In this paper, we combine decision fusion methods with four meta-heuristic algorithms (Particle Swarm Optimization (PSO) algorithm, Cuckoo search algorithm, modification of Cuckoo Search (CS McCulloch) algorithm and Genetic algorithm) in order to improve the image segmentation. The proposed technique based on fusing the data from Particle Swarm Optimization (PSO), Cuckoo search, modification of Cuckoo Search (CS McCulloch) and Genetic algorithms are obtained for improving magnetic resonance images (MRIs) segmentation. Four algorithms are used to compute the accuracy of each method while the outputs are passed to fusion methods. In order to obtain parts of the points that determine similar membership values, we apply the different rules of incorporation for these groups. The proposed approach is applied to challenging applications: MRI images, gray matter/white matter of brain segmentations and original black/white images Behavior of the proposed algorithm is provided by applying to different medical images. It is shown that the proposed method gives accurate results; due to the decision fusion produces the greatest improvement in classification accuracy.

Medical images are increasingly used in healthcare for diagnosis, medication preparation, treatment directing, and disease progression tracking. Medical imaging primarily processes data that is unclear, incomplete, ambiguous, complementary, conflicting, overlapping, contradictory, skewed, and has a clear structural character. One of the general ways to understand an image is to match the previously stored models with the features and features that were extracted from the image. In order to obtain a high-level model, full knowledge of the objects to be modeled and their relationship to each other, how to use the information obtained in the model, and the best time to use it. Magnetic resonance imaging (MRI) offers detailed three-dimensional (3D) details on the anatomy of human soft tissues [

In recent years, applications that use the morphologic contents of MRI have often required segmentation of the image volume into tissue types due to the wealth of anatomy knowledge provided by MRI [

To deal with the complexity of MRI structure, hybrid techniques that combine more than one method are required [

The aim of image segmentation is to divide pixels into two or more classes based on their intensity levels and a threshold value. The procedure used to pick the threshold determines the accuracy of the segmentation. To determine the best multilevel threshold of image segmentation, various optimization techniques have been developed. the behaviour of birds flocking in search of food is a machine-learning technique [

In this paper, we introduce a technique based on combining these four different meta-heuristic algorithms with decision fusion to produce the greatest improvement in classification accuracy. This technique starts by partitioning the given source MRI image into several segments using multi-level threshold technique. Then we combine the result of 4 algorithms with decision fusion rule like Max, Min, Mean, Median and Product rules. As a result, image segmentation and fusion output estimation remain an open problem that needs further attention from the scientific community. Furthermore, results from experiments show that our algorithm can generate better image segments than some popular approaches.

The rest of the paper organized as follows. A brief review of the algorithms and fusion methods are presented in Section 2. The presented technique described in Section 3. In Section 4, the experimental results are presented. Our conclusion is presented in Section 5.

The most commonly used radiographic techniques are known as computed tomography (CT), magnetic resonance Imaging (MRI) and Positron Emission Tomography (PET). PET scan images display the internal formation of tumours and cancer cells by using the metabolism of the body parts, while other imaging methods such as CT and MRI only show the physiology of the body parts. Labeling pixels in 2D and 3D images is called segmentation. Typically, what is meant by regions is to divide the image into parts. It is essential to map 3D visualization and specific structures in medical imaging. There are different techniques for image segmentation, one of the most attractive methods that are called intelligent methods. In this section we will focused on Particle Swarm Optimization (PSO), Cuckoo search, modification of Cuckoo Search (CS McCulloch) and Genetic algorithms because it is more stable and efficiency.

The PSO primarily edges from the principle of swarm intelligence, which could be a property of a system that enables undeveloped agents to communicate locally with their environment and create coherent global career patterns [

A fitness function is employed to check particle success at every step of the algorithm. To model the swarm, every particle n moves during a multidimensional space according to position

The coefficients w, _{1}, _{2} and _{3} determine the weights that affect inertia, the local best, the global best and the neighborhood best when determining the new velocity, respectively. Typically, the inertial influence is set to a value slightly less than 1. _{1}, r_{2} and r_{3} are random vectors with every component generally a uniform random number between 0 and 1. Rather than multiplying the same random component with every particle’s velocity dimension, the aim is to multiply a new random component per velocity dimension.

The particles within the PSO are evaluated for the fitness function, that is defined as the between-class variance

First, we set the particle velocities to zero, the choice of sites depends on the search area, and this area depends on the density levels, Particles are spread between 0 and 255 if the frames are 8-bit images, as shown in

Given the nature of the problem, the local neighborhood and global bests are initialized with the worst possible values. Population size and stopping conditions are the other factors that need to be tweaked. To get an overall successful solution in a reasonable amount of time, Population size should be improved. The implicit connection between particles is explained by PSO (similar to broadcasting) through updating neighborhood and global knowledge, which affects the velocity and consequent location of particles. Stopping criteria may be a predefined number of iterations without having better results or other criteria, depending on the issue. Due to the implementation of random multipliers, there is also a stochastic exploration effect (r_{1}, r_{2} and r_{3}). Many applications of the PSO have been successful, including robotics [

Cuckoo Search (CS) is a recent heuristic algorithm taken from the parasitic behavior of some cuckoo species’ obligatory brooding when laying their eggs in host bird nests. Some cuckoos will imitate the colours and patterns of eggs from a limited number of host species. Egg abandonment is less likely as a result of this. If the host bird notices unusual eggs. They either abandon the eggs or discard them. The parasitic cuckoo selects a nest where the host bird’s eggs will be laid. Cuckoo eggs hatch before their hosts’ eggs, and when they do, the host’s eggs are pushed out of the nests. As a result, cuckoo chicks get a lot of food and occasionally imitate the host chicks’ voice to get more food [

The local random walk is given by:
_{best} is the immediate best solution. The random step length via Lévy flight is considered due to more efficiency of Lévy flights in exploring the search space and is drawn from a Lévy distribution having infinite variance and mean.

Since Lévy flights produce a portion of new solutions, the local quest accelerates. Any of the solutions should be produced using far field randomization to avoid the system being stuck in a local optimum., Γ (λ) is the gamma function, p is the switch probability, ε is a random number and (1 < λ ≤ 3). Due to large scale randomization, the step length in cuckoo search is heavy-tailed, and any large step is probable.

The following algorithm [

By merging McCulloch’s method, this is a modification of the standard Cuckoo Search (CS) algorithm [

J.H McCulloch [

The skewness _{z}

To avert overflow,

Special cases:

Gaussian case,

^{2}(α has no effect) respectively

Cauchy case, β = 1

Genetic algorithms use natural choice, as discovered by Charles Darwin [

After you’ve built your segmentation, you’ll need to figure out how to combine their outputs effectively. Fusion is usually used wherever several methods are needed to perform at the data or decision level [

In this research, we focus on fusion method using equivalent architectures. The problem can be explained as follows. The target to evaluate each image point y to one class label β_{i} out of D class labels α = {w_{1}, w_{2},…, w_{D}}. To complete this job, a set of R segmentation approach may be consulted. The output of each approach can be one of two kinds:

Hard label, which W_{i} € α. b) Soft label, D element vector W_{i} = [w_{i,1}, w_{i,2},…, w_{i,D}]^{T} which represent the supports to the D classes. Some segmentation approaches produce hard outputs so we need to convert from hard label to soft label. This establish by using Gaussian membership function. The Gaussian membership function is a popular technique that defines how each point in the input space is mapped to a membership value. The mathematical tractability given by the following equation:

_{i}

We have a tendency to use the fusion techniques to combine the results of the 4 meta-heuristic algorithms that we used in MRI segmentation in order to improve the segmentation accuracy. There are many decision fusion approaches for each kind of outputs. In this paper, we will use many of them, namely the median, maximum, minimum, mean and output rules for soft outputs. The simple fusion rules (Max, Min, Sum, Product, and Median) acquire the system output by operating every column of DP(X) [

Max rule is

The Max rule takes the maximum of each DP(X) column as the fused output C(X).

Min rule is

The Min rule takes the minimum of each DP(X) column as the fused output C(X)

Sum rule is

The Sum rule computes the sum of each DP(X) column as the fused output C(X). It is conjointly known as the mean rule once it computes the mean; these are simply two forms of the same rule.

Product rule is

The output rule calculate the outputs of each DP(X) column as the fused output C(X).

Median rule is

The average rule computes the median of each DP(X) column as the fused output C(X). If l is an even number, then the mean of two medians is taken as the result of a column.

We propose an approach based on integrating the fusion concepts into the segmentation process. The architecture of multiple classifier systems is used to categories them. The serial suite and the parallel suite are the two basic groups in this regard [

The target of this paper is to introduce a new strategy for improving segmentation accuracy that is focused on decision fusion. Instead of focusing on a single approach, this new paradigm considers a variety of them. It consists of a series of segmentation methods that are used in parallel in its most common form. After that, a fusion module blends the decisions of the different methods. In this case, the individual strategies are capable of working independently and concurrently. A comparative analysis is also presented to show whether one fusion process outperforms the others in terms of segmentation. Several advances are discussed here, including how to improve the accuracy of multiple segmentation methods using fusion methods, as well as how to test these methods when applied to different data sets (medical and non-medical data).

The introduced algorithm begins with dividing the source image into several segments using 4 different meta-heuristic algorithms, Particle Swarm Optimization (PSO)algorithm, Cuckoo search algorithm, CS McCulloch algorithm and Genetic algorithm. The result of the segmentation methods are the input of decision fusion techniques to improvement the MRI segmentation accuracy. The purpose of the fusion step is to combine each result of segmentation method to produce a segmentation accuracy better than the accuracy of each method taken separately (as shown in

Image fusion is a method for combining a multispectral image with high spectral resolution but low spatial resolution with a panchromatic image with high spatial resolution but low spectral resolution. The merged image should have more information than the multispectral and panchromatic images combined. Image fusion is more practical and cost-effective than developing an advanced sensor that meets all resolution requirements. Image fusion can be done at three different stages, depending on the stage of the fusion process: pixel, function, and decision [

Several data sets are used for experiments, where the first experiment consists of simple synthetic images with image size 256 × 256 pixels and corrupted by 9% salt and pepper noise. The another group T1-weighted MR data with sliver density of 1 mm, no intensity inhomogeneities and 3% noise, with 258 × 258 image size, we got it from McGill University’s classic brain simulation.

We present an approach based on combining 4 different meta-heuristic algorithms with decision fusion to produce the greatest improvement in classification accuracy. This method starts with partitioning the given source MRI image into many segments. Then we combine the result of four algorithms with decision fusion rule like Max, Min, Mean, Median and Product rules.

The segmentation method relies heavily on the consistency of the segmentation algorithm. Every algorithm’s comparison score S is proposed in [_{ref} denotes the reference segmented image’s set of pixels belonging to the same class (ground truth).

We use 4 different meta-heuristic algorithms, Particle Swarm Optimization (PSO) algorithm, Cuckoo search algorithm, CS McCulloch algorithm and Genetic algorithm. Every method from these segmentation methods applied in practice to different images with different sizes; every method was applied on original black/white images and MRI images, as in

Segmentation methods | Segmented image | Accuracy |
---|---|---|

Swarm | 93.57% | |

CSMC_otsu | 93.51% | |

Cuckoo Search CSMC_kapur | 93.36% | |

Genetic | 93.24% |

Fusion method | Fusion image | Accuracy |
---|---|---|

Max | 95.21% | |

Min | 95.09% | |

Mean | 95.34% | |

Median | 95.07% | |

Product | 95.43% |

Segmentation methods | Brain2 | Brain3 | ||
---|---|---|---|---|

Segmented image | Accuracy | Segmented image | Accuracy | |

swarm | 44.69% | 85.04% | ||

CSMC_otsu | 46.19% | 87.39% | ||

Cuckoo Search CSMC_kapur | 45.42% | 87.33% | ||

Genetic | 57.12% | 89.54% |

Fusion method | Brain2 | Brain3 | ||
---|---|---|---|---|

Fusion image | Accuracy | Fusion image | Accuracy | |

Max | 47.98% | 90.42% | ||

Min | 59.90% | 88.56% | ||

Mean | 62.49% | 93.04% | ||

Median | 62.32% | 95.14% | ||

Product | 60.31% | 89.63% |

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In this research, we present a strategy that combines four segmentation algorithms using fusion modules to improve when compared to utilize each method independently. The Particle Swarm Optimization (PSO) algorithm, Cuckoo search algorithm, CS McCulloch algorithm and Genetic algorithm are used to partition the provided image into various segments. The proposed approach was tested with MRIs data then implemented in Matlab. The proposed median, maximum, minimum, mean, and product have demonstrated to be higher robustness to segment many different types of brain images data. The segmentation accuracy proved that median and mean fusion techniques preserves more spectral information as compared with max, min and product Image fusion techniques. It is easier to determine the contributions of these components in the fused image and select the best fusion method that meets the user’s needs using the fusion methods used in this analysis. As seen in

The authors would like to thanks Taif University Researchers for Supporting Project number (TURSP-2020/214), Taif University, Taif Saudi Arabia.