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Methods/evaluation Pre Re Fm JI DSC (%) Otsu's method 0.91 0.89 0.90 0.89 94 Isodata 0.86 0.84 0.85 0.84 91 Maximum entropy 0.84 0.94 0.88 0.94 91 Cross entropy 0.94 0.82 0.87 0.82 90 Minimum error 0.85 0.82 0.83 0.82 89 Fuzzy entropy 0.68 0.67 0.68 0.67 80 Adaptive thresholding 0.96 0.50 0.66 0.50 67 k-means 0.90 0.89 0.89 0.89 94 Fuzzy c-means 0.94 0.77 0.85 0.77 77 Mean shift 0.93 0.91 0.92 0.91 95 Chan-Vese method 0.89 0.87 0.88 0.87 94 Graph-based min cut 0.87 0.95 0.91 0.87 93 Figure 4: Image quality assessment metrics: (a) comparison of filtering methods in terms of peak signal-to-noise ratio (PSNR) and (b) comparison of different contrast enhancement methods in terms of the contrast improvement index (CII).
The Principle of Two-Dimensional Maximum Between-Class Variance (Otsu) and Its Improvement
For comparison purposed the results obtained using classical Otsu method are used.
At the center of the proposed model, an image enhancement algorithm called Min-Max Gray Level Discrimination (M2GLD) is put forward as a preprocessing step to improve the Otsu binarization approach, followed by shape analyses for meliorating the crack detection performance.
However, there are few studies concerning the Otsu algorithm or that investigate the MapReduce distributed parallel processing of edge detection operators and their application to the field of digital image processing.
The comparative analysis shows that the thresholding method proposed by Otsu  produces the most accurate segmentation among the ten comparative methods; therefore, the method of Otsu  is used to binarize the MGMF response in further analysis.
(2016) implemented a SC approach to segment the soft brain tissues using a two-step procedure based on Otsu's thresholding .
In this paper, in order to improve the effect of segmentation and reduce computational cost of two-dimensional Otsu algorithm, a novel fast image segmentation method using two-dimensional Otsu based on estimation of distributed algorithm is proposed.
The variance in Otsu method will be used as a reference in this paper.
This process begins with thresholding the blue canal of the RGB (Red, Green, and Blue) image using the Otsu thresholding method to obtain a binary image.
In , various evolutionary approaches such as Differential Evolution (DE), Tabu Search (TS), and Simulated Annealing (SA) are discussed to solve the limitations of Otsu's and Kapur's approaches for MT.
The third stage is responsible of the texture-segmentation using either Otsu (1979) or Gabor filters (Sandler & Lindenbaum, 2006).
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