IMAGE THRESHOLDING BASED ON HIERARCHICAL CLUSTERING ANALYSIS AND PERCENTILE METHOD FOR TUNA IMAGE SEGMENTATION

Alifia Puspaningrum, Nahya Nur, Ozzy Secio Riza, Agus Zainal Arifin

Abstract


Automatic classification of tuna image needs a good segmentation as a main process. Tuna image is taken with textural background and the tuna’s shadow behind the object. This paper proposed a new weighted thresholding method for tuna image segmentation which adapts hierarchical clustering analysisand percentile method. The proposed method considering all part of the image and the several part of the image. It will be used to estimate the object which the proportion has been known. To detect the edge of tuna images, 2D Gabor filter has been implemented to the image. The result image then threshold which the value has been calculated by using HCA and percentile method. The mathematical morphologies are applied into threshold image. In the experimental result, the proposed method can improve the accuracy value up to 20.04%, sensitivity value up to 29.94%, and specificity value up to 17,23% compared to HCA. The result shows that the proposed method cansegment tuna images well and more accurate than hierarchical cluster analysis method.


Full Text:

PDF

References


MacielZortea., Eliezer Flores.and Jacob Scharcanski. (2017) ‘A simple weighted thresholding method for the segmentation of pigmented skin lesions in macroscopic images’, Pattern Recognition, 64, 92–104.

Agus Zainal Arifin., and A. Asano. (2006) ‘Image Segmentation by Histogram Thresholding Using Hierarchical Cluster Analysis’,Pattern Recognition Letters, 27, 13, 1515-1521.

“Ekspor Ikan Tongkol/Tuna Menurut Negara Tujuan Utama, 2002-2015” https://www.bps.go.id/linkTabelStatis/view/id/1019 ac-cessed on 2 April 2017.

P. D. Sathya., and R. Kayalvizhi.(2011) ‘Modifed bacterial foraging algorithm based multilevel thresholding for image segmentation’,Engineering Applications of Artificial Intelligence, vol. 24, no. 4, pp. 595–615.

Sanjay Agrawal., Rutuparna Panda., SudiptaBhuyan., and B.K. Panigrahi.(2013) ‘Tsallis entropy based optimal multilevel thresholding using cuckoo search algorithm’,Swarm and Evolutionary Computation, 11, 16–30.

Otsu, N. (1979) ‘A threshold selection method from gray-level histograms’,IEEE Transactions of Systems, Man, and Cybernetics 9, 62–66.

Gandomi, A. H., Yang, X. S., Talatahari, S., & Deb, S. (2012) ‘Coupled eagle strategy and differential evolu-tion for uconstrained and constrained global optimiza-tion’, Computers & Mathematics with Applications, 63(1), 191 – 200.

Qi Wang., Xiangde Zhang., Mingqi Li., Xiaopeng Dong., Qunhua Zhou., and Yu Yin. (2012) ‘Adaboost and multi-orientation 2D Gabor-based noisy iris recognition’, Pattern Recognition Letters 33, 978–983.

Wang Xuan., Lei Li., and Wang Mingzhe. (2012) ‘Palmprint verification based on 2D – Gabor wavelet and pulse-coupled neural network’, Knowledge-Based Systems, 27, 451–455.

Priyadarsan Parida., Nilamani Bhoi. (2016) ‘2-D Gabor filter based transition region extraction and morphological operation for image segmentation’, Computers and Electrical Engineering 000, 1–16.




DOI: http://dx.doi.org/10.36564/njca.v2i1.24

Refbacks

  • There are currently no refbacks.


Copyright (c) 2018 Alifia Puspaningrum, Nahya Nur, Ozzy Secio Riza, Agus Zainal Arifin


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

NJCA(Nusantara Journal of Computers and Its Applications)
Published by Computer Society of Nahdlatul Ulama, Indonesia.