Image Thresholding Algorithms Research Articles

Analysis of morphological properties of fibrous electrospun polyurethane grafts using image segmentation

The concentration of the polymer in the electrospinning solution greatly influences the mechanical behaviour of electrospun vascular grafts due to the influence on scaffold morphology. The scaffold morphology (fiber diameter, fiber orientation and inter-fiber voids) of the grafts plays an important role in their behaviour during use. Even though manual methods and complex algorithms have been used so far for characterisation of the morphology of electrospun architecture, they still have several drawbacks that limit their reliability. This study therefore uses conventional, statistical region merging and a hybrid image segmentation algorithm, to characterise the morphology of the electrospun vascular grafts. Consequently, vascular grafts were fabricated using an in-house electrospinning equipment using three polymer material concentration levels (14%, 16% and 18%) of medical-grade thermoplastic polyurethane (Pellethane®). The image thresholding and segementation algorithms were then used for segmentation of SEM images extracted from the polymer grafts and then morphological parameters were investigated in terms of fiber diameter, fiber orientation, and interfiber spaces (pore area and porosity). The results indicate that electrospun image segmentation was "best" when the hybrid algorithm and the conventional algorithm was used, which implied that fiber property values computed from the hybrid algorithm were closed to the manually measurements especially for the 14% PU with fiber diameter 2.2%, fiber orientation 7.6% and porosity at 1.9%. However there was higher disperity between the manual and hybrid algorithm. This suggests more fiber uniformity in the 14%PU potentially affected the accuracy of the hybrid algorithm.

Hybrid improved capuchin search algorithm for plant image thresholding.

With the development and wider application of meta-heuristic optimization algorithms, researchers increasingly apply them to threshold optimization of multi-level image segmentation. This paper explores the performance and effects of Capuchin Search Algorithm (CAPSA) in threshold optimization. To solve problems of uneven distribution in the initial population of Capuchin Search Algorithm, low levels of global search performance and premature falling into local optima, this paper proposes an improved Capuchin Search Algorithm (ICAPSA) through a multi-strategy approach. ICAPSA uses chaotic opposite-based learning strategy to initialize the positions of individual capuchins, and improve the quality of the initial population. In the iterative position updating process, Levy Flight disturbance strategy is introduced to balance the global optimization and local exploitation of the algorithm. Finally, taking Kapur as the objective function, this paper applies ICAPSA to multi-level thresholding in the plant images, and compares its segmentation effects with the original CAPSA, the Fuzzy Artificial Bee Colony algorithm (FABC), the Differential Coyote Optimization Algorithm (DCOA), the Modified Whale Optimization Algorithm (MWOA) and Improved Satin Bowerbird Optimization Algorithm (ISBO). Through comparison, it is found that ICAPSA demonstrates superior segmentation effect, both in the visual effects of image segmentation and in data comparison.

Parallel Approach of Adaptive Image Thresholding Algorithm on GPU

Image thresholding is used to segment an image into background and foreground using a given threshold. The threshold can be generated using a specific algorithm instead of a pre-defined value obtained from observation or experiment. However, the algorithm involves per pixel operation, histogram calculation, and iterative procedure to search the optimum threshold that is costly for high-resolution images. In this research, parallel implementations on GPU for three adaptive image thresholding methods, namely Otsu, ISODATA, and minimum cross-entropy, were proposed to optimize their computational times to deal with high-resolution images. The approach involves parallel reduction and parallel prefix sum (scan) techniques to optimize the calculation. The proposed approach was tested on various sizes of grayscale images. The result shows that the parallel implementation of three adaptive image thresholding methods on GPU achieves 4-6 speeds up compared to the CPU implementation, reducing the computational time significantly and effectively dealing with high-resolution images.

Analysis of Image Thresholding Algorithms for Automated Machine Learning Training Data Generation

Secured compounds often safeguard physical layout details of both internal and external facilities, but these details are at risk due to the growing inclusion of Light Detection and Ranging (LiDAR) sensors in consumer off-the-shelf (COTS) technology such as cell phones. The ability to record detailed distance data with cell phones facilitates the production of high-quality three-dimensional scans in a discrete manner which directly threatens the security of private compounds. Therefore, it behooves the organizations in charge of private compounds to detect LiDAR activity. Many security cameras already detect LiDAR sources as generic light sources in specific conditions, but further analysis must identify these light sources as LiDAR sources in order to alert an organization of a potential security incident. Testing confirms the feasibility of identifying some LiDAR sources based on the color and intensity of light shined directly into a camera sensor, but this analysis proves inadequate for cell phone LiDAR. However, the unique intensity and pattern characteristics of cell phone LiDAR reflected off a surface can potentially be identified by an object identification machine learning model. In order to train a model to identify a LiDAR object, we must first produce a training dataset containing marked and labelled LiDAR objects. To do this, we apply an image thresholding algorithm to isolate the LiDAR object in an image to calculate its bounding box. The image thresholding algorithm directly affects the bounding box accuracy, so we test two different algorithms and find that Otsu’s image thresholding algorithm performs best, resulting in 99.5% accurate bounding boxes.

Self-Adaptive Image Thresholding within Nonextensive Entropy and the Variance of the Gray-Level Distribution.

In order to automatically recognize different kinds of objects from their backgrounds, a self-adaptive segmentation algorithm that can effectively extract the targets from various surroundings is of great importance. Image thresholding is widely adopted in this field because of its simplicity and high efficiency. The entropy-based and variance-based algorithms are two main kinds of image thresholding methods, and have been independently developed for different kinds of images over the years. In this paper, their advantages are combined and a new algorithm is proposed to deal with a more general scope of images, including the long-range correlations among the pixels that can be determined by a nonextensive parameter. In comparison with the other famous entropy-based and variance-based image thresholding algorithms, the new algorithm performs better in terms of correctness and robustness, as quantitatively demonstrated by four quality indices, ME, RAE, MHD, and PSNR. Furthermore, the whole process of the new algorithm has potential application in self-adaptive object recognition.

An adaptive firefly algorithm for multilevel image thresholding based on minimum cross-entropy

An adaptive firefly algorithm for multilevel image thresholding based on minimum cross-entropy

Fuzzy Hybrid Coyote Optimization Algorithm for Image Thresholding

In order to address the problems of Coyote Optimization Algorithm in image thresholding, such as easily falling into local optimum, and slow convergence speed, a Fuzzy Hybrid Coyote Optimization Algorithm (hereinafter referred to as FHCOA) based on chaotic initialization and reverse learning strategy is proposed, and its effect on image thresholding is verified. Through chaotic initialization, the random number initialization mode in the standard coyote optimization algorithm (COA) is replaced by chaotic sequence. Such sequence is nonlinear and long-term unpredictable, these characteristics can effectively improve the diversity of the population in the optimization algorithm. Therefore, in this paper we first perform chaotic initialization, using chaotic sequence to replace random number initialization in standard COA. By combining the lens imaging reverse learning strategy and the optimal worst reverse learning strategy, a hybrid reverse learning strategy is then formed. In the process of algorithm traversal, the best coyote and the worst coyote in the pack are selected for reverse learning operation respectively, which prevents the algorithm falling into local optimum to a certain extent and also solves the problem of premature convergence. Based on the above improvements, the coyote optimization algorithm has better global convergence and computational robustness. The simulation results show that the algorithm has better thresholding effect than the five commonly used optimization algorithms in image thresholding when multiple images are selected and different threshold numbers are set.

Correction to: An adaptive Lévy flight firefly algorithm for multilevel image thresholding based on Rényi entropy

Correction to: An adaptive Lévy flight firefly algorithm for multilevel image thresholding based on Rényi entropy

An adaptive Lévy flight firefly algorithm for multilevel image thresholding based on Rényi entropy

An adaptive Lévy flight firefly algorithm for multilevel image thresholding based on Rényi entropy

Bat algorithm for multilevel image thresholding based on Otsu and Kapur’s entropy

Multilevel image thresholding has attracted plenty of attention in the past decades. Otsu and Kapur’s entropy-based methods are often applied to search the optimal bi-thresholding. These techniques are also suitable for multilevel thresholds. However, it takes a lot of computation to solve the multilevel threshold problem. To address this problem, in this paper, a recently proposed bat algorithm is used to find the appropriate multilevel thresholds, in which Otsu and Kapur’s entropy is regarded as its fitness functions. Evaluation of image segmentation effect is performed using the peak-to-signal ratio (PSNR) and structural similarity (SSIM) index. The experiment results show that Otsu based method is more suitable for multi-level threshold image segmentation.

Constriction coefficient based particle swarm optimization and gravitational search algorithm for multilevel image thresholding

AbstractImage segmentation is one of the pivotal steps in image processing. Actually, it deals with the partitioning of the image into different classes based on pixel intensities. This work introduces a new image segmentation method based on the constriction coefficient‐based particle swarm optimization and gravitational search algorithm (CPSOGSA). The random samples of the image act as searcher agents of the CPSOGSA algorithm. The optimal number of thresholds is determined using Kapur's entropy method. The effectiveness and applicability of CPSOGSA in image segmentation is accomplished by applying it to five standard images from the USC‐SIPI image database, namely Aeroplane, Cameraman, Clock, Lena, and Pirate. Various performance metrics are employed to investigate the simulation outcomes, including optimal thresholds, standard deviation, MSE (mean square error), run time analysis, PSNR (peak signal to noise ratio), best fitness value calculation, convergence maps, segmented image graphs, and box plot analysis. Moreover, image accuracy is benchmarked by utilizing SSIM (structural similarity index measure) and FSIM (feature similarity index measure) metrics. Also, a pairwise non‐parametric signed Wilcoxon rank‐sum test is utilized for statistical verification of simulation results. In addition, the experimental outcomes of CPSOGSA are compared with eight different algorithms including standard PSO, classical GSA, PSOGSA, SCA (sine cosine algorithm), SSA (salp swarm algorithm), GWO (grey wolf optimizer), MFO (moth flame optimizer), and ABC (artificial bee colony). The simulation results clearly indicate that the hybrid CPSOGSA has successfully provided the best SSIM, FSIM, and threshold values to the benchmark images.

An improved cuckoo search algorithm for multilevel color image thresholding based on modified fuzzy entropy

Multilevel thresholding is a significant technology for image segmentation. Traditional exhaustive search method to solve the optimal multilevel thresholds is time consuming, color images contain more information are even worse. To address this issue, an improved cuckoo search algorithm (ICS) is proposed for color image segmentation in this paper, and a modified fuzzy entropy is used as its objective function. In ICS, two modifications are adopted to improve the standard cuckoo search algorithm. First, an adaptive control parameter mechanism is utilized to enhance the performance of exploration. Second, a hybrid search strategy is used to boost the local search efficiency. To fully demonstrate the superior performance of the ICS, the experiments are conducted on a series of color benchmark test images, and a total of six optimization algorithms are compared with the proposed algorithm. The experimental results show that the presented ICS algorithm outperforms all the other algorithms in term of objective function value, PSNR, FSIM, convergence speed and parametric statistical test. Compared to other algorithms, the ICS algorithm is an effective method for multilevel color image thresholding segmentation.

An adaptive gravitational search algorithm for multilevel image thresholding

An adaptive gravitational search algorithm for multilevel image thresholding

Colorimetric Sensor Array Reader System Using Smartphone Camera

Colorimetric Sensor Array Reader System Using Smartphone Camera

An improved cuckoo search algorithm for multi-level gray-scale image thresholding

In decades, Yang’s cuckoo search algorithm has been widely developed to select the optimal threshold of bi-level image threshoding, but the amount of computation of which increases exponentially with multi-level thresholding. To reduce the computation quantity, the iterative step size is adaptively decided by its fitness values of the current iteration without using the Levy distribution in this study. The modification may cause the solution drops into the local optima during the later period. Therefore, the constant discovery probability pa is automatically changed relating to the current and total iterations. And then, to verify segmentation accuracy and efficiency of the proposed method, an adaptive cuckoo search algorithm proposed by Naik and Yang’s cuckoo search algorithm are included to test on several gray-scale images. The results show that the proposed algorithm is expert in selecting optimal thresholds for segmenting gray-scale image.

A new automatic thresholding algorithm for unimodal gray-level distribution images by using the gray gradient information

Automatic thresholding is an important image-processing technique that extracts target regions of interest from background based on the gray-level information. There are many methods that have been proposed to provide an appropriate threshold value for an image with an evident bimodal gray-level histogram. Nevertheless, most of these methods can not accurately and efficiently segment an image with a unimodal gray-level distribution. In this study, a new approach of determining the optimal threshold is proposed to address the above issues by introducing the gray gradient of pixels. The gray gradient value of each pixel is calculated by performing an element multiplication with the image patch and the Sobel operator masks. Then, the gray gradient distribution is obtained on the basis of the results of statistical averaging the gray gradient values of the pixels with different gray-levels. By analysing the morphology of the gray gradient distribution histogram, the optimal threshold for image segmentation is obtained. Furthermore, several test images with unimodal gray-level distributions are segmented by other algorithms, such as the Otsu algorithm, MaxEntropy algorithm and Valley-Emphasis algorithm, to assess the performance of our proposed algorithm. In addition, conventional physical mercury intrusion porosimetry (MIP) and nuclear magnetic resonance (NMR) are used to test the porosity of the imaged samples, to further verify the accuracy of the new algorithm. The comparison results indicate that the proposed algorithm demonstrates a better segmentation performance over those of the other methods for an image with a unimodal gray-level distribution and results in a porosity that is in accordance with that of the MIP and NMR test.

Adaptive Kaniadakis entropy thresholding segmentation algorithm based on particle swarm optimization

Kaniadakis entropy is a kind of generalized entropy based on the $$ \kappa $$ probability distribution, which has a good ability to deal with the distribution of long tail. The image thresholding algorithm based on Kaniadakis entropy can effectively segment images with long-tailed distribution histograms, such as nondestructive testing image. However, Kaniadakis entropy is a generalized information entropy with parameter. How to choose appropriate parameter $$ \kappa $$ is a problem to be solved. In this paper, we proposed an adaptive parameter selection Kaniadakis entropy thresholding algorithm. Based on a clustering effectiveness evaluation index, we transform the parameter selection problem into an optimization problem, then use particle swarm optimization search algorithm to optimize it and finally obtain the segmentation threshold under the optimal parameter. The presented algorithm can adaptively select parameters according to different images and obtain the optimal segmentation images. In order to show the effectiveness of the proposed method, the segmentation results are compared with several existing entropy-based thresholding algorithms. Experimental results both qualitatively and quantitatively demonstrate that the proposed method is effective.

MOVING OBJECT DETECTION USING SPATIAL CORRELATION IN LAB COLOUR SPACE

Abstract. Background subtraction-based techniques of moving object detection are very common in computer vision programs. Each technique of background subtraction employs image thresholding algorithms. Different thresholding methods generate varying threshold values that provide dissimilar moving object detection results. A majority of background subtraction techniques use grey images which reduce the computational cost but statistics-based image thresholding methods do not consider the spatial distribution of pixels. In this study, authors have developed a background subtraction technique using Lab colour space and used spatial correlations for image thresholding. Four thresholding methods using spatial correlation are developed by computing the difference between opposite colour pairs of background and foreground frames. Out of 9 indoor and outdoor scenes, the object is detected successfully in 7 scenes whereas existing background subtraction technique using grey images with commonly used thresholding methods detected moving objects in 1–5 scenes. Shape and boundaries of detected objects are also better defined using the developed technique.

Estimation of Linkages between Biometric Indexes of Forests and Pattern of Canopy Spaces on Super-High-Resolution Satellite Images

This paper presents the results of studying a promising area related to the remote assessment of canopy spaces in forests by thresholding methods of image segmentation. The study is conducted based on the example of mixed forests in the Losiny Ostrov National Park. The proposed methodological approach to assessing the pattern of forest canopy on super-high (detailed) resolution satellite images is based on an analysis of light and shaded plots of canopy spaces using image-thresholding algorithms. The pixel count for different brightness thresholds give enough information to estimate a range of biometric indexes, including volume density and average age and height of stands from statistical relationships. The accuracy of estimates is assessed for prescribed deviations and verified against the norms of estimation of corresponding taxation data. We have found a statistical relationship of forest-canopy morphology indicators with brightness thresholds for shaded plots of canopy spaces and stemwood phytomass in forest ecosystems. Thus, super-high-resolution images may be considered an information basis for estimating the biometric parameters of stands, morphological indicators of forest canopy, and the productivity of forest ecosystems.

Automated Lung Segmentation on Computed Tomography Image for the Diagnosis of Lung Cancer.

Image processing techniques are widely used in several medical areas for early detection and treatment especially in the detection of various cancer tumors such as Squamous, Adenocarcinoma, Large Cell Carcinomas and Small Cell Lung Cancer. Segmentation of lung tissues from Computed Tomography (CT) image is considered as a pre-processing step in Lung Imaging. However, during Lung Segmentation, the Juxta-Pleural nodules (nodules attach to parenchymal walls) are missed out as they have similar appearance (intensity) to that of other non-pulmonary structures, which leads to a challenge to segment lung region along with Juxta-Pleural nodules. The complexity to segment lung region is mainly due to its inhomogeneity (different structures and intensity values of lungs). Thus, the existing segmentation algorithms like image thresholding algorithm, region-growing algorithm, active contour, level sets, etc. fail to segment lung tissues including Juxta-Pleural nodules. So, in this paper, a new fully-automated lung segmentation method with Juxta-Pleural nodules inclusion, is proposed.