244257, 2015. Results show that the proposed method is more robust to salt-and-pepper noise and acquires better segmentation results on uneven illumination images in general without compromising its performance on regular illumination images. OpenCV: Image Thresholding It is a representation of the distribution of the data. In addition to salt-and-pepper noise, traditional 2D Otsus method usually produces poor segmentation on uneven illumination images. The definition of ME is described as follows:where represent pixel sets of foreground and background and are manually labelled foreground and background, respectively. As introduced in Section 2, the 2D histogram can be partitioned into four regions. S. S. Pradhan, D. Patra, and P. K. Nanda, Adaptive thresholding based image segmentation with uneven lighting condition, in Proceedings of the 2008 IEEE Region 10 and the Third international Conference on Industrial and Information Systems, pp. Aiming at the problems of the traditional maximum inter-class variance method (Otsu) in multi-threshold image segmentation, such as large amount of computation, long computation time and low segmentation accuracy. J. Xing, P. Yang, and L. Qingge, Automatic thresholding using a modified valley emphasis, IET Image Processing, vol. The threshold value is calculated for each image frame separately resulting in a output vector of length equal to the total number of frames in the image. The improved method can deal with salt-and-pepper noise properly compared to original 2D and 1D Otsus methods and can make a significant improvement on segmenting uneven illumination images both qualitatively and quantitatively. Threshold segmentation is a commonly used method to deal with image segmentation problems. For scheme one, the improved 2D Otsus method MAOTSU_2D is directly implemented on Ip1 and Ip2 separately, and we can obtain two threshold vectors for the corresponding two parts. Segmentation results of each algorithm on salt-and-pepper corrupted images. (1) Set a convergence factor a that changes nonlinearly with the number of evolutionary iterations, compared with the original convergence factor alinearly decreasing from 2 to 0, this improvement enables the algorithm to have a strong global search ability in the early stage of the search, and to ensure the algorithm has a faster convergence speed in the later stage, while avoiding the algorithm from falling into the local optimal. 12, pp. And how to create the histogram for an input image? 2D Otsu's method is more robust compared to 1D Otsu's method. PDF Reviewing Otsu's Method For Image Thresholding IEEE Trans Image Process 27(5):21602175, Article Figure 10 shows the segmentation results of each algorithm on images with different noise intensity. PDF Modified Otsu-based Image Segmentation Algorithm (OBISA) 24192431, 2012. J Sys Sci Math Scis 39(1):120132, MATH The workflow of our proposed method is shown in Figure 4. The smaller average DSC value may also indicate its lower foreground detection ability to some extent. Figure 5 shows the optimal value of the objective function of comparison algorithms in the case of three-thresholds during the iterations. 256 numbers, P(i)], so it's It is equivalent to WOA on Lena, and IDE and GWO on Baboon, but obvious advantages in other image segmentation. (2) The Lvy flight strategy is introduced in the evolution process. Elaziz MA, Lu SF, He SB (2021) A multi-leader whale optimization algorithm for global optimization and image segmentation[J]. Collectively these experimental results verify the effectiveness of the proposed schemes on the synthetic and real world images. Otsu's method is a global image thresholding algorithm. Based on the above discussion, we can come to the conclusion that the more compact the 2D histogram is along the diagonal line, the less the influence of noise will be. In [30], Ashour et al. Otsu's method chooses a threshold that minimizes the intraclass variance of the thresholded black and white pixels. X. Cao, T. Li, H. Li et al., A robust parameter-free thresholding method for image segmentation, IEEE Access, vol. Update parameters A, a, C, l according (16)(17) and (23)(25). Segmentation results of each algorithm on other uneven illumination images. When only a single threshold value is used to segment image, it is known as global Thresholding is a commonly used method that improves the image segmentation effect. Hamdaouil F, Sakly A et al (n.d.) An efficient multi-level thresholding method for image segmentation based on the hybridization of modified PSO and Otsus method[J]. Parker [27] proposed a pixel level thresholding using local intensity gradient (LIG) and a region growing strategy for badly illuminated images. So generally speaking we are finding a threshold that splits the image pixels into two distributions whose inter-class variance(between-class variance) is maximizes or intra-class variance(within-class variance) is minimizes. Google Scholar, Otsu N (1979) A threshold selection method form gray-lever histograms[J]. Relationship between ME and value of noise intensity parameter. In this paper, we calculate the optimal threshold pair by searching the whole threshold value space . (b) Expanding for the first time. Soft Comput: Theories Appl. MathSciNet Multi-Otsu Thresholding#. 956961, 2011. 7, pp. The comparison results demonstrate the advantage of our proposed methods. Ridler and Calvard's, Kittler and Illingworth's and Otsu's methods for Signal Image Video Process 12(2):385392, Paul D, Daw N, Roy ND et al (2020) An automated dual threshold band-based approach for malaria parasite segmentation from thick blood smear[M]. In addition, the proposed methods are more robust in foreground area compared to MAOTSU_2D with the postprocessing technology. So the Thresholding process just boils down to finding a specific threshold for whatever task you wanna do with it. Below is shown two examples of the processed thresholds using Otsu method and the histogram of each of the images. The proposed method could reach best ME and DSC values for all images except for image #6, indicating best segmentation results. Generate a random number p between [0,1], update according to Eq. There are many ways to find the threshold. In this paper, we assume that the illumination is changing vertically, and we just partition the image into two parts. J. R. Parker, Gray level thresholding in badly illuminated images, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 3.3.9.7. Otsu thresholding Scipy lecture notes The update formula proposed in this article is: At the same time, the spiral motion interference coefficient l adopts adaptive change, the formula is as follows: Here: tis the current number of iterations and T is the maximum number of iterations. To solve the abovementioned problems of 2D Otsus algorithm, in this paper, we focus on 2D histogram constructing to enhance the robustness of 2D Otsus method to salt-and-pepper noise, and the image partitioning technology is studied to improve the algorithms effectiveness on uneven illumination images. decomposed the 2D Otsus method into two 1D Otsus algorithms and calculated the optimal threshold of each 1D Otsus algorithm independently [19]. proposed two adaptive thresholding schemes for uneven illuminated images. Otsu's Thresholding Method Based on a very simple idea: Find the threshold that minimizes the weighted within-class variance. Figure 12 exhibits the segmentation results of all the testing methods. 35, no. The results show that (1) NDRI can separate rapeseed from other vegetation well; (2) the OCED-THAT method can accurately map rapeseed in Jiangsu with an overall accuracy (OA) of 0.9559 and a Kappa coefcient of 0.8569, and it performed better than the Otsu-THAT method; (3) the OCED-THAT method had a lower but acceptable accuracy than the Random . To alleviate these limitations and improve the overall performance, here we propose an improved 2D Otsu's algorithm to . Ok, now we gotta worry about the criteria, which is, as you can see in the above program we are calculating the criteria for each of the available thresholds, and what we are doing with that is that, we are splitting the image into two parts. In addition, there are many other technologies for uneven image enhancement and segmentation including machine learning method [32] and background correction [33]. (a) Image coins. Let i, j be pixel gray level of original image and averaged image; then is a gray level pair representing that the pixel gray level in image I is i, and the gray level of the corresponding pixel at the same location in the averaged image G is j. It is a very straightforward idea to divide the uneven illumination images into some patches in which the illumination is uniform. threshold ( img,127,255, cv2. Unlike the seam-line, the splitting line makes the intensity difference around it maximum, and it should avoid crossing geometrical structure as much as possible as well. The position weight is defined using a Gaussian function defined below: For an image of size , parameter in formula (15) is set to be m/2 due to the effect of position weight, and is empirically set to in our implementation. Due to the limitation of traditional 2D Otsus method for uneven illumination images, in this section we explore partition strategy to improve the algorithms segmentation ability. Syst Eng Electron 42(10):21642171, Luo J, Yang YS et al (2019) Multi-threshold image segmentation of 2-D Otsu based on improved adaptive differential evolution algorithm[J]. Now before getting into other adaptive thresholding techniques, I want to discuss the limitation of Otsus Thresholding, and why we need Adaptive Thresholding. J Comput Appl 32(S2):147150, Watkins WA, Schevill WE (1979) Aerial observation of feeding behavior in for baleen whales: Eubalaena glacialis, Balaenoptera borealis, Megaptera novaeangliae, and Balaenoptera physalus[J]. One is a window merging method based on Lorentz information measure, and the other is a window growing technology using the notion of entropy. Signal Process 2(3):223237, Qin J, Shen XJ, Mei F et al (2019) An Otsu multi-thresholds segmentation algorithm based on improved ACO[J]. Experiments are conducted on both synthetic and real world images to evaluate the performance of the proposed method. A. S. Ashour, S. Samanta, N. Dey, N. Kausar, W. B. Abdessalemkaraa, and A. E. Hassanien, Computed tomography image enhancement using Cuckoo search: a log transform based approach, Journal of Signal and Information Processing, vol. The weakness of original 2D Otsus method, MAOTSU_2D, and DVE algorithm can be found obviously from the segmentation results on the synthetic image. It means that the dispersity between the two classes is the lowest (it is not a good result). Image Segmentation (Part 1). Thresholding, Otsu's and HSV | by Ralph However, it still has limitations on salt-and-pepper noise corrupted images and uneven illumination images. On one hand, the proposed scheme-1 can detect almost all the foreground objects; on the other hand, the proposed scheme-2 misses less foreground objects than the original 2D Otsus method, MAOTSU_2D, and Caos method. P. Roy, S. Dutta, N. Dey, G. Dey, S. Chakraborty, and R. Ray, Adaptive thresholding: a comparative study, in Proceedings of the 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT), pp. It is obvious that the two proposed schemes are more robust compared with original 2D Otsus method, MAOTSU_2D without postprocessing, and 1D Otsus methods including Caos method and DVE algorithm, whose ME values are growing rapidly with increasing noise. 38, pp. IEEE Trans. Otsu performance analysis. Google Scholar, Mishra S, Panda M (2018) Bat algorithm for multilevel color image segmentation using entropy-based thresholding[J]. As discussed in Section 2, the 2D histogram used in original 2D Otsus method and MAOTSU_2D lacks robustness to salt-and-pepper noise, and in this section we will firstly discuss the strategy for constructing a robust 2D histogram. Arguments However, from the analysis of the overall segmentation effect, HWOA has a good advantage, but we can also see that HWOA also has poor segmentation effect on some images, such as Couple. 33, no. The final segmentation result would be the better one from the two schemes. 519543, 2018. Global image threshold using Otsu's method - MATLAB graythresh - MathWorks foreground and background). Step 2: expanding sequentially until reaching the last column: for a pixel with as coordinate, calculate its cumulative energy using (18). Syst Eng Electron 37(7):15041511, Truongm TN, Kim S (2017) Automatic image thresholding using Otsus method and entropy weighting scheme for surface defect detection[J]. All algorithms are evaluated both quantitatively and qualitatively. Given a gray-scale image, a common task is to segment the image into a binary black & white image using simple thresholding. Average ME and DSC values of each algorithm on cell images. The cell dataset contains 22 cell images and the corresponding ground truths are manually labelled. For image #21, DVE achieves worst segmentation result while all other methods could generate similar results. Thresholding is used to create a binary image from a grayscale image [ 1]. 9921001, 2014. The optimal threshold value is determined by minimizing . Furthermore, we have previously proposed a new modified valley emphasis metric using second order derivative (DVE) to better fit the real valley of histogram [14]. Introduction. The histogram generated contains two peaks. Performance analysis of image thresholding: Otsu technique The proposed schemes can produce more competitive results on images #2, #8, and #13 than other methods except DVE. 26, no. 13021315, 2013. 2, pp. 339351, 2016. J. L. Fan and F. Zhao, Two-dimensional Otsus curve thresholding segmentation method for gray-level images, Acta Electronica Sinica, vol. The magnitude of gradient is adopted to represent the term, and we use Sobel operator to calculate the gradient. PubMedGoogle Scholar. Performance Analysis of Otsu-Based Thresholding Algorithms: A - Hindawi 14, no. R. Saini and M. Dutta, Image segmentation for uneven lighting images using adaptive thresholding and dynamic window based on incremental window growing approach, International Journal of Computer Applications, vol. However, it is worth noting that a median-average filter and a postprocess strategy were both adopted in MAOTSU_2D. It is simple and easy to implement. In recent years, researchers have found that in nature, the trajectories of many creatures in the process of searching for food satisfy the Lvy flight pattern. Later, Fan pointed out that in Ngs method only the valley point value of the histogram was weighted, and it could be beneficial to use the neighborhood information as well [13]. 7, pp. A vector of length equal to the total number of frames in x. The manually labelled ground truth for each image is shown in the second column. Syntax: cv2.threshold (source, thresholdValue, maxVal, thresholdingTechnique) We can say that these two classes represent the background and foreground of the input image respectively. Q. Huang, W. Gao, and W. Cai, Thresholding technique with adaptive window selection for uneven lighting image, Pattern Recognition Letters, vol. In the previous experimental sections, robustness and segmentation performance of the improved 2D Otsus method are tested. And, to get the suitable threshold value is to find the maximum value of Vb. The improvement is more significant on the synthetic image for proposed scheme 1, showing lowest ME and highest DSC. 48. If you are interested in the math secrets behind Otsu thresholding, the article provides a complete introduction. The PSNR calculation formula is as follows: SSIM measures image similarity from three aspects: image brightness, contrast, and structure. Tables 4 and 5 show the result of Friedman test. These quantitative results prove that the proposed method not only is effective on uneven illumination images, but can also achieve competitive results on regular illumination real world images. 2016-QGY-5, 2017-QGY-4, and 2018-QGY-6). 125, no. In this section, we will discuss automatic thresholding segmentation for uneven illumination images based on the two split parts, Ip1 and Ip2. Next, we evaluate the effectiveness of all testing methods quantitatively. In the followingthree sections, Ill explain how Otsu thresholding works and why it does that. Google Scholar. https://doi.org/10.1007/s11042-022-14041-1, DOI: https://doi.org/10.1007/s11042-022-14041-1. (b) Segmentation result of traditional 2D Otsus method. J.-L. IDE has fast convergence speed and performs well in three-threshold segmentation. Gray level of corresponding pixels in background or foreground should be approximate as these areas are relatively smooth, and this results in the fact that regions I and IV around the diagonal are corresponding to the background and foreground, respectively. Segmentation results of each algorithm on the synthetic and real world images. Details This turns out to be the same as maximizing the between-class variance. Otsu thresholding Scipy lecture notes Note Click here to download the full example code 3.3.9.7. 9, no. Later, Yue et al. Tables 1 and 2 show the values of PSNR and SSIM obtained from HWOA,WOA,GWO,PSO and IDE [16] at different threshold levels, and the computation time of the HWOA is given in Table 1. On the other hand, we proposed a second scheme to calculate a uniform threshold vector. In order to compute the color energy for the first row, we add image padding using the first row of the image in our implementation. Auto Threshold - ImageJ Wiki Description. It is so far still widely used in many applications including document image binarization [7], medical image processing [8], life science [9], and combating infectious diseases such as coronavirus disease (COVID-19) [10]. (a) Initialization. H.-F. Ng, Automatic thresholding for defect detection, Pattern Recognition Letters, vol. Consequently, we propose two schemes for uneven illumination image thresholding based on the partition technology. (a) Image coins corrupted by salt-and-pepper noise (. (a) Original images. As a fundamental technique for computer vision related applications, image segmentation has been studied for decades [16]. Note that the range of the pixel value is from 0 to 255. Otsu's method Binarization divides the histogram of the images in two groups, namely class 0 and class 1, as illustrated Fig. Comput Eng Appl 57(8):3647, Singh N, Goyal S (2018) Determination and segmentation of brain tumor using threshold segmentation with morphological operations[M]. There are lots of adaptive thresholding methods such as Otsus method, Kapurs method, and entropy based method [5]. 5, pp. 1, p. 74, 2017. Motivated by the fact that median filter is effective to salt-and-pepper noise, we introduce a median filtering step in 2D histogram construction, and we name the 2D Otsus method using the improved 2D histogram MMAOTSU_2D. Figure 2(c) shows the result of MAOTSU_2D on Figure 2(a), and the method produces a better segmentation result. In this section, experiments are conducted to verify the effectiveness of our proposed schemes on uneven illumination images. It is a widely used image segmentation method. (b) Manually labelled ground truth. 10221027, 2009. Vt is held fixed in one image, and Vw is minimum so that Vb should be maximum. Singapore: Springer 485500, Pun T (1980) A new method for grey-lever picture thresholding using the entropy of the histogram[J]. Numeric vector of length 2 specifying the histogram range used for thresholding. C.-H. Tung and Y.-G. Lin, Efficient uneven-lighting image binarization by support vector machines, Journal of Information and Optimization Sciences, vol. WOA and IDE are second only, the situation of PSO showing premature convergence is particularly obvious. 6, pp. For example, the binarization results of the proposed method for images #1, #2, #3, #4, and #7 are much closer to the corresponding ground truth compared with those of other algorithms. C. Sha, J. Hou, and H. Cui, A robust 2D Otsus thresholding method in image segmentation, Journal of Visual Communication and Image Representation, vol. 11821186, IEEE, Kanyakumari, India, July 2014. Multimed Tools Appl 82, 1500715026 (2023). (c) Projection of 2D histogram. PDF Two-Stepwise Hierarchical Adaptive Threshold Method for Automatic import cv2 from matplotlib import pyplot as plt img = cv2. Operates directly on the gray level histogram [e.g. 183, pp. Otsu's thresholding method [1] is useful to automatically perform clustering-based image thresholding. Figure 3(a) is the typical image rice widely used in image processing field, and one remarkable characteristic of image rice is nonuniform illumination. 1, pp. Figure 9 demonstrates the relationship between ME and noise intensity parameter which is ranging from 0 to 0.5. In the rest of this section, we will separately evaluate the algorithms robustness to salt-and-pepper noise and segmentation ability on uneven illumination images.