Classification of Pomegranate Fruit using Texture Analysis of MR Images

Authors

  • Alireza Khoshroo University of tehran
  • Alireza Keyhani University of Tehran
  • Reza A. Zoroofi University of Tehran
  • Shahin Rafiee University of Tehran
  • Zabihollah Zamani University of Tehran
  • Mohamad R. Alsharif University of the Ryukyus

Abstract

Images obtained by Magnetic Resonance Imaging (MRI) of Iranian important export cultivar of pomegranate Malase-e-Torsh were analyzed by texture analysis to determine Gray Level Co-occurrence Matrix (GLCM) and Pixel Run-Length Matrix (PRLM) parameters. The T2 slices measured at 1.5 T for 4 quality classes of pomegranate semi-ripe, ripe, over-ripe and internal defects classes were analyzed numerically using the software MaZda. To classify pomegranate into different classes, discriminant analysis was conducted using cross-validation method and texture features. Ten GLCM and 5 PRLM features were used in 2 different classifiers. Mean classification accuracy was 95.75 % and 91.28 % for GLCM and PRLM features respectively. By using GLCM and RPLM features, classification accuracy for semi-ripe, over-ripe and internal defects classes was higher when GLCM features were used. Ripe class had higher classification accuracy while PRLM features were used. To improve classification accuracy, combination of GLCM and PRLM features were used. For achieving best classification accuracy, optimum numbers of features were selected based on their contribution to the model. Combination of 7 GLCM and 4 PRLM features resulted in mean accuracy of 98.33 % and the lowest type I and II errors. Especially, type I error in ripe and over-ripe classes were significantly decreased. The classification accuracies were 100, 98.47, 100 and 95 % for semi-ripe, ripe, over-ripe and internal defects classes.

Author Biographies

Alireza Khoshroo, University of tehran

PhD Candidate, Faculty of Biosystems Engineering, University of Tehran, Karaj, Iran

Alireza Keyhani, University of Tehran

Associate Professor, Faculty of Biosystems Engineering, University of Tehran, Karaj, Iran

Reza A. Zoroofi, University of Tehran

Associate Professor, School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran

Shahin Rafiee, University of Tehran

Associate Professor, Faculty of Biosystems Engineering, University of Tehran, Karaj, Iran

Zabihollah Zamani, University of Tehran

Associate Professor, Department of Horticultural Sciences, University of Tehran, Karaj, Iran

Mohamad R. Alsharif, University of the Ryukyus

Professor, Department of Information Engineering, University of the Ryukyus, Okinawa, Japan

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Published

2009-03-24

Issue

Section

VI-Postharvest Technology and Process Engineering