Multilevel Thresholding Technique Research Articles

Accurate multilevel thresholding image segmentation via oppositional Snake Optimization algorithm: Real cases with liver disease

Liver-related diseases significantly contribute to global mortality rates. Accurate segmentation of liver disease from CT scans is essential for early diagnosis and treatment selection, particularly in computer-aided diagnosis (CAD) systems. To address challenges posed by inconsistent liver presence and unclear boundaries, an enhanced Snake Optimization (SO) algorithm is proposed that integrates with opposition-based learning (OBL) called (SO-OBL), proving effective in global optimization and multilevel image segmentation. Experiments using CEC’2022 test functions compare SO-OBL with eleven recent and state-of-the-art metaheuristic algorithms, demonstrating its superior performance. Additionally, an advanced liver disease segmentation model based on SO-OBL incorporates an optimized multilevel thresholding technique, leveraging Otsu’s function. Notable segmentation metric results, including FSIM = 0.947, SSIM = 0.941, PSNR = 24.876, MSE = 236.88, and execution time = 0.281, underscore the model’s efficiency and potential for accurate diagnosis in CAD systems.

Fractional-order modified heterogeneous comprehensive learning particle swarm optimizer for intelligent disease detection in IoMT environment

This paper presents an alternative early disease detection technique based on the Internet of Medical Things (IoMT) to improve the healthcare system. Since it is necessary to address different aspects to help healthcare organizations to improve their efficiency. Early disease detection using a fast and efficient test, such as chest X-ray images, is one of the most important issues. In this paper, we develop an efficient model to allocate abnormalities in medical chest X-ray images. The developed model consists of a set of stages; the first stage is to segment the images using a multilevel thresholding technique to determine the lung inside the image, then extracting the features from the segmented objects using different extractor methods. Then, we proposed a Fractional-order modified Heterogeneous comprehensive learning particle swarm optimizer (FMHCLPSO) as a feature selection method to determine the relevant features used to improve the detection process. To evaluate the performance of the developed IoMT model, a set of three medical datasets is used, called Covid-19 & Pneumonia, X-raycovid, and Radiography. The results illustrate the high efficiency of the developed model to detect diseases based on performance measures. Furthermore, We compared the proposed method to several existing methods, and it showed significant performance.

Gynecological Healthcare: Unveiling Pelvic Masses Classification through Evolutionary Gravitational Neocognitron Neural Network Optimized with Nomadic People Optimizer.

Accurate and early detection of malignant pelvic mass is important for a suitable referral, triage, and for further care for the women diagnosed with a pelvic mass. Several deep learning (DL) methods have been proposed to detect pelvic masses but other methods cannot provide sufficient accuracy and increase the computational time while classifying the pelvic mass. To overcome these issues, in this manuscript, the evolutionary gravitational neocognitron neural network optimized with nomadic people optimizer for gynecological abdominal pelvic masses classification is proposed for classifying the pelvic masses (EGNNN-NPOA-PM-UI). The real time ultrasound pelvic mass images are augmented using random transformation. Then the augmented images are given to the 3D Tsallis entropy-based multilevel thresholding technique for extraction of the ROI region and its features are further extracted with the help of fast discrete curvelet transform with the wrapping (FDCT-WRP) method. Therefore, in this work, EGNNN optimized with nomadic people optimizer (NPOA) was utilized for classifying the gynecological abdominal pelvic masses. It was executed in PYTHON and the efficiency of the proposed method analyzed under several performance metrics. The proposed EGNNN-NPOA-PM-UI methods attained 99.8%. Ultrasound image analysis using the proposed EGNNN-NPOA-PM-UI methods can accurately predict pelvic masses analyzed with the existing methods.

Multilevel Colonoscopy Histopathology Image Segmentation Using Particle Swarm Optimization Techniques.

Histopathology image segmentation is a challenging task in medical image processing. This work aims to segment lesion regions from colonoscopy histopathology images. Initially, the images are preprocessed and then segmented using the multilevel image thresholding technique. Multilevel thresholding is considered an optimization problem. Particle swarm optimization (PSO) and its variants, darwinian particle swarm optimization (DPSO), and fractional order darwinian particle swarm optimization (FODPSO) are used to solve the optimization problem and they generate the threshold values. The threshold values obtained are used to segment the lesion regions from the images of the colonoscopy tissue data set. Segmented images containing the lesion regions are then postprocessed to remove unnecessary regions. Experimental results reveal that the FODPSO algorithm with Otsu's discriminant criterion as the objective function achieves the best accuracy, Dice and Jaccard values of 0.89, 0.68 and 0.52, respectively, for the colonoscopy data set. The FODPSO algorithm also outperforms other optimization methods such as artificial bee colony and the firefly algorithms in terms of the accuracy, Dice and Jaccard values.

An Improved Search and Rescue Algorithm for Global Optimization and Blood Cell Image Segmentation

Image segmentation has been one of the most active research areas in the last decade. The traditional multi-level thresholding techniques are effective for bi-level thresholding because of their resilience, simplicity, accuracy, and low convergence time, but these traditional techniques are not effective in determining the optimal multi-level thresholding for image segmentation. Therefore, an efficient version of the search and rescue optimization algorithm (SAR) based on opposition-based learning (OBL) is proposed in this paper to segment blood-cell images and solve problems of multi-level thresholding. The SAR algorithm is one of the most popular meta-heuristic algorithms (MHs) that mimics humans' exploration behavior during search and rescue operations. The SAR algorithm, which utilizes the OBL technique to enhance the algorithm's ability to jump out of the local optimum and enhance its search efficiency, is termed mSAR. A set of experiments is applied to evaluate the performance of mSAR, solve the problem of multi-level thresholding for image segmentation, and demonstrate the impact of combining the OBL technique with the original SAR for improving solution quality and accelerating convergence speed. The effectiveness of the proposed mSAR is evaluated against other competing algorithms, including the L'evy flight distribution (LFD), Harris hawks optimization (HHO), sine cosine algorithm (SCA), equilibrium optimizer (EO), gravitational search algorithm (GSA), arithmetic optimization algorithm (AOA), and the original SAR. Furthermore, a set of experiments for multi-level thresholding image segmentation is performed to prove the superiority of the proposed mSAR using fuzzy entropy and the Otsu method as two objective functions over a set of benchmark images with different numbers of thresholds based on a set of evaluation matrices. Finally, analysis of the experiments' outcomes indicates that the mSAR algorithm is highly efficient in terms of the quality of the segmented image and feature conservation, compared with the other competing algorithms.

Artificial Intelligence Approach for Early Detection of Brain Tumors Using MRI Images

Artificial intelligence (AI) is one of the most promising approaches to health innovation. The use of AI in image recognition considerably extends findings beyond the constraints of human sight. The application of AI in medical imaging, which relies on picture interpretation, is beneficial for automatic diagnosis. Diagnostic radiology is evolving from a subjective perceptual talent to a more objective science thanks to AI. Automatic object detection in medical images is an essential AI technology in medicine. The problem of detecting brain tumors at an early stage is well advanced with convolutional neural network (CNN) and deep learning algorithms (DLA). The problem is that those algorithms require a training phase with a big database of more than 500 images and time-consuming with a complex computational and expensive infrastructure. This study proposes a classical automatic segmentation method for detecting brain tumors in the early stage using MRI images. It is based on a multilevel thresholding technique on a harmony search algorithm (HSO); the algorithm was developed to suit MRI brain segmentation, and parameters selection was optimized for the purpose. Multiple thresholds, based on the variance and entropy functions, break the histogram into multiple portions, and different colors are associated with each portion. To eliminate the tiny arias supposed as noise and detect brain tumors, morphological operations followed by a connected component analysis are utilized after segmentation. The brain tumor detection performance is judged using performance parameters such as Accuracy, Dice Coefficient, and Jaccard index. The results are compared to those acquired manually by experts in the field. The results were further compared with different CNN and DLA approaches using Brain Images dataset called the “BraTS 2017 challenge”. The average Dice Index was used as a performance measure for the comparison. The results of the proposed approach were found to be competitive in accuracy to those obtained by CNN and DLA methods and much better in terms of execution time, computational complexity, and data management.

A modified threshold score‐based multilevel thresholding segmentation technique for brain magnetic resonance images using opposition‐based learning hybrid rice optimization algorithm

AbstractIn modern clinical diagnostics, magnetic resonance imaging (MRI) is frequently used for brain tumor detection because of its high resolution of soft tissues, which plays a crucial role in the prevention, detection, and treatment planning. Therefore, it is meaningful to obtain high‐quality MR images by automatic thresholding for aiding diagnosis. Most multilevel thresholding techniques are based on histograms. It is susceptible to the limitation of grayscale spatial distribution and is difficult to be used for MR images with variable and complex morphology. In this paper, a novel multilevel thresholding segmentation approach with a non‐histogram using a modified threshold score (MTS) is proposed. An opposition‐based learning hybrid rice optimization (OHRO) algorithm is used to reduce the computational cost of MTS for the purpose of optimizing the threshold search. The strategy of opposition‐based learning expands the space of feasible solutions and avoids the search from stalling. The proposed approach is evaluated through the Harvard Medical School's whole brain atlas dataset. Comparing the results with TS‐OHRO, Tsallis‐OHRO, Kapur‐OHRO, and Masi‐OHRO, MTS‐OHRO achieves better quantitative and qualitative outcomes which demonstrate that the application of MTS‐OHRO to MR images is effective and feasible.

Segmentation on remote sensing imagery for atmospheric air pollution using divergent differential evolution algorithm.

Air pollution is a global issue causing major health hazards. By proper monitoring of air quality, actions can be taken to control air pollution. Satellite remote sensing is an effective way to monitor global atmosphere. Various sensors and instruments fitted to satellites and airplanes are used to obtain the radar images. These images are quite complex with various wavelength differentiated by very close color differences. Clustering of such images based on its wavelengths can provide the much-needed relief in better understanding of these complex images. Such task related to image segmentation is a universal optimization issue that can be resolved with evolutionary techniques. Differential Evolution (DE) is a fairly fast and operative parallel search algorithm. Though classical DE algorithm is popular, there is a need for varying the mutation strategy for enhancing the performance for varied applications. Several alternatives of classical DE are considered by altering the trial vector and control parameter. In this work, a new alteration of DE technique labeled as DiDE (Divergent Differential Evolution Algorithm) is anticipated. The outcomes of this algorithm were tested and verified with the traditional DE techniques using fifteen benchmark functions. The new variant DiDE exhibited much superior outcomes compared to traditional approaches. The novel approach was then applied on remote sensing imagery collected form TEMIS, a web based service for atmospheric satellite images and the image was segmented. Fuzzy Tsallis entropy method of multi-level thresholding technique is applied over DiDE to develop image segmentation. The outcomes obtained were related with the segmented results using traditional DE and the outcome attained was found to be improved profoundly. Experimental results illustrate that by acquainting DiDE in multilevel thresholding, the computational delay was greatly condensed and the image quality was significantly improved.

Multilevel segmentation of 2D and volumetric medical images using hybrid Coronavirus Optimization Algorithm

Medical image segmentation is a crucial step in Computer-Aided Diagnosis systems, where accurate segmentation is vital for perfect disease diagnoses. This paper proposes a multilevel thresholding technique for 2D and 3D medical image segmentation using Otsu and Kapur's entropy methods as fitness functions to determine the optimum threshold values. The proposed algorithm applies the hybridization concept between the recent Coronavirus Optimization Algorithm (COVIDOA) and Harris Hawks Optimization Algorithm (HHOA) to benefit from both algorithms' strengths and overcome their limitations. The improved performance of the proposed algorithm over COVIDOA and HHOA algorithms is demonstrated by solving 5 test problems from IEEE CEC 2019 benchmark problems. Medical image segmentation is tested using two groups of images, including 2D medical images and volumetric (3D) medical images, to demonstrate its superior performance. The utilized test images are from different modalities such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and X-ray images. The proposed algorithm is compared with seven well-known metaheuristic algorithms, where the performance is evaluated using four different metrics, including the best fitness values, Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Normalized Correlation Coefficient (NCC). The experimental results demonstrate the superior performance of the proposed algorithm in terms of convergence to the global optimum and making a good balance between exploration and exploitation properties. Moreover, the quality of the segmented images using the proposed algorithm at different threshold levels is better than the other methods according to PSNR, SSIM, and NCC values. Additionally, the Wilcoxon rank-sum test is conducted to prove the statistical significance of the proposed algorithm.

Multi-level Kapur’s thresholding using whale optimization and social group optimization for brain MRI image segmentation

Image thresholding is the vital part for segmentation of a region in image. A fresh technique of multilevel thresholding based on the whale optimizer and social group optimizer are in fact recommended in this particular paper. New Optimization techniques inspired are actually affected as a result of the interpersonal as well as the backpacking behaviour of all of the whales. The process of multi-level thresholding problem is applied through the Kapur’s entropy and metaheuristic algorithms. The suggested strategy is tried on brain MRI test image set. The suggested methods are next in contrast with particle swarm optimization (PSO) and differential evaluation (DE) constructed multilevel thresholding techniques and other optimization techniques in the literature. The segmentation really calculated with evaluation measures like Sensitivity, Specificity, and Dice Score (DS).

Multilayer Reversible Data Hiding Based on the Difference Expansion Method Using Multilevel Thresholding of Host Images Based on the Slime Mould Algorithm

Researchers have scrutinized data hiding schemes in recent years. Data hiding in standard images works well, but does not provide satisfactory results in distortion-sensitive medical, military, or forensic images. This is because placing data in an image can cause permanent distortion after data mining. Therefore, a reversible data hiding (RDH) technique is required. One of the well-known designs of RDH is the difference expansion (DE) method. In the DE-based RDH method, finding spaces that create less distortion in the marked image is a significant challenge, and has a high insertion capacity. Therefore, the smaller the difference between the selected pixels and the more correlation between two consecutive pixels, the less distortion can be achieved in the image after embedding the secret data. This paper proposes a multilayer RDH method using the multilevel thresholding technique to reduce the difference value in pixels and increase the visual quality and the embedding capacity. Optimization algorithms are one of the most popular methods for solving NP-hard problems. The slime mould algorithm (SMA) gives good results in finding the best solutions to optimization problems. In the proposed method, the SMA is applied to the host image for optimal multilevel thresholding of the image pixels. Moreover, the image pixels in different and more similar areas of the image are located next to one another in a group and classified using the specified thresholds. As a result, the embedding capacity in each class can increase by reducing the value of the difference between two consecutive pixels, and the distortion of the marked image can decrease after inserting the personal data using the DE method. Experimental results show that the proposed method is better than comparable methods regarding the degree of distortion, quality of the marked image, and insertion capacity.

Intelligent Deep-Learning-Enabled Decision-Making Medical System for Pancreatic Tumor Classification on CT Images.

Decision-making medical systems (DMS) refer to the design of decision techniques in the healthcare sector. They involve a procedure of employing ideas and decisions related to certain processes such as data acquisition, processing, judgment, and conclusion. Pancreatic cancer is a lethal type of cancer, and its prediction is ineffective with current techniques. Automated detection and classification of pancreatic tumors can be provided by the computer-aided diagnosis (CAD) model using radiological images such as computed tomography (CT) and magnetic resonance imaging (MRI). The recently developed machine learning (ML) and deep learning (DL) models can be utilized for the automated and timely detection of pancreatic cancer. In light of this, this article introduces an intelligent deep-learning-enabled decision-making medical system for pancreatic tumor classification (IDLDMS-PTC) using CT images. The major intention of the IDLDMS-PTC technique is to examine the CT images for the existence of pancreatic tumors. The IDLDMS-PTC model derives an emperor penguin optimizer (EPO) with multilevel thresholding (EPO-MLT) technique for pancreatic tumor segmentation. Additionally, the MobileNet model is applied as a feature extractor with optimal auto encoder (AE) for pancreatic tumor classification. In order to optimally adjust the weight and bias values of the AE technique, the multileader optimization (MLO) technique is utilized. The design of the EPO algorithm for optimal threshold selection and the MLO algorithm for parameter tuning shows the novelty. A wide range of simulations was executed on benchmark datasets, and the outcomes reported the promising performance of the IDLDMS-PTC model on the existing methods.

Iris Recognition Based on Multilevel Thresholding Technique and Modified Fuzzy c-Means Algorithm

Iris Recognition Based on Multilevel Thresholding Technique and Modified Fuzzy c-Means Algorithm

Intelligent Deep Learning Based Multi-Retinal Disease Diagnosis and Classification Framework

In past decades, retinal diseases have become more common and affect people of all age grounds over the globe. For examining retinal eye disease, an artificial intelligence (AI) based multilabel classification model is needed for automated diagnosis. To analyze the retinal malady, the system proposes a multiclass and multi-label arrangement method. Therefore, the classification frameworks based on features are explicitly described by ophthalmologists under the application of domain knowledge, which tends to be time-consuming, vulnerable generalization ability, and unfeasible in massive datasets. Therefore, the automated diagnosis of multi-retinal diseases becomes essential, which can be solved by the deep learning (DL) models. With this motivation, this paper presents an intelligent deep learning-based multi-retinal disease diagnosis (IDL-MRDD) framework using fundus images. The proposed model aims to classify the color fundus images into different classes namely AMD, DR, Glaucoma, Hypertensive Retinopathy, Normal, Others, and Pathological Myopia. Besides, the artificial flora algorithm with Shannon’s function (AFA-SF) based multi-level thresholding technique is employed for image segmentation and thereby the infected regions can be properly detected. In addition, SqueezeNet based feature extractor is employed to generate a collection of feature vectors. Finally, the stacked sparse Autoencoder (SSAE) model is applied as a classifier to distinguish the input images into distinct retinal diseases. The efficacy of the IDL-MRDD technique is carried out on a benchmark multi-retinal disease dataset, comprising data instances from different classes. The experimental values pointed out the superior outcome over the existing techniques with the maximum accuracy of 0.963.

Automated quantification of COVID-19 pneumonia severity in chest CT using histogram-based multi-level thresholding segmentation

BackgroundChest computed tomography (CT) has proven its critical importance in detection, grading, and follow-up of lung affection in COVID-19 pneumonia. There is a close relationship between clinical severity and the extent of lung CT findings in this potentially fatal disease. The extent of lung lesions in CT is an important indicator of risk stratification in COVID-19 pneumonia patients. This study aims to explore automated histogram-based quantification of lung affection in COVID-19 pneumonia in volumetric computed tomography (CT) images in comparison to conventional semi-quantitative severity scoring. This retrospective study enrolled 153 patients with proven COVID-19 pneumonia. Based on the severity of clinical presentation, the patients were divided into three groups: mild, moderate and severe. Based upon the need for oxygenation support, two groups were identified as follows: common group that incorporated mild and moderate severity patients who did not need intubation, and severe illness group that included patients who were intubated. An automated multi-level thresholding histogram-based quantitative analysis technique was used for evaluation of lung affection in CT scans together with the conventional semi-quantitative severity scoring performed by two expert radiologists. The quantitative assessment included volumes, percentages and densities of ground-glass opacities (GGOs) and consolidation in both lungs. The results of the two evaluation methods were compared, and the quantification metrics were correlated.ResultsThe Spearman’s correlation coefficient between the semi-quantitative severity scoring and automated quantification methods was 0.934 (p < 0.0001).ConclusionsThe automated histogram-based quantification of COVID-19 pneumonia shows good correlation with conventional severity scoring. The quantitative imaging metrics show high correlation with the clinical severity of the disease.

Remote Sensing Imagery Segmentation: A Hybrid Approach

In remote sensing imagery, segmentation techniques fail to encounter multiple regions of interest due to challenges such as dense features, low illumination, uncertainties, and noise. Consequently, exploiting vast and redundant information makes segmentation a difficult task. Existing multilevel thresholding techniques achieve low segmentation accuracy with high temporal difficulty due to the absence of spatial information. To mitigate this issue, this paper presents a new Rényi’s entropy and modified cuckoo search-based robust automatic multi-thresholding algorithm for remote sensing image analysis. In the proposed method, the modified cuckoo search algorithm is combined with Rényi’s entropy thresholding criteria to determine optimal thresholds. In the modified cuckoo search algorithm, the Lévy flight step size was modified to improve the convergence rate. An experimental analysis was conducted to validate the proposed method, both qualitatively and quantitatively against existing metaheuristic-based thresholding methods. To do this, the performance of the proposed method was intensively examined on high-dimensional remote sensing imageries. Moreover, numerical parameter analysis is presented to compare the segmented results against the gray-level co-occurrence matrix, Otsu energy curve, minimum cross entropy, and Rényi’s entropy-based thresholding. Experiments demonstrated that the proposed approach is effective and successful in attaining accurate segmentation with low time complexity.

An entropy minimization based multilevel colour thresholding technique for analysis of breast thermograms using equilibrium slime mould algorithm

Breast cancer is one of the leading causes of death in women due to the abnormal growth of cells known as a tumour. Thermography is an imaging technique used to capture infrared radiation and generate a thermogram. The major advantages of thermography are contactless, radiation-free, painless, and real-time screening. The thermogram is used further in a pathological investigation. However, in the modern-day, machine intelligence technologies are used for the analysis of breast thermograms, which assist the expert in decision making. The machine intelligence technique requires a thresholding image during the preprocessing stage, which warrants an efficient thresholding method. Here, we investigate the multilevel thresholding objective function to minimize the information regarding the entropic dependence on various classes. A new Equilibrium Slime Mould Algorithm (ESMA) is proposed for colour image thresholding, an improvement of the Slime Mould Algorithm (SMA), by integrating the equilibrium practice in updating the slime mould positions from an equilibrium pool concept of Equilibrium Optimizer (EO). The ESMA performance is compared with well know optimization algorithms and ranked one based on Friedman’s mean rank, when evaluated using 53 test problems. Further, the ESMA is used for the development of an entropy minimization based multilevel colour thresholding method for the analysis of breast thermograms. It is applied in two sets of experiments using grey components and the RGB components of breast thermograms. The encouraging results on the thermogram image analysis are presented. Even more interesting results are seen while evaluating our proposal in terms of different metrics—the peak signal to noise ratio (PSNR), the feature similarity (FSIM), and the structure similarity (SSIM). Statistical a nalysis provided reveals the suitability of the technique for the analysis of breast thermograms. The method may assist the medical practitioners, as an additional tool. The ESMA may be useful for solving different optimization problems in the world of engineering.

Segmentation of WM, GM, and CSF from the Brain MRIs Using an Advanced Metaheuristic Approach

Segmentation of brain tissues from the Magnetic Resonance Image (MRI) is vital to detect and diagnose brain-related diseases. Precise brain MRI segmentation is challenging because of the unpredictable anatomical structure of the brain, existence of Intensity Inhomogeneity (IIH), Partial Volume (PV) effects, and noise. Most of the brain disorders can be identified exactly by the segmentation of brain tissues, such as White Matter (WM), Gray Matter (GM), and Cerebrospinal Fluid (CSF). The optimization algorithmic approach is widely used for the brain image segmentation. In this work, a robust metaheuristic Multi-level Thresholding (MT) technique named as Advanced Wind Driven Optimization (AWDO) is employed for the brain MRI segmentation. The optimal MT values can be attained by combining the AWDO with the very popular Otsu’s objective function. The results obtained by the proposed AWDO in segmenting various brain images at an optimized value of threshold are compared with the groundtruths and have given the high values for the metrics such as sensitivity, specificity, segmentation accuracy, dice coefficient, and Precision. The values of the objective function, Peak Signal to Noise Ratio (PSNR), Standard Deviation (SD), and Convergence time of the proposed method are also being obtained at various threshold levels. The results are compared with the results of some existing algorithms like Multiplicative Intrinsic Component Optimization (MICO), Electromagnetism Optimization (EMO), Histogram based Darwinian Particle Swarm Optimization (HDPSO), Fully Convolutional Neural Networks (FCNN), and Advance Ant Colony Optimization (AACO). The results justified the notable performance of the proposed approach in segmenting different brain tissues.

Breast DCE-MRI segmentation for lesion detection by multi-level thresholding using student psychological based optimization

In recent years, the high prevalence of breast cancer in women has risen dramatically. Therefore, segmentation of breast Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) is a necessary task to assist the radiologist inaccurate diagnosis and detection of breast cancer in breast DCE-MRI. For image segmentation, thresholding is a simple and effective method. In breast DCE-MRI analysis for lesion detection and segmentation, radiologists agree that optimization via multi-level thresholding technique is important to differentiate breast lesions from dynamic DCE-MRI. In this paper, multi-level thresholding using Student Psychology-Based Optimizer (SPBO) is proposed to segment the breast DCE-MR images for lesion detection. First, MR images are denoised using the anisotropic diffusion filter and then, Intensity Inhomogeneities (IIHs) are corrected in the preprocessing step. The preprocessed MR images are segmented using the SPBO algorithm. Finally, the lesions are extracted from the segmented images and localized in the original MR images. The proposed method is applied on 300 Sagittal T2-Weighted DCE-MRI slices of 50 patients, histologically proven, and analyzed. The proposed method is compared with algorithms such as Particle Swarm Optimizer (PSO), Dragonfly Optimization (DA), Slime Mould Optimization (SMA), Multi-Verse Optimization (MVO), Grasshopper Optimization Algorithm (GOA), Hidden Markov Random Field (HMRF), Improved Markov Random Field (IMRF), and Conventional Markov Random Field (CMRF) methods. The high accuracy level of 99.44%, sensitivity 96.84%, and Dice Similarity Coefficient (DSC) 93.41% are achieved using the proposed automatic segmentation method. Both quantitative and qualitative results demonstrate that the proposed method performs better than the eight compared methods.

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.