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A Different Approach To The Chromosome Classification of Three Lallemantia Fisch. & C.A. Mey. Species By Using Local Binary Pattern (LBP)

Year 2022, Volume: 12 Issue: 1, 58 - 68, 01.03.2022
https://doi.org/10.21597/jist.884156

Abstract

The traditional karyotype studies are widely used in plant systematics to evaluate the positions of species. However, studies sometimes can not solve systematic problems. In recent years, computer-based systems have gained importance in contributing to the solution of the taxonomical problems. The aim of this study was to identify three Lallemantia species by using the LBP (local binary pattern) texture operator based on chromosome images in mitosis. LBP is the one of the most powerful and easily applicable tool for identifying local image patterns. In this study, microphotographs of 641 cells in the metaphase stage of mitosis were used. The LBP involves preprocessing, feature extraction, feature selection, and classification. Decision tree (DT), linear discriminant (LD), support vector machine (SVM), K-nearest neighbor (KNN), bagged tree (BT) and ensemble subspace nearest neighbor (SNN) were used in the classification stage. This study found that the best acting classifier was SNN because achievement rate was one hundred percent. Also, a dendrogram was formed to measure the similarity among the three species. As a result, LBP can be accepted as a tool for classifying plants by using chromosome images.

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References

  • Bereta M, Pedrycz W, 2013. Reformat M. local descriptors and similarity measures for frontal face recognition: A comparative analysis. Journal of Visual Communication and Image Representation 24: 1213-31.
  • Bhardwaj A, Kaur M, 2013. A review on plant recognition and classification techniques using leaf images. Int J Eng Trends Technol.: 86-91.
  • Chakraborty S, Singh SK, Chakraborty P, 2017. Local quadruple pattern: A novel descriptor for facial image recognition and retrieval. Computers & Electrical Engineering 62: 92-104.
  • Chang JD, Chen BH, Tsai CS, 2013. LBP-based fragile watermarking scheme for image tamper detection and recovery. 2013 International Symposium on Next-Generation Electronics IEEE: 173-6.
  • Davis P, 1982. Flora of Turkey and The East Aegean Island. Edinburgh University Press 7.
  • Dinc M, Munevver P, Dogu S, Yildirimli S, 2009. Micromorphological studies of Lallemantia L.(Lamiaceae) species growing in Turkey. Acta Biologica Cracoviensia Series Botanica 51: 45-54.
  • Ding C, Choi J, Tao D, Davis LS, 2015. Multi-directional multi-level dual-cross patterns for robust face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 38: 518-531.
  • Dolatyari A, & Kamrani A, 2015. Chromosome number and morphology of some accessions of four Lallemantia Fisch & CA Mey.(Lamiaceae) species from Iran. Wulfenia 2: 127-35.
  • Edmondson J, 1982. Lallemantia Fisch. & Mey. In: Davis, P. H. (ed.). Flora of Turkey and the East Aegean Islands. Edinburgh University Press Edinburgh 7: 297-313.
  • Elci S, 1982. Research methods and observations in cytogenetics. Firat University Faculty of Science and Arts Publishing (in Turkish) 3: 37-85.
  • El Khadiri I, Kas M, El Merabet Y, Ruichek Y, Touahni R, 2018. Repulsive-and-attractive local binary gradient contours: New and efficient feature descriptors for texture classification. Information Sciences 467: 634-53.
  • García-Pedrajas N, Ortiz-Boyer D, 2009. Boosting k-nearest neighbor classifier by means of input space projection. Expert Systems with Applications 36: 10570-82.
  • Goyal N, Kumar N, 2018. Plant species identification using leaf image retrieval: A study. 2018 International Conference on Computing, Power and Communication Technologies (GUCON) IEEE: 405-411.
  • Grinblat GL, Uzal LC, Larese MG, Granitto PM, 2016. Deep learning for plant identification using vein morphological patterns. Computers and Electronics in Agriculture 127: 418-24.
  • Guner A, Aslan S, 2012. Plant lists of Turkey: (vascular plants). Nezahat Gokyigit Botanical Garden Publishing (in Turkish).
  • Hong X, Zhao G, Pietikäinen M, Chen X, 2014. Combining LBP difference and feature correlation for texture description. IEEE Transactions on Image Processing 23: 2557-68.
  • Kamrani A, Riahi M, 2018. Using molecular data to test the monophyly of Lallemantia in the subtribe Nepetinae (Mentheae, Lamiaceae). Plant Biosystems-An International Journal Dealing with All Aspects of Plant Biology 152: 857-62.
  • Kaya Y, Kayci L, Uyar M, 2015. Automatic identification of butterfly species based on local binary patterns and artificial neural network. Applied Soft Computing 28: 132-7.
  • Kazak N, Koç M, 2016. Performance analysis of spiral neighbourhood topology based local binary patterns in texture recognition. International Journal of Applied Mathematics, Electronics and Computers 4(special issue): 383-341.
  • Lee SH, Chan CS, Mayo SJ, Remagnino P, 2017. How deep learning extracts and learns leaf features for plant classification. Pattern Recognition 71: 1-13.
  • Liao WH, 2010. Region description using extended local ternary patterns. 2010 20th International Conference on Pattern Recognition IEEE: 1003-1006.
  • Lukic M, Tuba E, Tuba M, 2017. Leaf recognition algorithm using support vector machine with Hu moments and local binary patterns. 2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI): IEEE: 000485-90.
  • Luo Y, Wu CM, Zhang Y, 2013. Facial expression recognition based on fusion feature of PCA and LBP with SVM. Optik-International Journal for Light and Electron Optics 124: 2767-70.
  • Martin E, Yildiz HK, Kahraman A, Binzat OK, Eroglu HE, 2018. Detailed chromosome measurements and karyotype asymmetry of some Vicia (Fabaceae) taxa from Turkey. Caryologia 71: 224-32.
  • Muthevi A, Uppu, RB, 2017. Leaf classification using completed local binary pattern of textures. 2017 IEEE 7th International Advance Computing Conference (IACC) IEEE: 870-874.
  • Nanni L, Franco A, 2011. Reduced reward-punishment editing for building ensembles of classifiers. Expert Systems with Application 38: 2395-2400.
  • Ojala T, Pietikäinen M, Mäenpää T, 2000. Gray scale and rotation invariant texture classification with local binary patterns. European Conference on Computer Vision Springer: 404-20.
  • Ojala T, Pietikäinen M, Mäenpää T, 2002. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis&Machine Intelligence: 971-987.
  • Ozcan T, Gezer E, Martin E, Dirmenci T, Altinordu, F. 2014. Karyotype analyses on the genus Lallemantia Fisch. & CA Mey.(Lamiaceae) from Turkey. Cytologia 79: 553-559.
  • Ozturk E, Kurnaz C, 2020. Görünüm tabanlı yuz tanıma yöntemleri kullanılarak cinsiyet belirleme. European Journal of Science and Technology special issue: 111-120.
  • Pahikkala T, Kari K, Mattila H, Lepisto A, Teuhola J, Nevalainen OS, et al. 2015. Classification of plant species from images of overlapping leaves. Computers and Electronics in Agriculture 118: 186-92.
  • Patterson RF, Savas E, 2016. On P-convergence of four-dimensional weighted sums of double random variables. Sains Malaysiana 45(7): 1177-1181.
  • Reyad YA, Berbar MA, Hussain M, 2014. Comparison of statistical, LBP, and multi-resolution analysis features for breast mass classification. Journal of Medical Systems 38:100.
  • Robnik-Šikonja M, Kononenko, I. 2003. Theoretical and empirical analysis of ReliefF and RReliefF. Machine Learning 53: 23-69.
  • Saleem G, Akhtar M, Ahmed N, Qureshi,W, 2019. Automated analysis of visual leaf shape features for plant classification. Computers and Electronics in Agriculture 157: 270-80.
  • Sun S, Zhang C, Zhang D, 2007. An experimental evaluation of ensemble methods for EEG signal classification. Pattern Recognition Letters 28: 2157-2163.
  • Seo J, Bakay M, Zhao P, Chen YW, Clarkson P, Shneiderman B, et al. 2003. Interactive color mosaic and dendrogram displays for signal/noise optimization in microarray data analysis. 2003 International Conference on Multimedia and Expo ICME'03 Proceedings (Cat No 03TH8698) IEEE: 461.
  • Tuncer T, Dogan S, Pławiak P, Acharya UR, 2019. Automated arrhythmia detection using novel hexadecimal local pattern and multilevel wavelet transform with ECG signals. Knowledge-based Systems 186: 104923.
  • Tuncer T, Aydemir E, 2020. An automated local binary pattern ship identification method by using sound. Acta Infologica 2020 4(1):57-63.
  • Uchida S, 2013. Image processing and recognition for biological images. Development, Growth & Differentiation 55: 523-49.
  • Wang X, Liang, J, Guo F, 2014. Feature extraction algorithm based on dual-scale decomposition and local binary descriptors for plant leaf recognition. Digital Signal Processing 34: 101-107.
  • Yigit E, Sabanci K, Toktas A, Kayabasi A, 2019. A study on visual features of leaves in plant identification using artificial intelligence techniques. Computers and Electronics in Agriculture 156: 369-77.
  • Zhao Y, Huang DS, Jia W, 2012. Completed local binary count for rotation invariant texture classification. IEEE Transactions on Image Processing 21: 4492-4497.

Yerel İkili Örnek Kullanarak Üç Lallemantia Fisch. & C.A. Mey. Türünün Kromozom Sınıflandırmasına Farklı Bir Yaklaşım

Year 2022, Volume: 12 Issue: 1, 58 - 68, 01.03.2022
https://doi.org/10.21597/jist.884156

Abstract

Geleneksel karyotip çalışmaları, bitki sistematiğinde türlerin konumlarını değerlendirmek için yaygın olarak kullanılmaktadır. Ancak çalışmalar bazen sistematik sorunları çözememektedir. Son yıllarda bilgisayar tabanlı sistemler taksonomik problemlerin çözümüne katkı sağlamada önem kazanmıştır. Bu çalışmanın amacı, mitozdaki kromozom görüntülerine dayalı yerel ikili örüntü doku operatörünü kullanarak üç Lallemantia türünü belirlemektir. Yerel ikili örüntü, yerel görüntü modellerini tanımlamak için en güçlü ve kolay uygulanabilir araçlardan biridir. Bu çalışmada mitozun metafaz evresindeki 641 hücrenin mikrofotoğrafları kullanılmıştır. Yerel ikili örüntü, ön işleme, özellik çıkarma, özellik seçme ve sınıflandırmayı içerir. Sınıflandırma aşamasında DT, LD, SVM, KNN, BT ve SNN kullanılmıştır. Bu çalışma, başarı oranı yüzde yüz olduğu için en iyi sınıflandırıcının SNN olduğunu buldu. Ayrıca, üç tür arasındaki benzerliği ölçmek için bir dendrogram oluşturulmuştur. Sonuç olarak, LBP, kromozom görüntülerini kullanarak bitkileri sınıflandırmak için bir araç olarak kabul edilebilir.

Project Number

-

References

  • Bereta M, Pedrycz W, 2013. Reformat M. local descriptors and similarity measures for frontal face recognition: A comparative analysis. Journal of Visual Communication and Image Representation 24: 1213-31.
  • Bhardwaj A, Kaur M, 2013. A review on plant recognition and classification techniques using leaf images. Int J Eng Trends Technol.: 86-91.
  • Chakraborty S, Singh SK, Chakraborty P, 2017. Local quadruple pattern: A novel descriptor for facial image recognition and retrieval. Computers & Electrical Engineering 62: 92-104.
  • Chang JD, Chen BH, Tsai CS, 2013. LBP-based fragile watermarking scheme for image tamper detection and recovery. 2013 International Symposium on Next-Generation Electronics IEEE: 173-6.
  • Davis P, 1982. Flora of Turkey and The East Aegean Island. Edinburgh University Press 7.
  • Dinc M, Munevver P, Dogu S, Yildirimli S, 2009. Micromorphological studies of Lallemantia L.(Lamiaceae) species growing in Turkey. Acta Biologica Cracoviensia Series Botanica 51: 45-54.
  • Ding C, Choi J, Tao D, Davis LS, 2015. Multi-directional multi-level dual-cross patterns for robust face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 38: 518-531.
  • Dolatyari A, & Kamrani A, 2015. Chromosome number and morphology of some accessions of four Lallemantia Fisch & CA Mey.(Lamiaceae) species from Iran. Wulfenia 2: 127-35.
  • Edmondson J, 1982. Lallemantia Fisch. & Mey. In: Davis, P. H. (ed.). Flora of Turkey and the East Aegean Islands. Edinburgh University Press Edinburgh 7: 297-313.
  • Elci S, 1982. Research methods and observations in cytogenetics. Firat University Faculty of Science and Arts Publishing (in Turkish) 3: 37-85.
  • El Khadiri I, Kas M, El Merabet Y, Ruichek Y, Touahni R, 2018. Repulsive-and-attractive local binary gradient contours: New and efficient feature descriptors for texture classification. Information Sciences 467: 634-53.
  • García-Pedrajas N, Ortiz-Boyer D, 2009. Boosting k-nearest neighbor classifier by means of input space projection. Expert Systems with Applications 36: 10570-82.
  • Goyal N, Kumar N, 2018. Plant species identification using leaf image retrieval: A study. 2018 International Conference on Computing, Power and Communication Technologies (GUCON) IEEE: 405-411.
  • Grinblat GL, Uzal LC, Larese MG, Granitto PM, 2016. Deep learning for plant identification using vein morphological patterns. Computers and Electronics in Agriculture 127: 418-24.
  • Guner A, Aslan S, 2012. Plant lists of Turkey: (vascular plants). Nezahat Gokyigit Botanical Garden Publishing (in Turkish).
  • Hong X, Zhao G, Pietikäinen M, Chen X, 2014. Combining LBP difference and feature correlation for texture description. IEEE Transactions on Image Processing 23: 2557-68.
  • Kamrani A, Riahi M, 2018. Using molecular data to test the monophyly of Lallemantia in the subtribe Nepetinae (Mentheae, Lamiaceae). Plant Biosystems-An International Journal Dealing with All Aspects of Plant Biology 152: 857-62.
  • Kaya Y, Kayci L, Uyar M, 2015. Automatic identification of butterfly species based on local binary patterns and artificial neural network. Applied Soft Computing 28: 132-7.
  • Kazak N, Koç M, 2016. Performance analysis of spiral neighbourhood topology based local binary patterns in texture recognition. International Journal of Applied Mathematics, Electronics and Computers 4(special issue): 383-341.
  • Lee SH, Chan CS, Mayo SJ, Remagnino P, 2017. How deep learning extracts and learns leaf features for plant classification. Pattern Recognition 71: 1-13.
  • Liao WH, 2010. Region description using extended local ternary patterns. 2010 20th International Conference on Pattern Recognition IEEE: 1003-1006.
  • Lukic M, Tuba E, Tuba M, 2017. Leaf recognition algorithm using support vector machine with Hu moments and local binary patterns. 2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI): IEEE: 000485-90.
  • Luo Y, Wu CM, Zhang Y, 2013. Facial expression recognition based on fusion feature of PCA and LBP with SVM. Optik-International Journal for Light and Electron Optics 124: 2767-70.
  • Martin E, Yildiz HK, Kahraman A, Binzat OK, Eroglu HE, 2018. Detailed chromosome measurements and karyotype asymmetry of some Vicia (Fabaceae) taxa from Turkey. Caryologia 71: 224-32.
  • Muthevi A, Uppu, RB, 2017. Leaf classification using completed local binary pattern of textures. 2017 IEEE 7th International Advance Computing Conference (IACC) IEEE: 870-874.
  • Nanni L, Franco A, 2011. Reduced reward-punishment editing for building ensembles of classifiers. Expert Systems with Application 38: 2395-2400.
  • Ojala T, Pietikäinen M, Mäenpää T, 2000. Gray scale and rotation invariant texture classification with local binary patterns. European Conference on Computer Vision Springer: 404-20.
  • Ojala T, Pietikäinen M, Mäenpää T, 2002. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis&Machine Intelligence: 971-987.
  • Ozcan T, Gezer E, Martin E, Dirmenci T, Altinordu, F. 2014. Karyotype analyses on the genus Lallemantia Fisch. & CA Mey.(Lamiaceae) from Turkey. Cytologia 79: 553-559.
  • Ozturk E, Kurnaz C, 2020. Görünüm tabanlı yuz tanıma yöntemleri kullanılarak cinsiyet belirleme. European Journal of Science and Technology special issue: 111-120.
  • Pahikkala T, Kari K, Mattila H, Lepisto A, Teuhola J, Nevalainen OS, et al. 2015. Classification of plant species from images of overlapping leaves. Computers and Electronics in Agriculture 118: 186-92.
  • Patterson RF, Savas E, 2016. On P-convergence of four-dimensional weighted sums of double random variables. Sains Malaysiana 45(7): 1177-1181.
  • Reyad YA, Berbar MA, Hussain M, 2014. Comparison of statistical, LBP, and multi-resolution analysis features for breast mass classification. Journal of Medical Systems 38:100.
  • Robnik-Šikonja M, Kononenko, I. 2003. Theoretical and empirical analysis of ReliefF and RReliefF. Machine Learning 53: 23-69.
  • Saleem G, Akhtar M, Ahmed N, Qureshi,W, 2019. Automated analysis of visual leaf shape features for plant classification. Computers and Electronics in Agriculture 157: 270-80.
  • Sun S, Zhang C, Zhang D, 2007. An experimental evaluation of ensemble methods for EEG signal classification. Pattern Recognition Letters 28: 2157-2163.
  • Seo J, Bakay M, Zhao P, Chen YW, Clarkson P, Shneiderman B, et al. 2003. Interactive color mosaic and dendrogram displays for signal/noise optimization in microarray data analysis. 2003 International Conference on Multimedia and Expo ICME'03 Proceedings (Cat No 03TH8698) IEEE: 461.
  • Tuncer T, Dogan S, Pławiak P, Acharya UR, 2019. Automated arrhythmia detection using novel hexadecimal local pattern and multilevel wavelet transform with ECG signals. Knowledge-based Systems 186: 104923.
  • Tuncer T, Aydemir E, 2020. An automated local binary pattern ship identification method by using sound. Acta Infologica 2020 4(1):57-63.
  • Uchida S, 2013. Image processing and recognition for biological images. Development, Growth & Differentiation 55: 523-49.
  • Wang X, Liang, J, Guo F, 2014. Feature extraction algorithm based on dual-scale decomposition and local binary descriptors for plant leaf recognition. Digital Signal Processing 34: 101-107.
  • Yigit E, Sabanci K, Toktas A, Kayabasi A, 2019. A study on visual features of leaves in plant identification using artificial intelligence techniques. Computers and Electronics in Agriculture 156: 369-77.
  • Zhao Y, Huang DS, Jia W, 2012. Completed local binary count for rotation invariant texture classification. IEEE Transactions on Image Processing 21: 4492-4497.
There are 43 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Bilgisayar Mühendisliği / Computer Engineering
Authors

İrfan Emre 0000-0003-0591-3397

Türker Tuncer 0000-0002-1425-4664

Sengul Dogan 0000-0001-9677-5684

Murat Kurşat 0000-0002-0861-4213

Osman Gedik 0000-0002-4816-3154

Yaşar Kıran 0000-0002-3225-2080

Project Number -
Publication Date March 1, 2022
Submission Date February 21, 2021
Acceptance Date December 5, 2021
Published in Issue Year 2022 Volume: 12 Issue: 1

Cite

APA Emre, İ., Tuncer, T., Dogan, S., Kurşat, M., et al. (2022). A Different Approach To The Chromosome Classification of Three Lallemantia Fisch. & C.A. Mey. Species By Using Local Binary Pattern (LBP). Journal of the Institute of Science and Technology, 12(1), 58-68. https://doi.org/10.21597/jist.884156
AMA Emre İ, Tuncer T, Dogan S, Kurşat M, Gedik O, Kıran Y. A Different Approach To The Chromosome Classification of Three Lallemantia Fisch. & C.A. Mey. Species By Using Local Binary Pattern (LBP). J. Inst. Sci. and Tech. March 2022;12(1):58-68. doi:10.21597/jist.884156
Chicago Emre, İrfan, Türker Tuncer, Sengul Dogan, Murat Kurşat, Osman Gedik, and Yaşar Kıran. “A Different Approach To The Chromosome Classification of Three Lallemantia Fisch. & C.A. Mey. Species By Using Local Binary Pattern (LBP)”. Journal of the Institute of Science and Technology 12, no. 1 (March 2022): 58-68. https://doi.org/10.21597/jist.884156.
EndNote Emre İ, Tuncer T, Dogan S, Kurşat M, Gedik O, Kıran Y (March 1, 2022) A Different Approach To The Chromosome Classification of Three Lallemantia Fisch. & C.A. Mey. Species By Using Local Binary Pattern (LBP). Journal of the Institute of Science and Technology 12 1 58–68.
IEEE İ. Emre, T. Tuncer, S. Dogan, M. Kurşat, O. Gedik, and Y. Kıran, “A Different Approach To The Chromosome Classification of Three Lallemantia Fisch. & C.A. Mey. Species By Using Local Binary Pattern (LBP)”, J. Inst. Sci. and Tech., vol. 12, no. 1, pp. 58–68, 2022, doi: 10.21597/jist.884156.
ISNAD Emre, İrfan et al. “A Different Approach To The Chromosome Classification of Three Lallemantia Fisch. & C.A. Mey. Species By Using Local Binary Pattern (LBP)”. Journal of the Institute of Science and Technology 12/1 (March 2022), 58-68. https://doi.org/10.21597/jist.884156.
JAMA Emre İ, Tuncer T, Dogan S, Kurşat M, Gedik O, Kıran Y. A Different Approach To The Chromosome Classification of Three Lallemantia Fisch. & C.A. Mey. Species By Using Local Binary Pattern (LBP). J. Inst. Sci. and Tech. 2022;12:58–68.
MLA Emre, İrfan et al. “A Different Approach To The Chromosome Classification of Three Lallemantia Fisch. & C.A. Mey. Species By Using Local Binary Pattern (LBP)”. Journal of the Institute of Science and Technology, vol. 12, no. 1, 2022, pp. 58-68, doi:10.21597/jist.884156.
Vancouver Emre İ, Tuncer T, Dogan S, Kurşat M, Gedik O, Kıran Y. A Different Approach To The Chromosome Classification of Three Lallemantia Fisch. & C.A. Mey. Species By Using Local Binary Pattern (LBP). J. Inst. Sci. and Tech. 2022;12(1):58-6.