Comparison of the visual texture calculation methods by image analysis, applied to mirror and scaled carp skin

Regions of interest (ROI) representative of the visual texture of images of mirror carp Cyprinus carpio carpio and scaled carp Cyprinus carpio were taken. Red, green, blue and grayscale (R, G, B, GS) histograms of these ROI were calculated. The following methods of visual texture calculations were performed on the ROIs: 1) image energy based on histograms, 2) image entropy based on histograms, 3) image energy based on co-occurrence matrices, 4) image entropy based on co-occurrence matrices, 5) texture based on fractal dimensions, 6) texture based on texture primitives method. Calculations were performed for color and grayscale images. The identification of the smoothest and roughest ROIs depended on the method used. The largest range between the minimum and maximum values was found in the co-occurrence matrix-based entropy calculation. A close second was the texture change index (TCI) method.

Aynalı ve pullu sazan derisine uygulanan görüntü analizi ile görsel tekstür hesaplama yöntemlerinin karşılaştırılması

Aynalı sazan (Cyprinus carpio carpio) ve pullu sazan (Cyprinus carpio) görüntülerinin görsel tekstürü temsil eden ilgili bölgeleri (ROI) değerlendirilmiştir. Bu ROI'lerin kırmızı, yeşil, mavi ve gri (R, G, B, GS) histogramları hesaplanmıştır. ROI'lere aşağıdaki görsel doku hesaplama yöntemleri uygulanmıştır: 1) Histogramlara dayalı görüntü enerjisi, 2) Histogramlara dayalı görüntü entropisi, 3) Eşdizimlilik matrislerine dayalı görüntü enerjisi, 4) Eşdizimlilik matrislerine dayalı görüntü entropisi, 5) Fraktal boyutlara dayalı tekstür, 6) Tekstür pirimitif yöntemine dayalı tekstür’dür. Hesaplamalar renkli ve gri tonlamalı resimler için yapılmıştır. En düz ve en kaba ROI’lerin tanımlanmasının kullanılan yönteme bağlı olduğu bulunmuştur. Minimum ve maksimum değerler arasındaki en büyük aralık, entropi hesaplamasına bağlı eşdizimlilik matrisinde tespit edilirken ikinci büyük aralık tekstür değişim indeksi (TCI) yöntemiyle bulunmuştur.

PDF

___

Alçiçek, Z. & Balaban, M.O. (2012). Development and application of “The Two Image” method for accurate object recognition and color analysis. Journal of Food Engineering, 111(1), 46-51. DOI:10.1016/j.jfoodeng.2012.01.031

Balaban, M. O. (2008). Quantifying non-homogeneous colors in agricultural materials. Part I: Method development. Journal of Food Science, 73(9), 431-437. DOI: 10.1111/j.1750-3841.2008.00807.x

Balaban, M. O., Stewart, K., Fletcher, G. C. & Alçiçek, Z. (2014). Color change of the snapper (Pagrus auratus) and gurnard (Chelidonichthys kumu) skin and eyes during storage: effect of light polarization and contact with ice. Journal of Food Sciences, 79(12), E2456-2479. DOI: 10.1111/1750-3841.12693

Basset, O., Buquet, B., Abuelkaram, S., Delachartre, P. & Culioli, J. (2000). Application of texture image analysis for the classification of bovine meat. Food Chemistry, 69, 437-445.

Bharati, M.H., Liu, J.J. & MacGregor, J.F. (2004). Image texture analysis: methods and comparisons. Chemometrics and Intelligent Laboratory Systems, 72 (1), 57-71. DOI: 10.1016/j.chemolab.2004.02.005

Gümüş, E., Yılayaz, A., Kanyılmaz, M., Gümüş, B. & Balaban., M. (2021). Evaluation of body weight and color of cultured European catfish (Silurus glanis) and African catfish (Clarias gariepinus) using image analysis. Aquacultural Engineering, 93, 102147. DOI:10.1016/j.aquaeng.2021.102147

Hendrawan, Y., Fauzi, M.R., Khoirunnisa, N. S., Andreane, M., Hartianti, P. O., Halim, T. D. & Umam, C. (2019). Development of colour co-occurrence matrix (CCM) texture analysis for biosensing, IOP Conference Series: Earth and Environmental Science, 230, 012022. DOI:10.1088/1755-1315/230/1/012022

Larkin, K.G. (2016). Reflections on Shannon information: In search of a natural information-entropy for images. [Online]. Available: https://arxiv.org/abs/1609.01117 (01.05.2021).

Luzuriaga, D.A., Balaban, M.O. & Yeralan, S. (1997). Analysis of visual quality attributes of white shrimp by machine vision. Journal of Food Sciences, 62(1), 113-119. DOI: 10.1111/j.1365-2621.1997.tb04379.x

Oliveira, A. C. M., Crapo, C. & Balaban, M. O. (2006). Grading of pink salmon skin watermarking using a machine vision system. Second Joint Transatlantic Fisheries Technology Conference. October 29–November 1, 2006, Quebec City, Quebec, Canada. P-46, p. 138.

Padmavathi, K. & Thangadurai, K. (2016). Implementation of RGB and grayscale images in plant leaves disease detection- comparative study. Indian Journal of Science and Technology. 9(6). DOI: 10.17485/ijst/2016/v9i6/77739, February 2016

Partio, M., Cramariuc, B., Gabbouj, M. & Visa, A. (2002). Rock Texture Retrieval Using Gray Level Co-Occurrence Matrix. 5th Nordic Signal Processing Symposium, Oct. 2002.

Pathak, B. & Barooah, D. (2013). Texture analysis based on the gray level co-occurrence matrix considering possible orientations. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Energy, 2(9), 4206-4212.

Perez-Nieto, A., Chanona-Perez, J. J., Farrera-Rebollo, R. R., Gutierrez-Lopez, G. F., Alamilla-Beltran, L. & Calderon-Dominguez, G. (2010). Image analysis of structural changes in dough during baking. LWT- Food Science and Technology, 43, 535-543.

Sharma, M., Markou, M. & Singh, S. (2001). Evaluation of texture methods for image analysis. in Intelligent Information Systems Conference, The Seventh Australian and New Zealand IEEE, 117–121.

Tamura, H., Mori, S. & Yamawaki, T. (1978). Textural features corresponding to visual perception. IEEE Transactions on Systems, Man and Cybernetics, SMC-8 (6), 460-473.

Tuceryan, M. (1998). Texture analysis. In” The handbook of Pattern Recognition and Computer Vision, 2nd edition”, Chen C H, Pau L F, Wang P S P, editors. Chapter 2.1 p: 207-248. World Scientific Publishing Co.

Varma, M. & Garg, R. 2007. Locally invariant fractal features for statistical texture classification. IEEE 11th International Conference on Computer Vision. 1-8. IEEEXPLORE.IEEE.ORG.

Zheng, C., Sun, D.W. & Zheng L. (2006). Recent developments and applications of image features for food quality evaluation and inspection – a review. Trends in Food Science Technology, 17, 642-655. DOI: 10.1016/j.tifs.2006.06.005

Zheng, C., Sun, D.W. & Zheng, L. (2007). A new region-primitive method for classification of colour meat image texture based on size, orientation, and contrast. Meat Science, 76, 620-627. DOI: 10.1016/j.meatsci.2007.02.003

Zucker, S.W. & Terzopoulos, D. (1980). Finding structure in co-occurrence matrices for texture analysis. Computer Graphics and Image Processing, 12, 286-308. DOI: 10.1016/0146-664X(80)90016-7