Otsu Method for Chicken Egg Embryo Detection based-on Increase Image Quality
Keywords:
Embryo Egg Detection, Image Analysis, Image Segmentation, K-means Clustering, Otsu, Thresholding
Abstract
Detection of chicken egg embryos using image processing has limitations and needs some processes for improvement. By human vision, the previous process used binoculars and candling using light/beams directed at the chicken eggs in the incubator. In this study, we propose the application of image segmentation using the Otsu method in detecting chicken egg embryos. This method uses image segmentation with increased image quality (preprocessing) by several methods such as resizing, grayscaling, image adjustment, and image enhancement. These processes produce a better image and can be used for input in the segmentation process. In addition, this study compares several segmentation methods in detecting chicken egg embryos, such as thresholding, Otsu basic, and k-means clustering. The results show that our proposed method produced segmentation images to detect chicken egg embryos of 200 datasets images. This method has a faster process and can create a uniform segmentation than other methods. However, other methods can also detect chicken egg embryos. The method’s accuracy proposed in this study increased by 1.5% compared to other methods. In addition, the resulting SSIM value has a percentage close to and more than 90%, which means that the segmentation of the results obtained can be used to detect chicken egg embryos.
Downloads
Download data is not yet available.
References
[1] S. Saifullah, A. P. Suryotomo, and Yuhefizar, “Detection of Chicken Egg Embryos using BW Image Segmentation and Edge Detection Methods,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 6, pp. 1062–1069, Dec. 2021, doi: 10.29207/resti.v5i6.3540.
[2] J. Dong et al., “Prediction of infertile chicken eggs before hatching by the Naïve-Bayes method combined with visible near infrared transmission spectroscopy,” Spectrosc. Lett., pp. 1–10, Apr. 2020, doi: 10.1080/00387010.2020.1748061.
[3] L. Huang, A. He, M. Zhai, Y. Wang, R. Bai, and X. Nie, “A Multi-Feature Fusion Based on Transfer Learning for Chicken Embryo Eggs Classification,” Symmetry (Basel)., vol. 11, no. 5, p. 606, May 2019, doi: 10.3390/sym11050606.
[4] L. Geng, Y. Hu, Z. Xiao, and J. Xi, “Fertility Detection of Hatching Eggs Based on a Convolutional Neural Network,” Appl. Sci., vol. 9, no. 7, p. 1408, Apr. 2019, doi: 10.3390/app9071408.
[5] E. H. Rachmawanto et al., “Eggs Classification based on Egg Shell Image using K-Nearest Neighbors Classifier,” in 2020 International Seminar on Application for Technology of Information and Communication (iSemantic), Sep. 2020, pp. 50–54, doi: 10.1109/iSemantic50169.2020.9234305.
[6] W. Lumchanow and S. Udomsiri, “Combination of GLCM and KNN Classification for Chicken Embryo Development Recognition,” in 2019 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT-NCON), Jan. 2019, pp. 322–325, doi: 10.1109/ECTI-NCON.2019.8692272.
[7] X.-S. Jin, J. Li, and X. Du, “Image Classification of Chicken Embryo Based on Matched Filter and Skeleton Curvature Feature,” J. Phys. Conf. Ser., vol. 1651, p. 12196, 2020, doi: 10.1088/1742-6596/1651/1/012196.
[8] S. Saifullah, “K-Means Clustering for Egg Embryo’s Detection Based-on Statistical Feature Extraction Approach of Candling Eggs Image,” SINERGI, vol. 25, no. 1, pp. 43–50, 2020, doi: 10.22441/sinergi.2021.1.006.
[9] S. Saifullah, “Segmentasi Citra Menggunakan Metode Watershed Transform Berdasarkan Image Enhancement Dalam Mendeteksi Embrio Telur,” Syst. Inf. Syst. Informatics J., vol. 5, no. 2, pp. 53–60, 2019, doi: 10.29080/systemic.v5i2.798.
[10] S. Saifullah, “Segmentation for embryonated Egg Images Detection using the K-Means Algorithm in Image Processing,” 2020 Fifth Int. Conf. Informatics Comput., pp. 1–7, Nov. 2020, doi: 10.1109/ICIC50835.2020.9288648.
[11] S. Saifullah and A. P. Suryotomo, “Thresholding and Hybrid CLAHE-HE for Chicken Egg Embryo Segmentation,” in 2021 International Conference on Communication & Information Technology (ICICT), Jun. 2021, pp. 268–273, doi: 10.1109/ICICT52195.2021.9568444.
[12] Sunardi, A. Yudhana, and S. Saifullah, “Identification of Egg Fertility Using Gray Level Co-Occurrence Matrix and Backpropagation,” Adv. Sci. Lett., vol. 24, no. 12, pp. 9151–9156, 2018, doi: 10.1166/asl.2018.12115.
[13] S. Saifullah and V. A. Permadi, “Comparison of Egg Fertility Identification based on GLCM Feature Extraction using Backpropagation and K-means Clustering Algorithms,” in Proceeding - 2019 5th International Conference on Science in Information Technology: Embracing Industry 4.0: Towards Innovation in Cyber Physical System, ICSITech 2019, Oct. 2019, pp. 140–145, doi: 10.1109/ICSITech46713.2019.8987496.
[14] S. Saifullah, “K-means Segmentation Based-on Lab Color Space for Embryo Egg Detection,” arXiv Prepr. arXiv2103.02288, Mar. 2021, [Online]. Available: http://arxiv.org/abs/2103.02288.
[15] S. Saifullah and A. P. Suryotomo, “Thresholding and hybrid CLAHE-HE for chicken egg embryo segmentation,” Int. Conf. Commun. Inf. Technol., 2021.
[16] D. Indra, T. Hasanuddin, R. Satra, and N. R. Wibowo, “Eggs Detection Using Otsu Thresholding Method,” in 2018 2nd East Indonesia Conference on Computer and Information Technology (EIConCIT), Nov. 2018, pp. 10–13, doi: 10.1109/EIConCIT.2018.8878517.
[17] J. G. C. Rancapan, E. R. Arboleda, J. L. Dioses, and R. M. Dellosa, “Egg fertility detection using image processing and fuzzy logic,” Int. J. Sci. Technol. Res., vol. 8, no. 10, pp. 3228–3230, Oct. 2019.
[18] L. Liu and M. O. Ngadi, “Detecting Fertility and Early Embryo Development of Chicken Eggs Using Near-Infrared Hyperspectral Imaging,” Food Bioprocess Technol., vol. 6, no. 9, pp. 2503–2513, Sep. 2013, doi: 10.1007/s11947-012-0933-3.
[19] S. Yang, X. Han, and Y. Chen, “Three-Dimensional Embryonic Image Segmentation and Registration Based on Shape Index and Ellipsoid-Fitting Method,” J. Comput. Biol., vol. 26, no. 2, pp. 128–142, Feb. 2019, doi: 10.1089/cmb.2018.0165.
[20] S. Saifullah, Sunardi, and A. Yudhana, “Analisis Perbandingan Pengolahan Citra Asli dan Hasil Croping Untuk Identifikasi Telur,” J. Tek. Inform. dan Sist. Inf., vol. 2, no. 3, pp. 341–350, 2016.
[21] S. Saifullah, “Analisis Perbandingan HE dan CLAHE pada Image Enhancement dalam Proses Segmentasi Citra untuk Deteksi Fertilitas Telur,” J. Nas. Pendidik. Tek. Inform. JANAPATI, vol. 9, no. 1, 2020.
[22] S. Saifullah, A. P. Suryotomo, and B. Yuwono, “Fish Detection Using Morphological Approach Based-on K-Means Segmentation,” Compiler, vol. 10, no. 1, Jan. 2021, doi: 10.28989/compiler.v10i1.946.
[23] S. Saifullah and Andiko Putro Suryotomo, “Chicken Egg Fertility Identification using FOS and BP-Neural Networks on Image Processing,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 5, pp. 919–926, Oct. 2021, doi: 10.29207/resti.v5i5.3431.
[24] S. Saifullah and A. P. Suryotomo, “Identification of chicken egg fertility using SVM classifier based on first-order statistical feature extraction,” Ilk. J. Ilm., vol. 13, no. 3, 2021.
[25] A. Muhammed and A. R. Pais, “A Novel Fingerprint Image Enhancement based on Super Resolution,” in 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), 2020, pp. 165–170.
[26] S. Saifullah, “Segmentasi Citra Menggunakan Metode Watershed Transform Berdasarkan Image Enhancement Dalam Mendeteksi Embrio Telur,” Syst. Inf. Syst. Informatics J., vol. 5, no. 2, pp. 53–60, Mar. 2020, doi: 10.29080/systemic.v5i2.798.
[27] Y. Xie, L. Ning, M. Wang, and C. Li, “Image Enhancement Based on Histogram Equalization,” J. Phys. Conf. Ser., vol. 1314, p. 012161, Oct. 2019, doi: 10.1088/1742-6596/1314/1/012161.
[28] K. G. Dhal, A. Das, S. Ray, J. Gálvez, and S. Das, “Histogram Equalization Variants as Optimization Problems: A Review,” Arch. Comput. Methods Eng., Apr. 2020, doi: 10.1007/s11831-020-09425-1.
[29] E. Park, S. Lohumi, and B.-K. Cho, “Line-scan imaging analysis for rapid viability evaluation of white-fertilized-egg embryos,” Sensors Actuators B Chem., vol. 281, pp. 204–211, Feb. 2019, doi: 10.1016/j.snb.2018.10.109.
[30] A. H. Pratomo, W. Kaswidjanti, A. S. Nugroho, and S. Saifullah, “Parking detection system using background subtraction and HSV color segmentation,” Bull. Electr. Eng. Informatics, vol. 10, no. 6, pp. 3211–3219, Dec. 2021, doi: 10.11591/eei.v10i6.3251.
[31] S. Saifullah, S. Sunardi, and A. Yudhana, “Perbandingan segmentasi pada citra asli dan citra kompresi wavelet untuk identifikasi telur,” Ilk. J. Ilm., vol. 8, no. 3, pp. 190–196, Dec. 2016, doi: 10.33096/ilkom.v8i3.75.190-196.
[32] Sunardi, A. Yudhana, and S. Saifullah, “Identity analysis of egg based on digital and thermal imaging: Image processing and counting object concept,” Int. J. Electr. Comput. Eng., vol. 7, no. 1, pp. 200–208, 2017, doi: 10.11591/ijece.v7i1.pp200-208.
[33] A. Yudhana, Sunardi, and S. Saifullah, “Segmentation comparing eggs watermarking image and original image,” Bull. Electr. Eng. Informatics, vol. 6, no. 1, pp. 47–53, 2017, doi: 10.11591/eei.v6i1.595.
[34] I. E. Y. Sari, M. Furqan, and S. Sriani, “Penerapan Metode Otsu dalam Melakukan Segmentasi Citra pada Citra Naskah Arab,” MATRIK J. Manajemen, Tek. Inform. dan Rekayasa Komput., vol. 20, no. 1, pp. 59–72, Sep. 2020, doi: 10.30812/matrik.v20i1.658.
[35] S. Saifullah, R. I. Mehriddinovich;, and L. K. Tolentino, “Chicken Egg Detection Based-on Image Processing Concept: A Review,” Comput. Inf. Process. Lett., vol. 1, no. 1, pp. 31–40, 2021, doi: 10.31315/cip.v1i1.6129.
[2] J. Dong et al., “Prediction of infertile chicken eggs before hatching by the Naïve-Bayes method combined with visible near infrared transmission spectroscopy,” Spectrosc. Lett., pp. 1–10, Apr. 2020, doi: 10.1080/00387010.2020.1748061.
[3] L. Huang, A. He, M. Zhai, Y. Wang, R. Bai, and X. Nie, “A Multi-Feature Fusion Based on Transfer Learning for Chicken Embryo Eggs Classification,” Symmetry (Basel)., vol. 11, no. 5, p. 606, May 2019, doi: 10.3390/sym11050606.
[4] L. Geng, Y. Hu, Z. Xiao, and J. Xi, “Fertility Detection of Hatching Eggs Based on a Convolutional Neural Network,” Appl. Sci., vol. 9, no. 7, p. 1408, Apr. 2019, doi: 10.3390/app9071408.
[5] E. H. Rachmawanto et al., “Eggs Classification based on Egg Shell Image using K-Nearest Neighbors Classifier,” in 2020 International Seminar on Application for Technology of Information and Communication (iSemantic), Sep. 2020, pp. 50–54, doi: 10.1109/iSemantic50169.2020.9234305.
[6] W. Lumchanow and S. Udomsiri, “Combination of GLCM and KNN Classification for Chicken Embryo Development Recognition,” in 2019 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT-NCON), Jan. 2019, pp. 322–325, doi: 10.1109/ECTI-NCON.2019.8692272.
[7] X.-S. Jin, J. Li, and X. Du, “Image Classification of Chicken Embryo Based on Matched Filter and Skeleton Curvature Feature,” J. Phys. Conf. Ser., vol. 1651, p. 12196, 2020, doi: 10.1088/1742-6596/1651/1/012196.
[8] S. Saifullah, “K-Means Clustering for Egg Embryo’s Detection Based-on Statistical Feature Extraction Approach of Candling Eggs Image,” SINERGI, vol. 25, no. 1, pp. 43–50, 2020, doi: 10.22441/sinergi.2021.1.006.
[9] S. Saifullah, “Segmentasi Citra Menggunakan Metode Watershed Transform Berdasarkan Image Enhancement Dalam Mendeteksi Embrio Telur,” Syst. Inf. Syst. Informatics J., vol. 5, no. 2, pp. 53–60, 2019, doi: 10.29080/systemic.v5i2.798.
[10] S. Saifullah, “Segmentation for embryonated Egg Images Detection using the K-Means Algorithm in Image Processing,” 2020 Fifth Int. Conf. Informatics Comput., pp. 1–7, Nov. 2020, doi: 10.1109/ICIC50835.2020.9288648.
[11] S. Saifullah and A. P. Suryotomo, “Thresholding and Hybrid CLAHE-HE for Chicken Egg Embryo Segmentation,” in 2021 International Conference on Communication & Information Technology (ICICT), Jun. 2021, pp. 268–273, doi: 10.1109/ICICT52195.2021.9568444.
[12] Sunardi, A. Yudhana, and S. Saifullah, “Identification of Egg Fertility Using Gray Level Co-Occurrence Matrix and Backpropagation,” Adv. Sci. Lett., vol. 24, no. 12, pp. 9151–9156, 2018, doi: 10.1166/asl.2018.12115.
[13] S. Saifullah and V. A. Permadi, “Comparison of Egg Fertility Identification based on GLCM Feature Extraction using Backpropagation and K-means Clustering Algorithms,” in Proceeding - 2019 5th International Conference on Science in Information Technology: Embracing Industry 4.0: Towards Innovation in Cyber Physical System, ICSITech 2019, Oct. 2019, pp. 140–145, doi: 10.1109/ICSITech46713.2019.8987496.
[14] S. Saifullah, “K-means Segmentation Based-on Lab Color Space for Embryo Egg Detection,” arXiv Prepr. arXiv2103.02288, Mar. 2021, [Online]. Available: http://arxiv.org/abs/2103.02288.
[15] S. Saifullah and A. P. Suryotomo, “Thresholding and hybrid CLAHE-HE for chicken egg embryo segmentation,” Int. Conf. Commun. Inf. Technol., 2021.
[16] D. Indra, T. Hasanuddin, R. Satra, and N. R. Wibowo, “Eggs Detection Using Otsu Thresholding Method,” in 2018 2nd East Indonesia Conference on Computer and Information Technology (EIConCIT), Nov. 2018, pp. 10–13, doi: 10.1109/EIConCIT.2018.8878517.
[17] J. G. C. Rancapan, E. R. Arboleda, J. L. Dioses, and R. M. Dellosa, “Egg fertility detection using image processing and fuzzy logic,” Int. J. Sci. Technol. Res., vol. 8, no. 10, pp. 3228–3230, Oct. 2019.
[18] L. Liu and M. O. Ngadi, “Detecting Fertility and Early Embryo Development of Chicken Eggs Using Near-Infrared Hyperspectral Imaging,” Food Bioprocess Technol., vol. 6, no. 9, pp. 2503–2513, Sep. 2013, doi: 10.1007/s11947-012-0933-3.
[19] S. Yang, X. Han, and Y. Chen, “Three-Dimensional Embryonic Image Segmentation and Registration Based on Shape Index and Ellipsoid-Fitting Method,” J. Comput. Biol., vol. 26, no. 2, pp. 128–142, Feb. 2019, doi: 10.1089/cmb.2018.0165.
[20] S. Saifullah, Sunardi, and A. Yudhana, “Analisis Perbandingan Pengolahan Citra Asli dan Hasil Croping Untuk Identifikasi Telur,” J. Tek. Inform. dan Sist. Inf., vol. 2, no. 3, pp. 341–350, 2016.
[21] S. Saifullah, “Analisis Perbandingan HE dan CLAHE pada Image Enhancement dalam Proses Segmentasi Citra untuk Deteksi Fertilitas Telur,” J. Nas. Pendidik. Tek. Inform. JANAPATI, vol. 9, no. 1, 2020.
[22] S. Saifullah, A. P. Suryotomo, and B. Yuwono, “Fish Detection Using Morphological Approach Based-on K-Means Segmentation,” Compiler, vol. 10, no. 1, Jan. 2021, doi: 10.28989/compiler.v10i1.946.
[23] S. Saifullah and Andiko Putro Suryotomo, “Chicken Egg Fertility Identification using FOS and BP-Neural Networks on Image Processing,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 5, pp. 919–926, Oct. 2021, doi: 10.29207/resti.v5i5.3431.
[24] S. Saifullah and A. P. Suryotomo, “Identification of chicken egg fertility using SVM classifier based on first-order statistical feature extraction,” Ilk. J. Ilm., vol. 13, no. 3, 2021.
[25] A. Muhammed and A. R. Pais, “A Novel Fingerprint Image Enhancement based on Super Resolution,” in 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), 2020, pp. 165–170.
[26] S. Saifullah, “Segmentasi Citra Menggunakan Metode Watershed Transform Berdasarkan Image Enhancement Dalam Mendeteksi Embrio Telur,” Syst. Inf. Syst. Informatics J., vol. 5, no. 2, pp. 53–60, Mar. 2020, doi: 10.29080/systemic.v5i2.798.
[27] Y. Xie, L. Ning, M. Wang, and C. Li, “Image Enhancement Based on Histogram Equalization,” J. Phys. Conf. Ser., vol. 1314, p. 012161, Oct. 2019, doi: 10.1088/1742-6596/1314/1/012161.
[28] K. G. Dhal, A. Das, S. Ray, J. Gálvez, and S. Das, “Histogram Equalization Variants as Optimization Problems: A Review,” Arch. Comput. Methods Eng., Apr. 2020, doi: 10.1007/s11831-020-09425-1.
[29] E. Park, S. Lohumi, and B.-K. Cho, “Line-scan imaging analysis for rapid viability evaluation of white-fertilized-egg embryos,” Sensors Actuators B Chem., vol. 281, pp. 204–211, Feb. 2019, doi: 10.1016/j.snb.2018.10.109.
[30] A. H. Pratomo, W. Kaswidjanti, A. S. Nugroho, and S. Saifullah, “Parking detection system using background subtraction and HSV color segmentation,” Bull. Electr. Eng. Informatics, vol. 10, no. 6, pp. 3211–3219, Dec. 2021, doi: 10.11591/eei.v10i6.3251.
[31] S. Saifullah, S. Sunardi, and A. Yudhana, “Perbandingan segmentasi pada citra asli dan citra kompresi wavelet untuk identifikasi telur,” Ilk. J. Ilm., vol. 8, no. 3, pp. 190–196, Dec. 2016, doi: 10.33096/ilkom.v8i3.75.190-196.
[32] Sunardi, A. Yudhana, and S. Saifullah, “Identity analysis of egg based on digital and thermal imaging: Image processing and counting object concept,” Int. J. Electr. Comput. Eng., vol. 7, no. 1, pp. 200–208, 2017, doi: 10.11591/ijece.v7i1.pp200-208.
[33] A. Yudhana, Sunardi, and S. Saifullah, “Segmentation comparing eggs watermarking image and original image,” Bull. Electr. Eng. Informatics, vol. 6, no. 1, pp. 47–53, 2017, doi: 10.11591/eei.v6i1.595.
[34] I. E. Y. Sari, M. Furqan, and S. Sriani, “Penerapan Metode Otsu dalam Melakukan Segmentasi Citra pada Citra Naskah Arab,” MATRIK J. Manajemen, Tek. Inform. dan Rekayasa Komput., vol. 20, no. 1, pp. 59–72, Sep. 2020, doi: 10.30812/matrik.v20i1.658.
[35] S. Saifullah, R. I. Mehriddinovich;, and L. K. Tolentino, “Chicken Egg Detection Based-on Image Processing Concept: A Review,” Comput. Inf. Process. Lett., vol. 1, no. 1, pp. 31–40, 2021, doi: 10.31315/cip.v1i1.6129.
Published
2022-03-31
How to Cite
Suhirman, S., Saifullah, S., Hidayat, A., & Sejati, R. (2022). Otsu Method for Chicken Egg Embryo Detection based-on Increase Image Quality. MATRIK : Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer, 21(2), 417-428. https://doi.org/https://doi.org/10.30812/matrik.v21i2.1724
Issue
Section
Articles
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
.png)
