Crop Images Segmentation using Adaptive Morphologic Descriptors

Miguel Ángel Gil Ríos, Juan Manuel López Hernández, Dolores Juárez Ramírez, Maria del Carmen Ruiz Robledo, Laura Paulina Badillo Canchola, Ariana Aranda López

Abstract


This research is focused on the segmentation improvement of crop images by using adaptive morphologic descriptors instead of classic algorithms like K-means and the top-hat operator using predefined shapes like disk or diamond. Obtained results shows that using an adaptive morphologic descriptor improves the segmentation performance against the classic shapes like disc and diamond. In order to measure the process a set of 60 crop images was used including their respective ground-truth images. The images were segmented using the K-Means algorithm and the top-hat operator with the disk and diamond shapes at different sizes into a range to validate their performance. In order to generate the adaptive morphologic descriptor, the Univariated Marginal Distribution Algorithm was used with no constraints by exploring a range of different sizes. Also, performance metrics like receiver operating characteristic and accuracy rate were applied to the generated data in order to assess the results.


Keywords


image segmentation; morphologic descriptor; crop detection; UMDA; algorithms; K-Means algorithm; computing; digital images; adaptive structural elements; mathematical morphology

Full Text:

XML PDF

References


Alba E., Madera J., Dorronsoro B., Ochoa A. and Soto M. (2006). Theory and Practice of Cellular UMDA for Discrete Optimization, 242-251. Parallel Problem Solving from Nature - PPSN IX. Vol. 4193. Springer.

Bouraoui B., Ronse C., Baruthio J., Passat N. and Germain P. (2008). Fully automatic 3D segmentation of coronary arteries based on mathematical motphology, 1059-1062. 5th IEEE International Symposium on Biomedical Imaging (ISBI). IEEE.

Cruz Aceves I., Hernández Aguirre A. and Aviña Cervantes J.G. (2015). Automatic segmentation of coronary arteries using a multiscale Top-Hat operator and multiobjective optimization, 297-320. Revista Electrónica Nova Scientia.

Eiho S. and Qian Y. (1997). Detection of coronary artery tree using morphological operator, 696-699. Computers in cardiology. Vol. 24. IEEE.

Frangi A.F., Niessen, W.J., Vincken K.L. and Viergever M.A. (1998). Multiscale vessel enhancement filtering. Medical Image Computing and Computer-Assisted Intervention — MICCAI’98. Vol. 1496. Springer.

Guerrero-Turrubiates J.J. Cruz-Aceves I., Ledesma S., Sierra-Hernández J.M., Velasco J., Avina-Cervantes J.G., Avila-Garcia M.S., Rostro-Gonzalez H. and Rojas-Laguna R. (2017). Fast parabola detection using estimation of distribution algorithms, 1-13. Computational and Mathematical Methods in Medicine. Hindawi.

Haugh S. and Ostermann J. (2015). A Crop/Weed Field Image Dataset for the Evaluation of Computer Vision Based Precision Agriculture Tasks, 105-116. Springer Editorial.

Hauschild M. and Pelikan M. (2011). An introduction and survey of estimation of distribution algorithms, 111-128. Swarm and Evolutionary Computation. Vol. 1.

Heinz M. and Mahnig T. (2001). Evolutionary algorithms: From recombination to search distributions, 135-173. Springer.

Hisashi H. (2005). Estimation of Distribution Algorithms with Mutation, 112-121. European Conference on Evolutionary Computation in Combinatorial Optimization. Springer.

Hu, J., Kashi, R., Lopresti, D., Nagy, G. and Wilfong, G. (2001). Why Table Ground-Truthing is Hard, 129-133. Proceedings of Sixth International Conference on Document Analysis and Recognition. IEEE.

Jaware, T., Badgujar R.D. and Patil, G. (2012). Crop Disease Detection using Image Segmentation. Proceedings of "Conference on Advances in Communication and Computing (NCACC'12)”, 190-194. India: R.C. Patel Institute of Technology, Shirpur, Dist. Dhule, Maharastra.

Jian-zhuang, L. and Wen-qing, L. (1991). The Automatic threshold of gray level pictures via Two-dimentional Otsu Method, 325-327. International Conference on Circuits and Systems. China.

MacQueen J. (1967). Some Methods for Classification and Analysis of Multivariate Observations. Fifth Berkeley Symposium. Los Angeles: University of California.

Marler R. T. and Arora J. S. (2010). The weighted sum method for multi-objective optimization: new insights, Struct, 853-862. Multidiscipl. Optim. Vol. 41.

Meyer F. (1977). Contrast feature extraction. Quantitative Analysis of Microstructures in Material Scences, Biology and Medicine.

Patil R., Udgave S., More S., Nemishte D. and Kasture M. (2016). Grape Leaf Disease Detection Using K-means Clustering Algorithm. International Research Journal of Engineering and Technology (IRJET), 2330-2333. Vol. 3.

Qu, Z. and Hang, L. Research on Image Segmentation Based on the Improved Otsu Algorithm. (2010). 2010 Second International Conference on Intelligent Human-Machine Systems and Cybernetics, 228-231. IEEE.

Soille P. (1999). Morphological Image Analysis: Principles and Applications. Springer-Verlag Editorial.

Sun K. and Sang, N. (2008). Morphological enhancement of vascular angiogram with multiscale detected by gabor filters. Electronic letters. Vol. 4.

Swets J. (1988). Measuring the Accuracy of Diagnostic Systems. Science 240(4857), 1285-1293.

Topon Kumar P. and Hitoshi I. (2003). Reinforcement Learning Estimation of Distribution Algorithm, 1259-1270. Genetic and Evolutionary Computation Conference. Springer.

Vala, H. and Baxi, A. (2013). A Review on Otsu Image Segmentation Algorithm, 387-389. Vol. 2.

Wang S, Li B. and Zhou S. (2012). A Segmentation Method of Coronary Angiograms Based on Multi-scale Filtering and Region-Growing. Conference: Biomedical Engineering and Biotechnology (iCBEB), 2012.

Zhu W., Zeng N. and Wang N. (2010). Sensitivity, Specificity, Accuracy, Associated Confidence Interval and ROC Analysis with Practical SAS ® Implementations. NESUG 2010.




DOI: https://doi.org/10.21640/ns.v12i24.2152

Refbacks

  • There are currently no refbacks.


Copyright (c) 2020 Nova Scientia

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Scope

Nova Scientia is a multidisciplinary, electronic publication that publishes twice a year in the months of May and November; it is published by the Universidad De La Salle Bajío and aims to distribute unpublished and original papers from the different scientific disciplines written by national and international researchers and academics. It does not publish reviews, bibliographical revisions, or professional applications.

Nova Scientia, year 12, issue 24, May – October 2020, is a biannual journal printed by the Universidad De La Salle Bajío, with its address: Av. Universidad 602, Col. Lomas del Campestre, C. P. 37150, León, Gto. México. Phone: (52) 477 214 3900, http://novascientia.delasalle.edu.mx/. Chief editor: Ph.D. Ramiro Rico Martínez. ISSN 2007 - 0705. Copyright for exclusive use No. 04-2008-092518225500/102, Diffusion rights via computer net 04 - 2008 – 121011584800-203 both granted by the Instituto Nacional del Derecho de Autor.

Editor responsible for updating this issue: Direction of Research Department of the Universidad De La Salle Bajío, last updated on May 15th, 2020.