neural network (CNN) are combined in the architecture. In each wavelet transformed sub-space, discriminative features are extracted by convolution kernels to provide various pattern characteristics of the same organelle. The generated feature maps are concatenated and passed directly to the fully connected layers of the classifier. In order to reduce the computational time and improve performance on limited dataset, transfer learning method is adopted, with the utilization of compact MobileNet model. Experiments on two benchmark datasets CHO and 2D HeLa are conducted to evaluate the performance of the proposed model on fluorescence microscopic images of sub-cellular organelles. The classification accuracies of 98.4\(\%\) and 96.1\(\%\) are achieved on these two datasets respectively, which are significantly higher than both hand-crafted feature based methods and recent deep learning based models.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Boland, M., Markey, M., Murphy, R.: Automated recognition of patterns characteristic of subcellular structures in fluorescence microscopy images. Cytometry 33(3), 366–375 (1998)
Boland, M., Murphy, R.: A neural network classifier capable of recognizing thepatterns of all major subcellular structures in fluorescence microscope images of HeLa cells. Bioinformatics 17(12), 1213–1223 (2001)
Chebira, A., et al.: A multiresolution approach to automated classification of protein subcellular location images. BMC Bioinform. 8(1), 210 (2007)
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: CVPR 2009, pp. 248–255 (2009)
Godinez, W.J., Hossain, I., Lazic, S.E., Davies, J.W., Zhang, X.: A multi-scale convolutional neural network for phenotyping high-content cellular images. Bioinformatics 33(13), 2010–2019 (2017)
Hamilton, N.A., Pantelic, R.S., Hanson, K., Teasdale, R.D.: Fast automated cell phenotype image classification. BMC Bioinform. 8(1), 110 (2007)
Howard, A.G., et al.: MobileNets: efficient convolutional neural networks for mobile vision applications. CoRR (2017). http://arxiv.org/abs/1704.04861
Huang, K., Murphy, R.F.: Boosting accuracy of automated classification of fluorescence microscope images for location proteomics. BMC Bioinform. 5(1), 78 (2004)
Wang, H., Vieira, J.: 2-D wavelet transforms in the form of matrices and application in compressed sensing. In: 2010 8th World Congress on Intelligent Control and Automation, pp. 35–39, July 2010
Lin, D., Lin, Z., Sun, L., Toh, K., Cao, J.: LLC encoded bow features and softmax regression for microscopic image classification. In: 2017 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1–4, May 2017
Liu, D., Wang, S., Huang, D., Deng, G., Zeng, F., Chen, H.: Medical image classification using spatial adjacent histogram based on adaptive local binary patterns. Comput. Biol. Med. 72, 185–200 (2016)
Nguyen, L.D., Lin, D., Lin, Z., Cao, J.: Deep CNNs for microscopic image classification by exploiting transfer learning and feature concatenation. In: 2018 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1–5, May 2018
Orlov, N.: WND-CHARM: multi-purpose image classification using compound image transforms. Pattern Recogn. Lett. 29(11), 1684–1693 (2008)
Parnamaa, T.: Accurate classification of protein subcellular localization from high-throughput microscopy images using deep learning. G3: Genes, Genomes, Genet. 7(5), 1385–1392 (2017)
Ranzato, M., Taylor, P., House, J., Flagan, R., LeCun, Y., Perona, P.: Automatic recognition of biological particles in microscopic images. Pattern Recogn. Lett. 28(1), 31–39 (2007)
Zhang, X., Zhao, S.G.: Fluorescence microscopy image classification of 2D HeLa cells based on the CapsNet neural network. Med. Biol. Eng. Comput. 57(6), 1187–1198 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Tran, D.H., Meunier, M., Cheriet, F. (2019). WaveM-CNN for Automatic Recognition of Sub-cellular Organelles. In: Karray, F., Campilho, A., Yu, A. (eds) Image Analysis and Recognition. ICIAR 2019. Lecture Notes in Computer Science(), vol 11662. Springer, Cham. https://doi.org/10.1007/978-3-030-27202-9_16
Download citation
DOI: https://doi.org/10.1007/978-3-030-27202-9_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-27201-2
Online ISBN: 978-3-030-27202-9
eBook Packages: Computer ScienceComputer Science (R0)