Multilevel Thresholding Technique Research Articles

Maximum 3D Tsallis entropy based multilevel thresholding of brain MR image using attacking Manta Ray foraging optimization

Nevertheless, the accuracy of a multilevel image thresholding technique using 1D or 2D Tsallis entropy is limited. To overcome this, we propose a maximum 3D Tsallis entropy-based multilevel thresholding method. The idea of 3D Tsallis entropy is introduced. Opposed to the 1D/2D Tsallis entropy, the 3D Tsallis entropy-based approach is more robust, it performs well even in the case of the low signal-to-noise-ratio and contrast. Manta Ray Foraging Optimization (MRFO) algorithm is a newly introduced algorithm to solve the optimization problem by imitating the foraging technique of Manta Ray fish in the ocean using a mathematical model. Due to insufficient energy levels of search agents in MRFO, they fail to avoid local minima and fall on it. To make the algorithm more effective for the segmentation application, we introduce a new algorithm coined as attacking Manta Ray foraging optimization (AMRFO). A set of classical benchmark functions together with composite functions (CEC 2014) is used to validate the proposed AMRFO algorithm. Statistical analysis is implicitly carried out using Wilcoxon’s signed-rank test and Friedman’s mean rank test. Interestingly, the results show that the proposed AMRFO is superior to the state-of-the-art optimization algorithms. Moreover, the proposed method is also compared with 1D/2D Tsallis entropy-based approaches. To experiment, 100 test images from the AANLIB MR Image dataset are considered. Our method outperforms 1D/2D Tsallis entropy-based approaches. The proposed scheme would be useful for the segmentation of multi-spectral color images.

Corona Virus Detection and Classification with radiograph images using RNN

We’re working on detecting the symptoms of Corona virus, also known as Covid-19, in this project.COVID-19 is a highly infectious disease that has been declared a Pub- lic Health Emergency and a Pandemic by the World Health Organization.The virus has infected over 25 million people worldwide,which has killed over 840,000 people and threat- ened the lives of millions more. COVID-19 is characterised by a dry cough, sore throat, and a high temperature. It is critical to find quick and accurate results for Covid-19 at this time in order to stop it in its early stages and avoid it from being a problem. Deep learning concepts are being used to analyse and classify symptoms from radiograph im- ages.Chest radiographs are one of the early screening tests to assess the onset of disease since the infection seriously affects the lungs.In this proposal, we used a recurrent neu- ral network model combined with a multi-level thresholding technique to detect Corona virus. One of the machine learn- ing techniques for prediction is the RNN model. A Recur- rent Neural Network is used to decide if the given images belong to Covid-19 during the classification process. This implementation is based on a publicly available dataset of radiograph images.

Impact of image segmentation techniques on celiac disease classification using scale invariant texture descriptors for standard flexible endoscopic systems

Celiac disease (CD) is quite common and is a proximal small bowel disease that develops as a permanentintolerance to gluten and other cereal proteins in cereals. It is considered as one of the most di?icult diseases to diagnose.Histopathological evidence of small bowel biopsies taken during endoscopy remains the gold standard for diagnosis.Therefore, computer-aided detection (CAD) systems in endoscopy are a newly emerging technology to enhance thediagnostic accuracy of the disease and to save time and manpower. For this reason, a hybrid machine learning methodshave been applied for the CAD of celiac disease. Firstly, a context-based optimal multilevel thresholding technique wasemployed to segment the images. Afterward, images were decomposed into subbands with discrete wavelet transform(DWT), and the distinctive features were extracted with scale invariant texture recognition. Classification accuracy,sensitivity and specificity ratio are 94.79%, 94.29%, and 95.08%, respectively. The results of the proposed models arecompared with the results of other state-of-the-art methods such as a convolutional neural network (CNN) and higherorder spectral (HOS) analysis. The results showed that the proposed hybrid approaches are accurate, fast, and robust.

The Implementation of Multilevel Colour Thresholding on a Prototype Coffee Machine

The development of computer vision has expanded widely as there is a vast number of its applications in various aspects of daily life. One of its implementations is integrating the image processing technique on a prototype coffee machine based on the speech recognition system. This study aims to detect the requested coffee colour spoken by users which are black, middle and light. The sensor used in this research is a digital PC camera and the applied method is Multilevel Colour Thresholding. Of all experiments conducted, the image processing technique can work perfectly as the camera is able to identify the requested colour of the coffee solution. Furthermore, the system might be developed by improving the multilevel colour thresholding technique as well as advancing the hardware design in order to establish more robust coffee machine based on the requested colour.

A novel evolutionary row class entropy based optimal multi-level thresholding technique for brain MR images

The local averaging technique adopted for the construction of 2D histogram in Otsu’s method fails to preserve the edge information. Further, the consideration of the diagonal pixels only results in the loss of information. These make the 2D Otsu method of multi-level thresholding inefficient to retain the spatial correlation information. Although the computation of 2D histogram based on gray gradient information is a better way to threshold an image, it faces a backlash due to the high magnitude peaks. To solve these problems, we suggest a new normalized local variance (NLV) method for constructing 2D histogram using the local variance followed by a novel evolutionary row class entropy (ERCE) method for optimal multi-level image thresholding, which tries to preserve maximum spatial information through normalization of the local variance. A new optimization technique called hybrid Adaptive Cuckoo Search-Squirrel Search Algorithm (ACS-SSA) is also introduced. A new fitness function is suggested. The standard CEC 2005 benchmark test functions are used to validate the performance of our proposed ACS-SSA technique. The optimum threshold values obtained are used to segment 100 slices of T-2 weighted axial brain MR images (taken from the Harvard Medical School database). Several performance evaluation metrics are computed to compare the performance of our method with the state-of-the-art methods. The analysis of the results shows that ERCE method outperforms other methods. This method may set a new direction in the multilevel image thresholding research.

Normalized square difference based multilevel thresholding technique for multispectral images using leader slime mould algorithm

The existing methodologies used for multilevel thresholding are not efficient in terms of both accuracy and computation time. Two-dimensional histogram-based techniques are better in terms of accuracy while they are computation intensive. The slime mould algorithm used for optimization mainly depends on the best leader and two randomly pooled slime moulds from the population, which leads to poor exploitation with more iteration to converge. These problems are solved in this paper by introducing a novel normalized square difference (NSD) based multilevel thresholding technique using a leader slime mould algorithm (LSMA). The contributions are many fold – i) a NSD based multilevel thresholding method is proposed using the gray level and normalized square difference (GLNSD) 2-D histogram with reduced computation; ii) LSMA is suggested; iii) 23 classical and 6 modern composition test functions from the IEEE CEC 2014 test suite are considered for evaluation of LSMA; iv) experiments on multispectral images are presented. The benefits are – i) reduces computations, ii) improves accuracy. The qualitative metrics used for analysis include – search history, trajectory, and average fitness history. Scalability analysis and statistical analysis (using Friedman's mean rank test) are presented. The proposal is compared with state-of-the-art techniques and found better.

Multilevel thresholding image segmentation based on energy curve with harmony Search Algorithm

Image segmentation is a process of portion image into regions. From image segmentation schemes available, multilevel thresholding on the histogram is a highly established method. Otsu’s method is a significant multilevel thresholding technique, in this multiple threshold levels selected on histogram and group the pixels of an image into different regions. The optimized threshold levels computed with an Optimized technique by maximizing the inter-class variance. Methods with histograms are incapable to possess spatial details of contextual information for finding optimal threshold levels. As a remedy, a novel method proposed the Energy Curve is used instead of a histogram with Otsu’s method and Harmony Search Algorithm to compute optimized gray levels. The proposed method experimented on several benchmark images, and results compared with various optimization algorithms with histogram by Dunn Index, DB Index, SD Index, mean of fitness and PSNR, comparison clarifies that the proposed method is superior to histogram-based methods.

Elephant Herding Optimization for Multi-Level Image Thresholding

Multilevel thresholding plays a significant role in the arena of image segmentation. The main issue of multilevel image thresholding is to select the optimal combination of threshold value at different level. However, this problem has become challenging with the higher number of levels, because computational complexity is increased exponentially as the increase of number of threshold. To address this problem, this paper has proposed elephant herding optimization (EHO) based multilevel image thresholding technique for image segmentation. The EHO method has been inspired by the herding behaviour of elephant group in nature. Two well-known objective functions such as ‘Kapur's entropy' and ‘between-class variance method' have been used to determine the optimized threshold values for segmentation of different objects from an image. The performance of the proposed algorithm has been verified using a set of different test images taken from a well-known benchmark dataset named Berkeley Segmentation Dataset (BSDS). For comparative analysis, the results have been compared with three popular algorithms, e.g. cuckoo search (CS), artificial bee colony (ABC) and particle swarm optimization (PSO). It has been observed that the performance of the proposed EHO based image segmentation technique is efficient and promising with respect to the others in terms of the values of optimized thresholds, objective functions, peak signal-to-noise ratio (PSNR), structure similarity index (SSIM) and feature similarity index (FSIM). The algorithm also shows better convergence profile than the other methods discussed.

Development of Ripeness Indicator for Quality Assessment of Harumanis Mango by using Image Processing Technique

Visual appearance is the main source of information that can be used for quality assessment of mango. In this study, a non-destructive ripeness level estimation for mango of the cultivar Harumanis based on digital image analysis was employed. The changing peel and flesh colour of mango is strongly correlated to ripeness that can be measured as a sensual quality parameter. This measurement of ripeness level has been determined by image analysis technique which needs to attribute external and internal colour feature from image segmentation Multilevel thresholding technique is proposed for colour image segmentation to extract the mango region from the background which every channel of five colour spaces have been applied. Colour analysis technique and Total soluble solids (TSS) is used to compare and evaluate for the prediction. The optimal results were obtained that a* channel from L*a*b colour space has given more logical and better performance of prediction which is more than 92% accuracy.

A novel interdependence based multilevel thresholding technique using adaptive equilibrium optimizer

The multilevel thresholding is one important operation in computer vision, which is a subfield of artificial intelligence (AI), used to understand and interpret the data in the real world. The existing entropic methods, based on the histogram of an image for the multilevel thresholding mostly deal with the maximization of the entropic information excluding the shred boundary, which reduces the accuracy. These problems lead to the poor thresholding accuracy and a lower speed. To address the problem, we propose a novel interdependence based technique that uses the shred boundary, which is a minimization problem. A firsthand objective function is investigated, which takes care of the shred boundary. The traditional multilevel thresholding techniques are computationally expensive due to the exhaustive search process, an alternate method is to use the evolutionary computation based on a nature-inspired algorithm. In this paper, a new optimizer called adaptive equilibrium optimizer (AEO) is also proposed for multilevel thresholding, an improvement over the basic equilibrium optimizer (EO) by implementing an adaptive decision making of dispersal for nonperformer search agents. The AEO performance is compared with state-of-the-art algorithms — equilibrium optimizer (EO), gray wolf optimizer (GWO), whale optimization algorithm (WOA), squirrel search algorithm (SSA) and the wind driven optimization (WDO) algorithm, using standard benchmark functions. Based on the qualitative and quantitative analysis, the AEO outperformed EO, GWO, WOA, SSA, and WDO. The optimal thresholds are obtained by minimizing the objective function using the AEO. For the experiment, 500 images of the BSDS 500 dataset are considered. Popular metrics such as the peak signal to noise ratio (PSNR), structural similarity index (SSIM), and the feature similarity index (FSIM) are considered for a quantitative analysis. Remarkable differences in the thresholding accuracy are observed with a simultaneously decreasing computational complexity. The merits of the paper are highlighted to ensure its future use in the world of engineering applications using soft computing, a subfield of the AI.

A new fusion of mutual information and Otsu multilevel thresholding technique for hyperspectral band selection

Hyperspectral data are a curse with huge dimensionality, high redundancy of spectral information, and are noisy in nature. Hundreds of narrow adjacent bands are present in HS (Hyperspectral) data with high spectral information and it always leads to a computational complexity in space and time. The information theoretic methods are used for hyperspectral band selection to avoid computational complexity. In order to address this issue, the new fusion of Mutual Information (MI) with Otsu (MI_Otsu) threshold method is proposed for hyperspectral band selection by employing three different entropy measures such as joint, conditional, and relative. The proposed approach identifies the probabilities, entropy, and mutual information between two hyperspectral bands. The optimal threshold is obtained using Otsu multi-threshold technique and highly informative bands will be selected. In addition, the SVM (Support Vector Machine) classification technique is adapted for further classification of selected bands to analyze the performance of the proposed algorithm. The experimental analysis is carried out using the real-time dataset from the test site ‘Indian Pines’ in Northwestern Indiana recorded by AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) sensor that demonstrates the effectiveness of this proposed approach. It is proved that the proposed work shows the competitive performance even with less selected bands and the relative MI_Otsu method shows a higher accuracy of 92.16% with the comparison of joint and conditional MI_Otsu.

An adaptive Harris hawks optimization technique for two dimensional grey gradient based multilevel image thresholding

A metaheuristic algorithm called Harris hawks optimization (HHO) is gaining its popularity among its clan and useful for optimization. In this algorithm, the prey gets completely exhausted when the escape energy is equal to zero, therefore it fails to explore further. The random operator chosen in the existing method is a wastage of search agents (Harris hawk). To overcome this issue, we propose an adaptive Harris Hawks optimization (AHHO) technique. In this work, the mutation is employed to restrict the escape energy within the range 0,2, except for the mutation interval. Our method adaptively decides the chance of the Harris hawk would do perch along with the other family members or move to a random tall tree with the help of average fitness. The proposed AHHO algorithm is benchmarked with 23 classical test functions and 30 modern test function from CEC 2014 test suite consisting of unimodal, multimodal, hybrid and composite functions. The qualitative and quantitative analysis, which include metrics such as statistical results, convergence curves, p-value from Wilcoxon rank-sum test and Friedman mean rank. It reveals that AHHO provides good results when compared with other well-known nature-inspired algorithms. It can be used for multilevel thresholding which is an optimization problem. Recently, 2D histogram based multilevel image thresholding techniques are becoming more popular for different image processing applications. The local averaging scheme used for the construction of a 2D histogram in existing methods fails to preserve the edge information. The choice of the diagonal pixels only results in the loss of information making the earlier multi-level thresholding methods inefficient to retain the spatial correlation information. Although the computation of 2D histogram based on grey gradient information is a better way to threshold an image, it faces problems due to the presence of the edge magnitude peaks. These problems are solved by investigating an improved 2D grey gradient (I2DGG) method, a new technique is suggested in this paper to suppress high edge magnitudes. The I2DGG is a maximization problem, which requires an exhaustive search process. Therefore, AHHO is used to obtain the optimal threshold values. The result of our proposed AHHO based multilevel thresholding using the I2DGG method is obtained using all the 500 images from the Berkeley Segmentation Data set (BSDS 500). When we compare the proposed method I2DGG with 2D Tsallis entropy and 1D Tsallis entropy based multilevel thresholding, the I2DGG outperforms other methods. The experimental results are also compared with the state-of-art optimization-based multilevel thresholding methods, which shows our proposed method is beneficial to the segmentation field of image processing.

A soft-computing based approach towards automatic detection of pulmonary nodule

Early detection of lung cancer is the major challenge for physicians to treat and control this deadly disease whose primary step is to detect pulmonary nodule from thoracic computed tomography (CT) images. In view of increasing the accuracy of the pulmonary nodule detection methodology, this paper proposes a novel technique that can aid early diagnosis of the patients. The study has considered high resolution computed tomography (HRCT) images from two public datasets LIDC and Lung-TIME and an independent dataset, created in collaboration between Peerless Hospital Kolkata and University of Calcutta. The key feature of the test dataset is that the class features are imbalanced in nature. The structures associated with lung parenchyma are segmented using parameterized multi-level thresholding technique, grayscale morphology, and rolling ball algorithm. Then random under sampling is implemented to overcome the imbalance class problem, followed by a feature selection methodology using binary particle swarm optimization (BPSO). The nodule and non-nodule classification are performed by implementing ensemble stacking. Indeed, it has been observed that there exists insufficient published literature that has been considered similar looking pulmonary abnormalities as non-nodule objects as well as imbalance class problem and feature selection algorithm to design an automated, accurate and robust model for automated detection of the pulmonary nodule. In reference to the LIDC dataset, the false positive, false negative detection rates and sensitivity are 1.01/scan, 0.56/scan and 99.01% respectively, which is an improvement in terms of accuracy as compared to the existing state-of-the-art research works.

Symbiotic Organisms Search Optimization for Multilevel Image Thresholding

Determination of optimum thresholds is the prime concern of any multilevel image thresholding technique. The traditional methods for multilevel thresholding are computationally expensive, time-consuming, and also suffer from lack of accuracy and stability. To address this issue, the authors propose a new methodology for multilevel image thresholding based on a recently developed meta-heuristic algorithm, Symbiotic Organisms Search (SOS). The SOS algorithm has been inspired by the symbiotic relationship among the organism in nature. This article has utilized the concept of the symbiotic relationship among the organisms to optimize three objective functions: Otsu's between class variance and Kapur's and Tsallis entropy for image segmentation. The performance of the SOS based image segmentation algorithm has been evaluated using a set of benchmark images and has been compared with four recent meta-heuristic algorithms. The algorithms are compared in terms of effectiveness and consistency. The quality of the algorithms has been estimated by some well-defined quality metrics such as peak signal-to-noise ratio (PSNR), structure similarity index (SSIM), and, feature similarity index (FSIM). The experimental results of the algorithms reveal that the balance of intensification and diversification of the SOS algorithm to achieve the global optima is better than others.

Hyperspectral image segmentation using discriminant independent component analysis and swarm optimization approaches

Hyperspectral data consists of hundreds of bands. The high dimension of hyperspectral data is a challenge for the researcher to design efficiency and accuracy image segmentation algorithm. In this paper, a new approach to extraction features and independence of hyperspectral image proposes using Discriminant independent component analysis (DICA) and multilevel thresholding techniques based on Otsu problems for image segmentation are introduced. Image segmentation is initial process of image analysis and recognition. Otsu’s problem with multilevel thresholding is solved in each band from hyperspectral data that has been reduced dimensionality using DICA. The main purpose using multilevel thresholding is to get an optimal threshold that maximizes variance between classes. The swarm optimization approach used in this study is Darwinian particle swarm optimization (DPSO). The result of experiment showed that DPSO was better compared to other swarm optimization approaches. DPSO shows a statistically significant increase, both from CPU time processing and fitness value. DPSO technique is able to find optimal threshold with greater variance between classes and smaller search times compared to particle swarm optimization (PSO).

Brain MR Image Multilevel Thresholding by Using Particle Swarm Optimization, Otsu Method and Anisotropic Diffusion

Multilevel thresholding is widely used in brain magnetic resonance (MR) image segmentation. In this article, a multilevel thresholding-based brain MR image segmentation technique is proposed. The image is first filtered using anisotropic diffusion. Then multilevel thresholding based on particle swarm optimization (PSO) is performed on the filtered image to get the final segmented image. Otsu function is used to select the thresholds. The proposed technique is compared with standard PSO and bacterial foraging optimization (BFO) based multilevel thresholding techniques. The objective image quality metrics such as Peak Signal to Noise Ratio (PSNR) and Mean Structural SIMilarity (MSSIM) index are used to evaluate the quality of the segmented images. The experimental results suggest that the proposed technique gives significantly better-quality image segmentation compared to the other techniques when applied to T2-weitghted brain MR images.

Oppositional symbiotic organisms search optimization for multilevel thresholding of color image

Selection of optimal threshold is the most crucial issue in threshold-based segmentation. In case of color image, this task is become challenging, because conventional color image segmentation has computational complexity and also it suffers from lack of accuracy. Various techniques such as threshold based, region growing, edge detection, graph cut, pixel classification, neural network, active contour, gray level co-occurrence matrix are proposed so far for image segmentation in the literature. Out of them, threshold-based segmentation is popular for its simplicity. To address the problem of color image segmentation, we propose an enhanced version of metaheuristic optimization algorithm called Opposition based Symbiotic Organisms Search (OSOS) to solve multilevel image thresholding technique for color image segmentation by introducing opposition based learning concepts to accelerate the convergence rate and enhance the performance of standard symbiotic organisms search (SOS). The performance of the proposed OSOS based algorithm is investigated thoroughly and compared with some existing techniques like Cuckoo Search (CS), BAT algorithm (BAT), artificial bee colony (ABC) and particle swarm optimization (PSO). The comparison is made by applying the algorithm to a set of color images taken from a well-known benchmark dataset (Berkeley Segmentation Dataset (BSDS)) and some of the color images collected for the COCO dataset. It is observed from the results that the performance of the OSOS based algorithm is promising with respect to standards SOS and others in terms of the values of objective functions as well as the values of some well-defined quality metrics such as peak signal-to-noise ratio (PSNR), structure similarity index (SSIM) and feature similarity index (FSIM). The results of the proposed algorithm may encourage the scientists and engineers to apply it into pattern recognition problems.

Segmentation by Fractional Order Darwinian Particle Swarm Optimization Based Multilevel Thresholding and Improved Lossless Prediction Based Compression Algorithm for Medical Images

The image segmentation refers to the extraction of region of interest and it plays a vital role in medical image processing. This work proposes multilevel thresholding based on optimization technique for the extraction of region of interest and compression of DICOM images by an improved prediction lossless algorithm for telemedicine applications. The role of compression algorithm is inevitable in data storage and transfer. Compared to the conventional thresholding, multilevel thresholding technique plays an efficient role in image analysis. In this paper, the Particle Swarm Optimization (PSO), Darwinian Particle Swarm Optimization (DPSO), and Fractional Order Darwinian Particle Swarm Optimization (FODPSO) are employed in the estimation of the threshold value. The simulation results reveal that the FODPSO-based multilevel level thresholding generate superior results. The fractional coefficient in FODPSO algorithm makes it effective optimization with fast convergence rate. The classification and blending prediction-based lossless compression algorithm generates efficient results when compared with the JPEG lossy and JPEG lossless approaches. The algorithms are tested for various threshold values and higher value of PSNR indicates the proficiency of the proposed segmentation approach. The performance of the compression algorithms was validated by metrics and was found to be appropriate for data transfer in telemedicine. The algorithms are developed in Matlab2010a and tested on DICOM CT images.

Image segmentation via multilevel thresholding using hybrid optimization algorithms

We introduce an alternative hybrid swarm algorithm for image segmentation that employs multilevel thresholding techniques. For the hybridization, we have combined the whale optimization algorithm (WOA) and the particle swarm optimization (PSO). The proposed method is called WOAPSO, and it operates in a cooperative environment, where the initial population is divided into two subpopulations (the first subpopulation is assigned for WOA and the other is assigned for PSO). Then, the WOA and the PSO operate in parallel during the iterative process to update the solutions and the best solution is selected from the union of the updated subpopulations according to the objective function. Here, two objective functions are used, the Otsu’s method and the fuzzy entropy method. These functions evaluate the quality of the thresholds generated by the WOAPSO considering the variance and the entropy of the classes where the pixels are cataloged. The experimental results and comparisons provide evidence of the ability of the proposed WOAPSO algorithm to reduce the time complexity without affecting the accuracy of the solutions.

Teaching-learning-based optimization algorithm to minimize cross entropy for Selecting multilevel threshold values

Image thresholding is one of the most important approaches for image segmentation. Among multilevel thresholding techniques, cross entropy has been widely used by researchers to find multilevel threshold values. In multilevel cross entropy thresholding techniques, main target is to find an optimal combination of threshold values at different levels for minimizing the cross entropy. In this paper, Teaching-Learning-based Optimization (TLBO) algorithm is used to find an optimal combination of threshold values at different levels for minimizing the cross entropy. TLBO algorithm is inspired by passing on knowledge within a classroom environment where students first gain knowledge from a teacher and then through mutual interaction. This new proposed approach is called the TLBO-based minimum cross entropy thresholding (TLBO-based MCET) algorithm. The performance of the proposed algorithm is tested on a number of standard test images. For comparative analysis, the results of TLBO-based MCET algorithm are compared with the results of Firefly-based minimum cross entropy thresholding (FF-based MCET), Honey Bee Mating Optimization-based minimum cross entropy thresholding (HBMO-based MCET) and Quantum Particle Swarm Optimization-based minimum cross entropy thresholding (Quantam PSO-based MCET). Comparative analysis is done based on two most popular objective performance measures, Peak Signal to Noise Ratio (PSNR) and Uniformity. From experimental results, it is observed that the proposed method is an efficient and feasible method to search an optimal combination of threshold values at 2nd, 3rd, 4th and 5th levels.