An objective method to identify optimum clip-limit and histogram specification of contrast limited adaptive histogram equalization for MR images

• Autorzy: Joseph J. , Sivaraman J. , Periyasamy R. , Simi V. R.

Identyfikatory

DOI

10.1016/j.bbe.2016.11.006

• Języki publikacji: EN

Abstrakty

EN

In contrast limited adaptive histogram equalization (CLAHE), the selection of tile size, clip-limit and the distribution which specify desired shape of the histogram of image tiles is paramount, as it critically influences the quality of the enhanced image. The optimal value of these parameters devolves on the generic of the image to be enhanced and usually they are selected empirically. In this paper, the degradation of intensity, textural and geometric features of the medical image with respect to the variation in clip-limit and specified histogram shape is analyzed. The statistical indices used to quantify the feature degradation are Absolute Mean Brightness Error (AMBE), Absolute Deviation in Entropy (ADE), Peak Signal to Noise Ratio (PSNR), Variance Ratio (VR), Structural Similarity Index Matrix (SSIM) and Saturation Evaluation Index (SEI). The images used for the analysis are axial plane MR images of magnetic resonance spectroscopy (MRS), under gradient recalled echo (GRE), diffusion weighted imaging (DWI) 1000b Array Spatial Sensitivity Encoding Technique (ASSET), T2 Fluid Attenuation Inversion Recovery (FLAIR) and T1 Fast Spin-Echo Contrast Enhanced (FS-ECE) series of pre-operative Glioblastoma-edema complex. The experimental analysis was performed using Matlab^®. Results show that for MR images the exponential histogram specification with a clip-limit of 0.01 is found to be optimum. At optimum clip-limit, the mean of SSIM exhibited by the Rayleigh, uniform and exponential histogram specification were found to be 0.7477, 0.7946 and 0.8457, for ten sets of MR images and mean of variance ratio are 1.242, 2.0316 and 1.7711, respectively.

Słowa kluczowe

EN

absolute deviation in entropy; absolute mean brightness error; clip limit; tile size; peak signal to noise ratio; saturation evaluation index

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2017
• Tom: Vol. 37, no. 3
• Strony: 489--497
• Opis fizyczny: Bibliogr. 20 poz., rys., tab., wykr.

Twórcy

autor

Joseph J.

• Department of Biomedical Engineering, National Institute of Technology, Raipur, India

autor

Sivaraman J.

• Department of Biomedical Engineering, Vel Tech Multi Tech Engineering College, Avadi, Chennai 600062, India

autor

Periyasamy R.

• Department of Biomedical Engineering, National Institute of Technology, Raipur, India

autor

Simi V. R.

• Department of Computer Science & Engg., KVM College of Engineering & Information Technology, Kerala, India

Bibliografia

• [1] Pizer SM, Amburn EP, Austin JD, Cromartie R, Geselowitz A, Greer T, et al. Adaptive histogram equalization and its variations. Comput Vis Graph Image Process 1987;39(3): 355–68.
• [2] Reza AM. Realization of the contrast limited adaptive histogram equalization (CLAHE) for real-time image enhancement. J VLSI Signal Process Syst 2004;38(1):35–44.
• [3] Stark JA. Adaptive image contrast enhancement using generalizations of histogram equalization. IEEE Trans Med Imaging 2000;9(5):889–96.
• [4] Hummel R. Image enhancement by histogram transformation. Comput Vis Graph Image Process 1977;6:184–95.
• [5] Pizer SM, Johnston RE, Ericksen JP, Yankaskas BC, Muller KE. Contrast-limited adaptive histogram equalization: speed and effectiveness. Proc of 1st Conf on Visualization in Biomed Comput; 1990. pp. 337–45.
• [6] Maxwell EG, Tripti C. A comparison between contrast limited adaptive histogram equalization and gabor filter sclera blood vessel enhancement techniques. Int J Soft Comput Eng 2013;3(4):22–5.
• [7] Sasi NM, Jayasree VK. Contrast limited adaptive histogram equalization for qualitative enhancement of myocardial perfusion images. Engineering 2013;5:326–31.
• [8] Shome SK, Krishna Vadali SR. Enhancement of diabetic retinopathy imagery using contrast limited adaptive histogram equalization. Int J Comput Sci Inf Technol 2011;2 (6):2694–9.
• [9] Meshram SP, Pawar MS. Extraction of retinal blood vessels from diabetic retinopathy imagery using contrast limited adaptive histogram equalization. Int J Adv Comput Theory Eng 2013;2(3):143–7.
• [10] Ahmad SAB, Taib MN, Khalid NEA, Ahmad ABT. The effect of sharp contrast-limited adaptive histogram equalization (SCLAHE) on intra-oral dental radiograph images. Proc of IEEE EMBS Conf on Biomed Eng Sci; 2010. pp. 400–5.
• [11] Pisano ED, Zong S, Hemminger BM, DeLuca M, Jhonston RE, Muller K, et al. Contrast limited adaptive histogram equalization image processing to improve the detection of simulated speculation in dense monograms. J Digit Imaging 1998;11(4):193–200.
• [12] Yousefi S, Qin J, Zhi Z, Wang RK. Uniform enhancement of optical micro-angiography images using Rayleigh contrast-limited adaptive histogram equalization. Quant Imaging Med Surg 2013;3(1):5–17.
• [13] Min BS, Lim DK, Kim SJ, Lee JH. A novel method of determining parameters of CLAHE based on image entropy. Int J Soft Eng Appl 2013;7(5):113–20.
• [14] Karel Z. Contrast limited adaptive histogram equalization. Graphic gems IV. San Diego: Academic Press Professional; 1994. p. 474–85.
• [15] Ravishankar M, Mohan S. Optimized histogram based contrast limited enhancement for mammogram images. ACEEE Int J Inf Technol 2013;3(1):5–8.
• [16] Mohan S, Mahesh TR. Particle swarm optimization based contrast limited enhancement for mammogram images. Proc of 7th Int Conf on Intel Syst and Con; 2013. pp. 384–8.
• [17] Wang Z, Bovik AC, Sheikh HR, Simoncelli EP. Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 2014;13:600–12.
• [18] Huynh-Thu Q, Ghanbari M. Scope of validity of PSNR in image/video quality assessment. Electron Lett 2008;44:800–1.
• [19] Saleem A, Beghdadi A, Boashash B. Image fusion-based contrast enhancement. Eurasip J Image Video Process 2012;10:1–17.
• [20] Yun SH, Kim JH, Kim S. Image enhancement using a fusion framework of histogram equalization and Laplacian pyramid. IEEE Trans Consum Electr 2010;56:2763–71.

Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).