Araştırma Makalesi

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

Cilt: 38 Sayı: 3 15 Eylül 2021
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Comparison of the visual texture calculation methods by image analysis, applied to mirror and scaled carp skin

Abstract

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.

Keywords

Teşekkür

The author would like to thank Prof. Dr. Murat O. Balaban for his invaluable technical support and Mr. Serkan ERKAN, Director of Republic of Turkey Ministry of Agriculture and Forestry, Mediterranean Fisheries Research Production and Training Institute, Antalya, Turkey.

Kaynakça

  1. 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
  2. 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
  3. 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
  4. 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.
  5. 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
  6. 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
  7. 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
  8. 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).

Ayrıntılar

Birincil Dil

İngilizce

Konular

Gıda Mühendisliği

Bölüm

Araştırma Makalesi

Yayımlanma Tarihi

15 Eylül 2021

Gönderilme Tarihi

11 Mayıs 2021

Kabul Tarihi

11 Temmuz 2021

Yayımlandığı Sayı

Yıl 1970 Cilt: 38 Sayı: 3

Kaynak Göster

APA
Gümüş, B. (2021). Comparison of the visual texture calculation methods by image analysis, applied to mirror and scaled carp skin. Ege Journal of Fisheries and Aquatic Sciences, 38(3), 383-391. https://doi.org/10.12714/egejfas.38.3.15