Generic placeholder image

Current Computer Science

Author(s): Xinpeng Man and Yinglei Song*

DOI: 10.2174/0129503779291012240424070357

DownloadDownload PDF Flyer Cite As
Classification of Biomedical Images with Mined Statistical Features and Dynamic Programming

Article ID: e280524230384 Pages: 10

  • * (Excluding Mailing and Handling)

Explore Articles
About Journal
For Authors
For Editors
For Reviewers

Abstract

Background: In the research and practice of medical sciences, accurate classification of biomedical images with computer programs may provide an important basis for the study and diagnosis of many diseases.

Methods: This paper proposes a new statistical approach that can accurately classify biomedical images based on their statistical features. In the first step of the proposed approach, a number of SIFT features of different types are computed for each pixel in a biomedical image and a statistical feature that describes the distribution of each type of SIFT features is obtained for the image. In the second step, a dynamic programming approach is used to efficiently analyze the dependence among different statistical features associated with an image and compute the probability for an image to belong to each possible class; the class with the largest probability is determined as the result of classification.

Results: Experimental results show that the proposed approach can lead to classification results with accuracy higher than that of a few state-of-the-art approaches for the classification of biomedical images.

Conclusion: The proposed approach can achieve classification accuracy comparable to that of several state-of-the-art classification approaches. It is thus potentially useful for applications where large models are not appropriate for classification tasks due to limitations in computational or communication resources.

Keywords: Biomedical image classification, SIFT features, statistical features, mutual dependence, maximum likelihood, dynamic programming.

[1]
M. Held, M.H.A. Schmitz, B. Fischer, T. Walter, B. Neumann, M.H. Olma, M. Peter, J. Ellenberg, and D.W. Gerlich, "CellCognition: Time-resolved phenotype annotation in high-throughput live cell imaging", Nat. Methods, vol. 7, no. 9, pp. 747-754, 2010.
[http://dx.doi.org/10.1038/nmeth.1486] [PMID: 20693996]
[2]
B. Misselwitz, G. Strittmatter, B. Periaswamy, M.C. Schlumberger, S. Rout, P. Horvath, K. Kozak, and W.D. Hardt, "Enhanced cellclassifier: A multi-class classification tool for microscopy images", BMC Bioinformatics, vol. 11, no. 1, p. 30, 2010.
[http://dx.doi.org/10.1186/1471-2105-11-30] [PMID: 20074370]
[3]
G. Pau, F. Fuchs, O. Sklyar, M. Boutros, and W. Huber, "EBImage-An R package for image processing with applications to cellular phenotypes", Bioinformatics, vol. 26, no. 7, pp. 979-981, 2010.
[http://dx.doi.org/10.1093/bioinformatics/btq046] [PMID: 20338898]
[4]
J. Zhou, S. Lamichhane, G. Sterne, B. Ye, and H. Peng, "BIOCAT: A pattern recognition platform for customizable biological image classification and annotation", BMC Bioinformatics, vol. 14, no. 1, p. 291, 2013.
[http://dx.doi.org/10.1186/1471-2105-14-291] [PMID: 24090164]
[5]
E. Glory, and R.F. Murphy, "Automated subcellular location determination and high-throughput microscopy", Dev. Cell, vol. 12, no. 1, pp. 7-16, 2007.
[http://dx.doi.org/10.1016/j.devcel.2006.12.007] [PMID: 17199037]
[6]
L. Nanni, S. Brahnam, and A. Lumini, "A very high performing system to discriminate tissues in mammograms as benign and malignant", Expert Syst. Appl., vol. 39, no. 2, pp. 1968-1971, 2012.
[http://dx.doi.org/10.1016/j.eswa.2011.08.050]
[7]
R. Nosaka, and K. Fukui, "HEp-2 cell classification using rotation invariant co-occurrence among local binary patterns", Pattern Recognit., vol. 47, no. 7, pp. 2428-2436, 2014.
[http://dx.doi.org/10.1016/j.patcog.2013.09.018]
[8]
T. Song, H. Li, F. Meng, Q. Wu, and J. Cai, "LETRIST: Locally encoded transform feature histogram for rotation-invariant texture classification", IEEE Trans. Circ. Syst. Video Tech., vol. 28, no. 7, pp. 1565-1579, 2018.
[http://dx.doi.org/10.1109/TCSVT.2017.2671899]
[9]
V. Uhlmann, S. Singh, and A.E. Carpenter, "CP-CHARM: Segmentation-free image classification made accessible", BMC Bioinformatics, vol. 17, no. 1, p. 51, 2016.
[http://dx.doi.org/10.1186/s12859-016-0895-y] [PMID: 26817459]
[10]
F. Yang, Y.Y. Xu, and H.B. Shen, "Many local pattern texture features: Which is better for image-based multilabel human protein subcellular localization classification?", ScientificWorldJournal, vol. 2014, pp. 1-14, 2014.
[http://dx.doi.org/10.1155/2014/429049] [PMID: 25050396]
[11]
Z. Zhu, X. You, C.L.P. Chen, D. Tao, W. Ou, X. Jiang, and J. Zou, "An adaptive hybrid pattern for noise-robust texture analysis", Pattern Recognit., vol. 48, no. 8, pp. 2592-2608, 2015.
[http://dx.doi.org/10.1016/j.patcog.2015.01.001]
[12]
R.M. Haralick, K. Shanmugam, and I.H. Dinstein, "Textural features for image classification", IEEE Trans. Syst. Man Cybern., vol. SMC-3, no. 6, pp. 610-621, 1973.
[http://dx.doi.org/10.1109/TSMC.1973.4309314]
[13]
I. Fogel, and D. Sagi, "Gabor filters as texture discriminator", Biol. Cybern., vol. 61, no. 2, pp. 103-113, 1989.
[http://dx.doi.org/10.1007/BF00204594]
[14]
L. Nanni, S. Brahnam, S. Ghidoni, E. Menegatti, and T. Barrier, "A comparison of methods for extracting information from the co-occurrence matrix for subcellular classification", Expert Syst. Appl., vol. 40, no. 18, pp. 7457-7467, 2013.
[http://dx.doi.org/10.1016/j.eswa.2013.07.047]
[15]
J. Barker, A. Hoogi, A. Depeursinge, and D.L. Rubin, "Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles", Med. Image Anal., vol. 30, no. 1, pp. 60-71, 2016.
[http://dx.doi.org/10.1016/j.media.2015.12.002] [PMID: 26854941]
[16]
Y. Xu, J.Y. Zhu, E.I.C. Chang, M. Lai, and Z. Tu, "Weakly supervised histopathology cancer image segmentation and classification", Med. Image Anal., vol. 18, no. 3, pp. 591-604, 2014.
[http://dx.doi.org/10.1016/j.media.2014.01.010] [PMID: 24637156]
[17]
N. Cristianini, and S.J. Taylor, An Introduction To Support Vector Machines And Other Kernel-based Learning Methods., Cambridge University Press: Cambridge, UK, 2000.
[http://dx.doi.org/10.1017/CBO9780511801389]
[18]
S. Otalora, "Combining unsupervised feature learning and riesz wavelets for histopathology image representation: application to identifying anaplastic medulloblastoma", Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention Munich, pp. 581-588, 2015.
[http://dx.doi.org/10.1007/978-3-319-24553-9_71]
[19]
T.H. Vu, H.S. Mousavi, V. Monga, G. Rao, and U.K.A. Rao, "Histopathological image classification using discriminative feature-oriented dictionary learning", IEEE Trans. Med. Imaging, vol. 35, no. 3, pp. 738-751, 2016.
[http://dx.doi.org/10.1109/TMI.2015.2493530] [PMID: 26513781]
[20]
H. Amakdouf, A. Zouhri, M. El Mallahi, A. Tahiri, D. Chenouni, and H. Qjidaa, "Artificial intelligent classification of biomedical color image using quaternion discrete radial Tchebichef moments", Multimedia Tools Appl., vol. 80, no. 2, pp. 3173-3192, 2021.
[http://dx.doi.org/10.1007/s11042-020-09781-x]
[21]
K. Bora, M. Chowdhury, L.B. Mahanta, M.K. Kundu, and A.K. Das, "Pap smear image classification using convolutional neural network", Proceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image Processing 18 December 2016, Guwahati Assam India, pp. 1-8, 2016.
[http://dx.doi.org/10.1145/3009977.3010068]
[22]
T. Ching, D.S. Himmelstein, B.K. Beaulieu-Jones, A.A. Kalinin, B.T. Do, G.P. Way, E. Ferrero, P.M. Agapow, M. Zietz, M.M. Hoffman, W. Xie, G.L. Rosen, B.J. Lengerich, J. Israeli, J. Lanchantin, S. Woloszynek, A.E. Carpenter, A. Shrikumar, J. Xu, E.M. Cofer, C.A. Lavender, S.C. Turaga, A.M. Alexandari, Z. Lu, D.J. Harris, D. DeCaprio, Y. Qi, A. Kundaje, Y. Peng, L.K. Wiley, M.H.S. Segler, S.M. Boca, S.J. Swamidass, A. Huang, A. Gitter, and C.S. Greene, "Opportunities and obstacles for deep learning in biology and medicine", J. R. Soc. Interface, vol. 15, no. 141, p. 20170387, 2018.
[http://dx.doi.org/10.1098/rsif.2017.0387] [PMID: 29618526]
[23]
X.H. Han, J. Lei, and Y.W. Chen, "HEp-2 cell classification using k-support spatial pooling in deep CNNs", Proceedings of International Workshop on Deep Learning in Medical Image Analysis Granada, Spain, pp. 3-11, 2016.
[http://dx.doi.org/10.1007/978-3-319-46976-8_1]
[24]
A. Rakhlin, A. Shvets, V. Iglovikov, and A.A. Kalinin, "Deep convolutional neural networks for breast cancer histology image analysis", Proceedings of International Conference Image Analysis and Recognition Póvoa de Varzim Portugal, pp. 737-744, 2018.
[http://dx.doi.org/10.1007/978-3-319-93000-8_83]
[25]
E.M. Senan, F.W. Alsaade, M.I.A. Al-Mashhadani, T.H.H. Aldhyani, and M.H. Al-Adhaileh, "Classification of histopathological images for early detection of breast cancer using deep learning", Journal of Applied Science and Engineering, vol. 24, no. 3, pp. 323-329, 2021.
[26]
C. Swarup, "Biologically inspired cnn network for brain tumor abnormalities detection and features extraction from mri Images", Human-centric Computing and Information Sciences, vol. 12, p. 22, 2022.
[27]
A. Das, S.K. Mohapatra, and M.N. Mohanty, "Design of deep ensemble classifier with fuzzy decision method for biomedical image classification", Appl. Soft Comput., vol. 115, p. 108178, 2022.
[http://dx.doi.org/10.1016/j.asoc.2021.108178]
[28]
H. Barzekar, and Z. Yu, "C-Net: A reliable convolutional neural network for biomedical image classification", Expert Syst. Appl., vol. 187, p. 116003, 2022.
[http://dx.doi.org/10.1016/j.eswa.2021.116003]
[29]
N. Kumar, M. Sharma, V.P. Singh, C. Madan, and S. Mehandia, "An empirical study of handcrafted and dense feature extraction techniques for lung and colon cancer classification from histopathological images", Biomed. Signal Process. Control, vol. 75, p. 103596, 2022.
[http://dx.doi.org/10.1016/j.bspc.2022.103596]
[30]
J. Yosinski, J. Clune, Y. Bengio, and H. Lipson, "How transferable are features in deep neural networks?", arXiv:1411.1792, 2014.
[31]
B.V. Ginneken, A.A.A. Setio, C. Jacobs, and F. Ciompi, "Off-the-shelf convolutional neural network features for pulmonary nodule detection in computed tomography scans", Proceedings of The IEEE 12th International Symposium on Biomedical Imaging Brooklyn NY, pp. 286-289, 2015.
[http://dx.doi.org/10.1109/ISBI.2015.7163869]
[32]
L. Nanni, S. Ghidoni, and S. Brahnam, "Ensemble of convolutional neural networks for bioimage classification", Applied Computing and Informatics, vol. 17, no. 1, pp. 19-35, 2021.
[http://dx.doi.org/10.1016/j.aci.2018.06.002]
[33]
G. Li, J. Sun, Y. Song, J. Qu, Z. Zhu, and M.R. Khosravi, "Real-time classification of brain tumors in MRI images with a convolutional operator-based hidden Markov model", J. Real-Time Image Process., vol. 18, no. 4, pp. 1207-1219, 2021.
[http://dx.doi.org/10.1007/s11554-021-01072-4]
[34]
L. Nanni, S. Brahnam, S. Ghidoni, G. Maguolo, and M. Paci, "General purpose (GenP) bioimage ensemble combining new data augmentation techniques and handcrafted features",
[35]
R.F. Mansour, N.M. Alfar, S. Abdel-Khalek, M. Abdelhaq, R.A. Saeed, and R. Alsaqour, "Optimal deep learning based fusion model for biomedical image classification", Expert Syst., vol. 39, no. 3, p. e12764, 2022.
[http://dx.doi.org/10.1111/exsy.12764]
[36]
A. Passah, S.N. Sur, and B. Paul, "SAR image classification: a comprehensive study and analysis", IEEE Access, vol. 10, pp. 20385-20399, 2022.
[37]
C. Shorten, and T.M. Khoshgoftaar, "A survey on image data augmentation for deep learning", J. Big Data, vol. 6, no. 1, p. 60, 2019.
[http://dx.doi.org/10.1186/s40537-019-0197-0]
[38]
J.N. Kather, C.A. Weis, F. Bianconi, S.M. Melchers, L.R. Schad, T. Gaiser, A. Marx, and F.G. Zöllner, "Multi-class texture analysis in colorectal cancer histology", Sci. Rep., vol. 6, no. 1, p. 27988, 2016.
[http://dx.doi.org/10.1038/srep27988] [PMID: 27306927]
[39]
D.G. Lowe, "Distinctive Image Features from Scale-Invariant Keypoints", Int. J. Comput. Vis., vol. 60, no. 2, pp. 91-110, 2004.
[http://dx.doi.org/10.1023/B:VISI.0000029664.99615.94]
[40]
L. Nanni, M. Paci, F.L.C.D. Santos, S. Brahnam, and J. Hyttinen, Review on texture descriptors for image classification.Computer Vision and Simulation: Methods, Applications and Technology., Nova Publications: Hauppauge, NY, 2016.
[41]
K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition", In Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 27-30 June 2016, Las Vegas NV, 2016, pp. 770-778
[http://dx.doi.org/10.1109/CVPR.2016.90]
[42]
G. Huang, Z. Liu, L. Van Der Maaten, and K.Q. Weinberger, "Densely connected convolutional networks", In Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition Honolulu, HI, 2017, pp. 2261-2269
[43]
Q. Wang, P. Li, L. Zhang, and W. Zuo, "Towards effective codebookless model for image classification", Pattern Recognit., vol. 59, pp. 63-71, 2016.
[http://dx.doi.org/10.1016/j.patcog.2016.03.004]