Abstract
So far, analysis and classification of urine cells number has become an important topic for medical diagnosis of some diseases. Therefore, in this study, we suggest a new technique based on Adaptive Discrete Wavelet Entropy Energy and Neural Network Classifier (ADWEENN) for Recognition of Urine Cells from Microscopic Images Independent of Rotation and Scaling. Some digital image processing methods such as noise reduction, contrast enhancement, segmentation, and morphological process are used for feature extraction stage of this ADWEENN in this study. Nowadays, the image processing and pattern recognition topics have come into prominence. The image processing concludes operation and design of systems that recognize patterns in data sets. In the past years, very difficulty in classification of microscopic images was the deficiency of enough methods to characterize. Lately, it is seen that, multi-resolution image analysis methods such as Gabor filters, discrete wavelet decompositions are superior to other classic methods for analysis of these microscopic images. In this study, the structure of the ADWEENN method composes of four stages. These are preprocessing stage, feature extraction stage, classification stage and testing stage. The Discrete Wavelet Transform (DWT) and adaptive wavelet entropy and energy is used for adaptive feature extraction in feature extraction stage to strengthen the premium features of the Artificial Neural Network (ANN) classifier in this study. Efficiency of the developed ADWEENN method was tested showing that an avarage of 97.58 % recognition succes was obtained.



Similar content being viewed by others
References
Lamchiagdhasea, P., Preechaborisutkula, K., Lomsomboonb, P., Srisucharta, P., Tantinitia, P., Khanurab, N., and Preechaborisutkula, B., Urine sediment examination: A comparison between the manual method and the iQ200 automated urine microscopy analyzer. Clin. Chim. Acta 384:28–34, 2007.
Lakatos, J., Bodor, T., Zidarics, Z., and Nagy, J., Data processing of digital recordings of microscopic examination of urinary sediment. Clin. Chim. Acta 297:225–237, 2000.
Li, Y., Li, Z., Mei, Y., and Zhang, J., Detecting algorithm based gabor in microscopic ımage. Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, Guangzhou, pp. 18–21, 2005.
Li, Y., and Zeng, X., A new strategy for urinary sediment segmentation based on wavelet, morphology and combination method. Comput. Methods Prog. Biomed. 84:162–173, 2006.
Zhang, Z., Xia, S., and Duan, H., Cellular neuralm network based urinary ımage segmentation, natural computation, 2007. ICNC 2007. Third International Conference 2:285–289, 2007.
Jiang X, Nie S (2007) Urine Sediment Image Segmentation based on Level Set and Mumford-Shah Model, Bioinformatics and Biomedical Engineering, pp.1028 – 1030
Luo H, Ma S, Wu D, Xu Z (2007) Mumford-Shah Segmentation for Microscopic Image of the Urinary Sediment, Bioinformatics and Biomedical Engineering, pp.861 – 863
Murasaki, Y., Tanigiichi, K., and Murakami, Y., Pattern recognition of urinary sediment ımages applying a fuzzy-neural network. Trans. of IECE 12:2630–2632, 1993.
Zeng, N., Taniguchi, K., Watanaeie, S., and Nakano, Y., Nakamoto H (2000) a precise classifier for the substances in urinary sediment images based on neural networks and fuzzy reasoning, systems, Man, and cybernetics. IEEE International Conference 3:1928–1933, 2000.
Dong, L., Yuan, S., Liu, G., and Zhou, L., Classification of urinary sediments ımage based on bayesian classifier. Mechatronics and Automation 556–560, 2007.
Qian, J., Fang, B., Li, C., and Chen, L., Coarse-to-fine particle segmentation in microscopic urinary ımages. 3rd International Conference on Bioinformatics and Biomedical Engineering, Beijing, 2009.
Fang, B., Hsu, W., and Lee, M., On the accurate counting of tumor cells. NanoBioscience, IEEE Transactions 2:94–103, 2003.
Pun, C., and Lee, M., Extraction of shift invariant wavelet features for classification of images with different sizes. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(9):1228–1233, 2004.
Li, C., Fang, B., Wang, Y., Lu, G. Z., Qian, J., and Chen, L., Automatic detecting and recognition of casts in urine sediment images. Proceedings of the 2009 International Conference on Wavelet Analysis and Pattern Recognition, Baoding, 2009.
Tuceryan M and Jain AK (1993) Texture analysis, handbook of pattern recognition and computer vision, World Scientific, 235–276
Conners, R. W., and Harlow, C. A., A theoretical comparison of texture algorithms. IEEE Trans. Pattern Analysis and Machine Intelligence 2:204–222, 1980.
Bovik A C, Clark M and Geisler W S (1990) Multichannel Texture Analysis Using Localized Spatial Filters, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 12
Teuner, A., Pichler, O., and Hosticka, B. J., Unsupervised texture segmentation of images using tuned matched Gabor filters, IEEE trans. Image Processing 6(4):863–870, 1995.
Chang, T., and Kuo, C. C. J., Texture analysis and classification with tree-structured wavelet transform, IEEE trans. Image Processing 2:429–441, 1993.
Laine, A., and Fan, J., Texture classification by wavelet packet signatures. IEEE Trans, Pattern Analysis and Machine Intelligence 15(11):1186–1191, 1993.
Unser, M., Texture classification and segmentation using wavelet frames. IEEE Trans. Image Processing 4:1549–1560, 1995.
Pun C and Lee M (2003) Log-Polar Wavelet Energy Signatures for Rotation and Scale Invariant Texture Classification, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 25, No.5.
Chang, T., and Kuo, J., Texture analysis and classification with tree structured wavelet transform. IEEE Trans. Image Processing 2(4):429–441, 1993.
Arivazhagan, S., and Ganesan, L., Texture classification using wavelet transform. Pattern Recogn. Lett. 24:1513–1521, 2003.
Weszka, J. S., Dyer, C. R., and Rosenfeld, A., A comparative study of texture measures for terrain classification. IEEE Trans. System Man Cybernat. SMC-6(4):269–286, 1976.
Davis, L. S., Johns, S. A., and Aggarwal, J. K., Texture analysis using generalized co-occurrence matrices. IEEE Trans. Pattern Anal. Machine Intell. PAMI-1:251–259, 1979.
Chang, T., and Kuo, C. C. J., Texture analysis and classification with tree-structured wavelet transform. IEEE Trans. Image Process. 2(4):429–440, 1993.
Haley, G. M., and Manjunath, B. S., Rotation invariant texture classification using modified Gabor filters. Proc. IEEE 262–265, 1995.
Manjunath, B. S., and Ma, W. Y., Texture features for browsing and retrieval of image data. IEEE Trans. Pattern Anal. Machine Intell. 18(8):837–842, 1996.
Wu, W. R., and Wei, S. C., Rotation and gray scale transform invariant texture classification using spiral resampling, sub band decomposition and hidden Markov model. IEEE Trans. Image Process. 5(10):1423–1433, 1996.
Avci, E., Turkoglu, I., and Poyraz, M., Intelligent target recognition based on wavelet packet neural network. Experts Systems with Applications 29(1):175–182, 2005.
Turkoglu, I., Arslan, A., and Ilkay, E., An Intelligent system for diagnosis of the heart valve diseases with wavelet packet neural networks. Computer in Biology and Medicine 33:319–331, 2003.
Avci E, Turkoglu I and Poyraz M (2005) Intelligent Target Recognition Based on Wavelet Adaptive Network Based Fuzzy Inference System, Lecture Notes in Computer Science, Springer-Verlag, vol. 3522/2005: 594–601.
Avci, E., and Avci, D., A novel approach for digital radio signal classification: Wavelet packet energy–multiclass support vector machine (WPE–MSVM). Expert Syst. Appl. 34(3):2140–2147, 2008.
Avci, E., and Akpolat, Z. H., Speech recognition using a wavelet packet adaptive network based fuzzy inference system. Expert Syst. Appl. 31(3):495–503, 2006.
Dogantekin, E., Avci, E., and Erkus, O., Automatic RNA virus classification using the entropy-ANFIS method. Digital Signal Processing 23(4):1209–1215, 2013.
Eminaga, O., Özgür, E., Semjonow, A., Herden, J., Akbarov, I., Tok, A., Engelmann, U., and Wille, S., Linkage of data from diverse data sources (LDS): A data combination model provides clinical data of corresponding specimens in biobanking information system. J. Med. Syst. 37:9975, 2013.
Hsu, W., and Pan, J., The secure authorization model for healthcare information system. J. Med. Syst. 37:9974, 2013.
Wang, S., Construct an optimal triage prediction model: A case study of the emergency department of a teaching hospital in Taiwan. J. Med. Syst. 37:9968, 2013.
Goker, I., Osman, O., Ozekes, S., Baslo, M. B., Ertas, M., and Ulgen, Y., Classification of juvenile Myoclonic epilepsy data acquired through scanning electromyography with machine learning algorithms. J. Med. Syst. 36(5):2705–2711, 2012.
Naghibi, S., Teshnehlab, M., and Shoorehdeli, M. A., Breast cancer classification based on advanced multi dimensional fuzzy neural network. J. Med. Syst. 36(5):2713–2720, 2012.
Chikh, M. A., Meryem, S., Settouti, N., Chikh, M. A., and Saidi, M., Nesma SettoutiDiagnosis of diabetes diseases using an artificial ımmune recognition System2 (AIRS2) with fuzzy K- nearest neighbor. J. Med. Syst. 36(5):2721–2729, 2012.
Avci, D., and Varol, A., An expert diagnosis system for classification of human parasite eggs based on multi-class SVM. Expert Syst. Appl. 36:43–48, 2009.
Lamchiagdhase, P., Preechaborisutkul, K., Lomsomboon, P., Srisuchart, P., Tantiniti, P., Khan-u-ra, N., et al., Urine sediment examination: A comparison between the manual method and the iQ200 automated urine microscopy analyzer. Clin Chimica Acta 358:167–174, 2005.
Yuzhang, W. E. I., The research of urinary sediment visual component analysis based on fuzzy clustering [D]. Nanjing Information Engineering University 2008(3–4):15–32, 2008.
Maneesukasem, W., and Pintavirooj, C., Urine sediment image segmentation based on feedforward backpropagation neural network. Biomedical Engineering International Conference (BMEiCON) 2012:1–4, 2012.
Tangsuksant, W., Pintavirooj, C., and Taertulakarn, S., Development algorithm to count blood cells in urine sediment using ANN and hough transform. Biomedical Engineering International Conference (BMEiCON) 6:1–4, 2013.
Zhou, X., Xiao, X., and Ma, C., A study of automatic recognition and counting system of urine-sediment visual components. Biomedical Engineering and Informatics (BMEI) 1:78–81, 2010.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Avci, D., Leblebicioglu, M.K., Poyraz, M. et al. A New Method Based on Adaptive Discrete Wavelet Entropy Energy and Neural Network Classifier (ADWEENN) for Recognition of Urine Cells from Microscopic Images Independent of Rotation and Scaling. J Med Syst 38, 7 (2014). https://doi.org/10.1007/s10916-014-0007-3
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10916-014-0007-3