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      KCI등재 SCOPUS

      Compression and Enhancement of Medical Images using Opposition Based Harmony Search Algorithm

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      https://www.riss.kr/link?id=A106175023

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      다국어 초록 (Multilingual Abstract)

      The growth of telemedicine-based wireless communication for images—magnetic resonance imaging (MRI)and computed tomography (CT)—leads to the necessity of learning the concept of image compression. Overthe years, the transform based and spatial based compression techniques have attracted many types ofresearches and achieve better results at the cost of high computational complexity. In order to overcome this,the optimization techniques are considered with the existing image compression techniques. However, it failsto preserve the original content of the diagnostic information and cause artifacts at high compression ratio.
      In this paper, the concept of histogram based multilevel thresholding (HMT) using entropy is appended withthe optimization algorithm to compress the medical images effectively. However, the method becomes timeconsuming during the measurement of the randomness from the image pixel group and not suitable formedical applications. Hence, an attempt has been made in this paper to develop an HMT based imagecompression by utilizing the opposition based improved harmony search algorithm (OIHSA) as anoptimization technique along with the entropy. Further, the enhancement of the significant informationpresent in the medical images are improved by the proper selection of entropy and the number of thresholdschosen to reconstruct the compressed image.
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      The growth of telemedicine-based wireless communication for images—magnetic resonance imaging (MRI)and computed tomography (CT)—leads to the necessity of learning the concept of image compression. Overthe years, the transform based and spatial bas...

      The growth of telemedicine-based wireless communication for images—magnetic resonance imaging (MRI)and computed tomography (CT)—leads to the necessity of learning the concept of image compression. Overthe years, the transform based and spatial based compression techniques have attracted many types ofresearches and achieve better results at the cost of high computational complexity. In order to overcome this,the optimization techniques are considered with the existing image compression techniques. However, it failsto preserve the original content of the diagnostic information and cause artifacts at high compression ratio.
      In this paper, the concept of histogram based multilevel thresholding (HMT) using entropy is appended withthe optimization algorithm to compress the medical images effectively. However, the method becomes timeconsuming during the measurement of the randomness from the image pixel group and not suitable formedical applications. Hence, an attempt has been made in this paper to develop an HMT based imagecompression by utilizing the opposition based improved harmony search algorithm (OIHSA) as anoptimization technique along with the entropy. Further, the enhancement of the significant informationpresent in the medical images are improved by the proper selection of entropy and the number of thresholdschosen to reconstruct the compressed image.

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      참고문헌 (Reference)

      1 S. Bansod, "harmony search algorithm for color image compression" 2 (2): 1669-1672, 2014

      2 T. Bruylants, "Wavelet based volumetric medical image compression" 31 : 112-133, 2015

      3 S. Bansod, "Recent image compression algorithms : a survey" 2 (2): 4815-4820, 2013

      4 T. Kanumuri, "Progressive medical image coding using set of hierarchical trees" 1973-1978, 2015

      5 X. S. Yang, "Music-Inspired Harmony Search Algorithm" Springer 1-14, 2009

      6 D. Oliva, "Multilevel thresholding segmentation based on harmony search optimization" 2013 : 2013

      7 M. A. Alhanjouri, "Multi-resolution analysis for medical image compression" 3 (3): 215-228, 2011

      8 S. T. Lim, "Medical image compression using block-based PCA algorithm" 171-175, 2014

      9 D. Ravichandran, "Medical image compression based on daubechies wavelet global thresholding and huffman encoding algorithm" 5 (5): 7-12, 2016

      10 R. R. M. Daga, "Image compression using harmony search algorithm" 9 (9): 16-23, 2012

      1 S. Bansod, "harmony search algorithm for color image compression" 2 (2): 1669-1672, 2014

      2 T. Bruylants, "Wavelet based volumetric medical image compression" 31 : 112-133, 2015

      3 S. Bansod, "Recent image compression algorithms : a survey" 2 (2): 4815-4820, 2013

      4 T. Kanumuri, "Progressive medical image coding using set of hierarchical trees" 1973-1978, 2015

      5 X. S. Yang, "Music-Inspired Harmony Search Algorithm" Springer 1-14, 2009

      6 D. Oliva, "Multilevel thresholding segmentation based on harmony search optimization" 2013 : 2013

      7 M. A. Alhanjouri, "Multi-resolution analysis for medical image compression" 3 (3): 215-228, 2011

      8 S. T. Lim, "Medical image compression using block-based PCA algorithm" 171-175, 2014

      9 D. Ravichandran, "Medical image compression based on daubechies wavelet global thresholding and huffman encoding algorithm" 5 (5): 7-12, 2016

      10 R. R. M. Daga, "Image compression using harmony search algorithm" 9 (9): 16-23, 2012

      11 S. Singh, "Image compression on biomedical images using predictive coding with the help of ROI" 120-125, 2015

      12 M. Rehman, "Image compression : a survey" 7 (7): 656-672, 2014

      13 W. S. Jang, "Hybrid simplex-harmony search method for optimization problems" 4157-4164, 2008

      14 M. G. Omran, "Global-best harmony search" 198 (198): 643-656, 2008

      15 K. Uma, "Comparison of image compression using GA, ACO and PSO techniques" 815-820, 2011

      16 S. Bhavani, "Comparison of fractal coding methods for medical image compression" 7 (7): 686-693, 2013

      17 Y. Jiang, "Automatic multilevel thresholding for image segmentation using stratified sampling and Tabu Search" 19 (19): 2605-2617, 2015

      18 Quoc Bao Truong, "Automatic Multi-thresholds Selection for Image Segmentation based on Evolutionary Approach" 제어·로봇·시스템학회 11 (11): 834-844, 2013

      19 M. H. Horng, "Artificial Intelligence and Computational Intelligence" Springer 185-194, 2009

      20 J. Greblicki, "Analysis of the properties of the harmony search algorithm carried out on the one dimensional binary knapsack problem" Springer 697-704, 2009

      21 A. Banerjee, "An opposition-based harmony search algorithm for engineering optimization problems" 5 (5): 85-101, 2014

      22 J. Kalivarapu, "An improved harmony search algorithm with dynamically varying bandwidth" 48 (48): 1091-1108, 2016

      23 M. Mahdavi, "An improved harmony search algorithm for solving optimization problems" 188 (188): 1567-1579, 2007

      24 M. Mahdavi, "An improved harmony search algorithm for solving optimization problems" 188 (188): 1567-1579, 2007

      25 O. Hasancebi, "Adaptive harmony search method for structural optimization" 136 (136): 419-431, 2009

      26 B. Alagendran, "A survey on various medical image compression techniques" 2 (2): 425-428, 2012

      27 D. Manjarres, "A survey on applications of the harmony search algorithm" 26 (26): 1818-1831, 2013

      28 P. K. Sahoo, "A survey of thresholding techniques" 41 (41): 233-260, 1988

      29 G. Vijayvargiya, "A novel medical image compression technique based on structure reference selection using integer wavelet transform function and PSO algorithm" 91 (91): 9-13, 2014

      30 S. Paul, "A novel approach for image compression based on multi-level image thresholding using Shannon Entropy and Differential Evolution" 56-61, 2014

      31 T. Pun, "A new method for grey-level picture thresholding using the entropy of the histogram" 2 (2): 223-237, 1980

      32 J. N. Kapur, "A new method for gray-level picture thresholding using the entropy of the histogram" 29 (29): 273-285, 1985

      33 K. Hammouche, "A comparative study of various meta-heuristic techniques applied to the multilevel thresholding problem" 23 (23): 676-688, 2010

      34 K. Hammouche, "A comparative study of various meta-heuristic techniques applied to the multilevel thresholding problem" 23 (23): 676-688, 2010

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      학술지 이력

      학술지 이력
      연월일 이력구분 이력상세 등재구분
      2023 평가예정 해외DB학술지평가 신청대상 (해외등재 학술지 평가)
      2020-01-01 평가 등재학술지 유지 (해외등재 학술지 평가) KCI등재
      2012-01-01 평가 등재학술지 선정 (등재후보2차) KCI등재
      2011-01-01 평가 등재후보 1차 PASS (등재후보1차) KCI등재후보
      2009-01-01 평가 등재후보학술지 선정 (신규평가) KCI등재후보
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      학술지 인용정보

      학술지 인용정보
      기준연도 WOS-KCI 통합IF(2년) KCIF(2년) KCIF(3년)
      2016 0.09 0.09 0.09
      KCIF(4년) KCIF(5년) 중심성지수(3년) 즉시성지수
      0.07 0.06 0.254 0.59
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