Improving the segmentation of magnetic resonance brain images using the LSHADE optimization algorithm

Exploring a novel HE image segmentation technique for glioblastoma: A hybrid slime mould and differential evolution approach

Glioblastoma is a primary brain tumor with high incidence and mortality rates, posing a significant threat to human health. It is crucial to provide necessary diagnostic assistance for its management. Among them, Multi-threshold Image Segmentation (MIS) is considered the most efficient and intuitive method in image processing. In recent years, many scholars have combined different metaheuristic algorithms with MIS to improve the quality of Image Segmentation (IS). Slime Mould Algorithm (SMA) is a metaheuristic approach inspired by the foraging behavior of slime mould populations in nature. In this investigation, we introduce a hybridized variant named BDSMA, aimed at overcoming the inherent limitations of the original algorithm. These limitations encompass inadequate exploitation capacity and a tendency to converge prematurely towards local optima when dealing with complex multidimensional problems. To bolster the algorithm's optimization prowess, we integrate the original algorithm with a robust exploitative operator called Differential Evolution (DE). Additionally, we introduce a strategy for handling solutions that surpass boundaries. The incorporation of an advanced cooperative mixing model accelerates the convergence of BDSMA, refining its precision and preventing it from becoming trapped in local optima. To substantiate the effectiveness of our proposed approach, we conduct a comprehensive series of comparative experiments involving 30 benchmark functions. The results of these experiments demonstrate the superiority of our method in terms of both convergence speed and precision. Moreover, within this study, we propose a MIS technique. This technique is subsequently employed to conduct experiments on IS at both low and high threshold levels. The effectiveness of the BDSMA-based MIS technique is further showcased through its successful application to the medical image of brain glioblastoma. The evaluation of these experimental outcomes, utilizing image quality metrics, conclusively underscores the exceptional efficacy of the algorithm we have put forth.

A review on brain tumor segmentation based on deep learning methods with federated learning techniques

Brain tumors have become a severe medical complication in recent years due to their high fatality rate. Radiologists segment the tumor manually, which is time-consuming, error-prone, and expensive. In recent years, automated segmentation based on deep learning has demonstrated promising results in solving computer vision problems such as image classification and segmentation. Brain tumor segmentation has recently become a prevalent task in medical imaging to determine the tumor location, size, and shape using automated methods. Many researchers have worked on various machine and deep learning approaches to determine the most optimal solution using the convolutional methodology. In this review paper, we discuss the most effective segmentation techniques based on the datasets that are widely used and publicly available. We also proposed a survey of federated learning methodologies to enhance global segmentation performance and ensure privacy. A comprehensive literature review is suggested after studying more than 100 papers to generalize the most recent techniques in segmentation and multi-modality information. Finally, we concentrated on unsolved problems in brain tumor segmentation and a client-based federated model training strategy. Based on this review, future researchers will understand the optimal solution path to solve these issues.

Multi-threshold image segmentation using an enhanced fruit fly optimization for COVID-19 X-ray images

Since the outbreak of COVID-19, it has seriously endangered the health of human beings. Computer automatic segmentation of COVID-19 X-ray images is an important means to assist doctors in rapid and accurate diagnosis. Therefore, this paper proposes a modified FOA (EEFOA) with two optimization strategies added to the original FOA, including elite natural evolution (ENE) and elite random mutation (ERM). To be specific, ENE and ERM can effectively speed up the convergence and deal with the problem of local optima, respectively. The outstanding performance of EEFOA was confirmed by experimental results comparing EEFOA with the original FOA, other FOA variants, and advanced algorithms at CEC2014. After that, EEFOA is implemented for multi-threshold image segmentation (MIS) of COVID-19 X-ray images, where a 2D histogram consisting of the original greyscale image and the non-local means image is used to represent the image information, and Rényi's entropy is used as the objective function to find the maximum value. The evaluation results of the MIS segmentation experiments show that, whether high or low threshold, EEFOA can achieve higher quality segmentation results and greater robustness than other advanced segmentation methods.

An enhanced ant colony optimizer with Cauchy-Gaussian fusion and novel movement strategy for multi-threshold COVID-19 X-ray image segmentation

The novel coronavirus pneumonia (COVID-19) is a respiratory disease of great concern in terms of its dissemination and severity, for which X-ray imaging-based diagnosis is one of the effective complementary diagnostic methods. It is essential to be able to separate and identify lesions from their pathology images regardless of the computer-aided diagnosis techniques. Therefore, image segmentation in the pre-processing stage of COVID-19 pathology images would be more helpful for effective analysis. In this paper, to achieve highly effective pre-processing of COVID-19 pathological images by using multi-threshold image segmentation (MIS), an enhanced version of ant colony optimization for continuous domains (MGACO) is first proposed. In MGACO, not only a new move strategy is introduced, but also the Cauchy-Gaussian fusion strategy is incorporated. It has been accelerated in terms of convergence speed and has significantly enhanced its ability to jump out of the local optimum. Furthermore, an MIS method (MGACO-MIS) based on MGACO is developed, where it applies the non-local means, 2D histogram as the basis, and employs 2D Kapur’s entropy as the fitness function. To demonstrate the performance of MGACO, we qualitatively analyze it in detail and compare it with other peers on 30 benchmark functions from IEEE CEC2014, which proves that it has a stronger capability of solving problems over the original ant colony optimization for continuous domains. To verify the segmentation effect of MGACO-MIS, we conducted a comparison experiment with eight other similar segmentation methods based on real pathology images of COVID-19 at different threshold levels. The final evaluation and analysis results fully demonstrate that the developed MGACO-MIS is sufficient to obtain high-quality segmentation results in the COVID-19 image segmentation and has stronger adaptability to different threshold levels than other methods. Therefore, it has been well-proven that MGACO is an excellent swarm intelligence optimization algorithm, and MGACO-MIS is also an excellent segmentation method.

Robust lockwire segmentation with multiscale boundary-driven regional stability

Lockwire segmentation plays a vital role in ensuring mechanical safety in industrial fields. Aiming at the missed detection problem encountered in blurred and low-contrast situations, we propose a robust lockwire segmentation method based on multiscale boundary-driven regional stability. We first design a novel multiscale boundary-driven stability criterion to generate a blur-robustness stability map. Then, the curvilinear structure enhancement metric and linearity measurement function are defined to compute the likeliness of stable regions to belong to lockwires. Finally, the closed boundaries of lockwires are determined to achieve accurate segmentation. Experimental results demonstrate that our proposed method outperforms state-of-the-art object segmentation methods.

Boosted crow search algorithm for handling multi-threshold image problems with application to X-ray images of COVID-19

COVID-19 is pervasive and threatens the safety of people around the world. Therefore, now, a method is needed to diagnose COVID-19 accurately. The identification of COVID-19 by X-ray images is a common method. The target area is extracted from the X-ray images by image segmentation to improve classification efficiency and help doctors make a diagnosis. In this paper, we propose an improved crow search algorithm (CSA) based on variable neighborhood descent (VND) and information exchange mutation (IEM) strategies, called VMCSA. The original CSA quickly falls into the local optimum, and the possibility of finding the best solution is significantly reduced. Therefore, to help the algorithm avoid falling into local optimality and improve the global search capability of the algorithm, we introduce VND and IEM into CSA. Comparative experiments are conducted at CEC2014 and CEC'21 to demonstrate the better performance of the proposed algorithm in optimization. We also apply the proposed algorithm to multi-level thresholding image segmentation using Renyi's entropy as the objective function to find the optimal threshold, where we construct 2-D histograms with grayscale images and non-local mean images and maximize the Renyi's entropy on top of the 2-D histogram. The proposed segmentation method is evaluated on X-ray images of COVID-19 and compared with some algorithms. VMCSA has a significant advantage in segmentation results and obtains better robustness than other algorithms. The available extra info can be found at https://github.com/1234zsw/VMCSA.

A modified reptile search algorithm for global optimization and image segmentation: Case study brain MRI images

In this paper, we proposed an enhanced reptile search algorithm (RSA) for global optimization and selected optimal thresholding values for multilevel image segmentation. RSA is a recent metaheuristic optimization algorithm depending on the hunting behavior of crocodiles. RSA is inclined to inadequate diversity, local optima, and unbalanced exploitation abilities as other metaheuristic algorithms. The RUNge Kutta optimizer (RUN) is a novel metaheuristic algorithm that has demonstrated effectiveness in solving real-world optimization problems. The enhanced solution quality (ESQ) in RUN utilizes the thus-far best solution to promote the quality of solutions, improve the convergence speed, and effectively balance the exploration and exploitation steps. Also, the Scale factor (SF) has a randomized adaptation nature, which helps RUN in further improving the exploration and exploitation steps. This parameter ensures a smooth transition from exploration to exploitation. In order to mitigate the drawbacks of the RSA algorithm, this paper proposed a modified RSA (mRSA), which combines the RSA algorithm with the RUN. The ESQ mechanism and the scale factor boost the original RSA's performance, enhance convergence speed, bypass local optimum, and enhance the balance between exploitation and exploration. The validity of mRSA was verified using two experimental sequences. First, we applied mRSA to CEC'2020 benchmark functions of various types and dimensions, showing that mRSA has more robust search capabilities than the original RSA and popular counterpart algorithms concerning statistical, convergence, and diversity measurements. The second experiment evaluated mRSA for a real-world application to solve magnetic resonance imaging (MRI) brain image segmentation. Overall experimental results confirm that the mRSA has a strong optimization ability. Also, mRSA method is a more successful multilevel thresholding segmentation and outperforms comparison methods according to different performance measures.

Segmentation of Infant Brain Using Nonnegative Matrix Factorization

This study develops an atlas-based automated framework for segmenting infants’ brains from magnetic resonance imaging (MRI). For the accurate segmentation of different structures of an infant’s brain at the isointense age (6–12 months), our framework integrates features of diffusion tensor imaging (DTI) (e.g., the fractional anisotropy (FA)). A brain diffusion tensor (DT) image and its region map are considered samples of a Markov–Gibbs random field (MGRF) that jointly models visual appearance, shape, and spatial homogeneity of a goal structure. The visual appearance is modeled with an empirical distribution of the probability of the DTI features, fused by their nonnegative matrix factorization (NMF) and allocation to data clusters. Projecting an initial high-dimensional feature space onto a low-dimensional space of the significant fused features with the NMF allows for better separation of the goal structure and its background. The cluster centers in the latter space are determined at the training stage by the K-means clustering. In order to adapt to large infant brain inhomogeneities and segment the brain images more accurately, appearance descriptors of both the first-order and second-order are taken into account in the fused NMF feature space. Additionally, a second-order MGRF model is used to describe the appearance based on the voxel intensities and their pairwise spatial dependencies. An adaptive shape prior that is spatially variant is constructed from a training set of co-aligned images, forming an atlas database. Moreover, the spatial homogeneity of the shape is described with a spatially uniform 3D MGRF of the second-order for region labels. In vivo experiments on nine infant datasets showed promising results in terms of the accuracy, which was computed using three metrics: the 95-percentile modified Hausdorff distance (MHD), the Dice similarity coefficient (DSC), and the absolute volume difference (AVD). Both the quantitative and visual assessments confirm that integrating the proposed NMF-fused DTI feature and intensity MGRF models of visual appearance, the adaptive shape prior, and the shape homogeneity MGRF model is promising in segmenting the infant brain DTI.

Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review

Multilevel Thresholding (MLT) is considered as a significant and imperative research field in image segmentation that can efficiently resolve difficulties aroused while analyzing the segmented regions of multifaceted images with complicated nonlinear conditions. MLT being a simple exponential combinatorial optimization problem is commonly phrased by means of a sophisticated objective function requirement that can only be addressed by nondeterministic approaches. Consequently, researchers are engaging Nature-Inspired Optimization Algorithms (NIOA) as an alternate methodology that can be widely employed for resolving problems related to MLT. This paper delivers an acquainted review related to novel NIOA shaped lately in last three years (2019-2021) highlighting and exploring the major challenges encountered during the development of image multi-thresholding models based on NIOA.

Performance optimization of differential evolution with slime mould algorithm for multilevel breast cancer image segmentation

Breast cancer is one of the most dangerous diseases for women's health, and it is imperative to provide the necessary diagnostic assistance for it. The medical image processing technology is one of the most critical of all complementary diagnostic technologies. Image segmentation is the core step of image processing, where multilevel image segmentation is considered one of the most efficient and straightforward methods. Many multilevel image segmentation methods based on evolutionary and population-based methods have been proposed in recent years, but many have the fatal weakness of poor convergence accuracy and the tendency to fall into local optimum. Therefore, to overcome these weaknesses, this paper proposes a modified differential evolution (MDE) algorithm with a vision based on the slime mould foraging behavior, where the recently proposed slime mould algorithm (SMA) inspires it. Besides, to obtain high-quality breast cancer image segmentation results, this paper also develops an excellent MDE-based multilevel image segmentation model, the core of which is based on non-local means 2D histogram and 2D Kapur's entropy. To effectively validate the performance of the proposed method, a comparison experiment between MDE and its similar algorithms was first carried out on IEEE CEC 2014. Then, an initial validation of the MDE-based multilevel image segmentation model was performed by utilizing a reference image set. Finally, the MDE-based multilevel image segmentation model was compared with peers using breast invasive ductal carcinoma images. A series of experimental results have proved that MDE is an evolutionary algorithm with high convergence accuracy and the ability to jump out of the local optimum, as well as effectively demonstrated that the developed model is a high-quality segmentation method that can provide practical support for further research of breast invasive ductal carcinoma pathological image processing.

Brain Image Segmentation in Recent Years: A Narrative Review

Brain image segmentation is one of the most time-consuming and challenging procedures in a clinical environment. Recently, a drastic increase in the number of brain disorders has been noted. This has indirectly led to an increased demand for automated brain segmentation solutions to assist medical experts in early diagnosis and treatment interventions. This paper aims to present a critical review of the recent trend in segmentation and classification methods for brain magnetic resonance images. Various segmentation methods ranging from simple intensity-based to high-level segmentation approaches such as machine learning, metaheuristic, deep learning, and hybridization are included in the present review. Common issues, advantages, and disadvantages of brain image segmentation methods are also discussed to provide a better understanding of the strengths and limitations of existing methods. From this review, it is found that deep learning-based and hybrid-based metaheuristic approaches are more efficient for the reliable segmentation of brain tumors. However, these methods fall behind in terms of computation and memory complexity.

Multilevel threshold image segmentation with diffusion association slime mould algorithm and Renyi's entropy for chronic obstructive pulmonary disease

Image segmentation is an essential pre-processing step and is an indispensable part of image analysis. This paper proposes Renyi's entropy multi-threshold image segmentation based on an improved Slime Mould Algorithm (DASMA). First, we introduce the diffusion mechanism (DM) into the original SMA to increase the population's diversity so that the variants can better avoid falling into local optima. The association strategy (AS) is then added to help the algorithm find the optimal solution faster. Finally, the proposed algorithm is applied to Renyi's entropy multilevel threshold image segmentation based on non-local means 2D histogram. The proposed method's effectiveness is demonstrated on the Berkeley segmentation dataset and benchmark (BSD) by comparing it with some well-known algorithms. The DASMA-based multilevel threshold segmentation technique is also successfully applied to the CT image segmentation of chronic obstructive pulmonary disease (COPD). The experimental results are evaluated by image quality metrics, which show the proposed algorithm's extraordinary performance. This means that it can help doctors analyze the lesion tissue qualitatively and quantitatively, improve its diagnostic accuracy and make the right treatment plan. The supplementary material and info about this article will be available at https://aliasgharheidari.com.

An efficient retinal blood vessel segmentation in eye fundus images by using optimized top-hat and homomorphic filtering

BACKGROUND AND OBJECTIVE

Automatic segmentation of retinal blood vessels makes a major contribution in CADx of various ophthalmic and cardiovascular diseases. A procedure to segment thin and thick retinal vessels is essential for medical analysis and diagnosis of related diseases. In this article, a novel methodology for robust vessel segmentation is proposed, handling the existing challenges presented in the literature.

METHODS

The proposed methodology consists of three stages, pre-processing, main processing, and post-processing. The first stage consists of applying filters for image smoothing. The main processing stage is divided into two configurations, the first to segment thick vessels through the new optimized top-hat, homomorphic filtering, and median filter. Then, the second configuration is used to segment thin vessels using the proposed optimized top-hat, homomorphic filtering, matched filter, and segmentation using the MCET-HHO multilevel algorithm. Finally, morphological image operations are carried out in the post-processing stage.

RESULTS

The proposed approach was assessed by using two publicly available databases (DRIVE and STARE) through three performance metrics: specificity, sensitivity, and accuracy. Analyzing the obtained results, an average of 0.9860, 0.7578 and 0.9667 were respectively achieved for DRIVE dataset and 0.9836, 0.7474 and 0.9580 for STARE dataset.

CONCLUSIONS

The numerical results obtained by the proposed technique, achieve competitive average values with the up-to-date techniques. The proposed approach outperform all leading unsupervised methods discussed in terms of specificity and accuracy. In addition, it outperforms most of the state-of-the-art supervised methods without the computational cost associated with these algorithms. Detailed visual analysis has shown that a more precise segmentation of thin vessels was possible with the proposed approach when compared with other procedures.

Improved manta ray foraging optimization for multi-level thresholding using COVID-19 CT images

Coronavirus disease 2019 (COVID-19) is pervasive worldwide, posing a high risk to people's safety and health. Many algorithms were developed to identify COVID-19. One way of identifying COVID-19 is by computed tomography (CT) images. Some segmentation methods are proposed to extract regions of interest from COVID-19 CT images to improve the classification. In this paper, an efficient version of the recent manta ray foraging optimization (MRFO) algorithm is proposed based on the oppositionbased learning called the MRFO-OBL algorithm. The original MRFO algorithm can stagnate in local optima and requires further exploration with adequate exploitation. Thus, to improve the population variety in the search space, we applied Opposition-based learning (OBL) in the MRFO's initialization step. MRFO-OBL algorithm can solve the image segmentation problem using multilevel thresholding. The proposed MRFO-OBL is evaluated using Otsu's method over the COVID-19 CT images and compared with six meta-heuristic algorithms: sine-cosine algorithm, moth flame optimization, equilibrium optimization, whale optimization algorithm, slap swarm algorithm, and original MRFO algorithm. MRFO-OBL obtained useful and accurate results in quality, consistency, and evaluation matrices, such as peak signal-to-noise ratio and structural similarity index. Eventually, MRFO-OBL obtained more robustness for the segmentation than all other algorithms compared. The experimental results demonstrate that the proposed method outperforms the original MRFO and the other compared algorithms under Otsu's method for all the used metrics.