Particle Swarm Optimization and Two-Way Fixed-Effects Analysis of Variance for Efficient Brain Tumor Segmentation (original) (raw)
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A Survey on Brain Image Segmentation Using Particle Swarm Optimization
Journal of emerging technologies and innovative research, 2019
Machine learning (ML) has gained enormous application with innovation in hardware requirements for computing. The application of computer vision techniques in health care has one of the aim to reduce human judgment in diagnosis. Thus, human error in judgment may be reduced. One of the primary diagnostic and treatment evaluation tools for interpretation has been magnetic resonance imaging (MRI). In fact MRI characteristics will help the doctor to avoid the human error in manual interpretation of medical content where the smallest aberrances in the human body can be identified. More preferred contrast information about brain tissues is provided by Magnetic Resonance imaging (MRI). MR images can also be used to determine normal and abnormal types of brain. Brain related diagnosis demands at most care and a minute error in judgment may be disastrous. This makes medical imaging very important field. Various imaging methods like CT Scans, X-Ray, and MRI are available but MRI is the most r...
Soft Computing in Industrial Applications, 2014
In this paper, a segmentation system with a modified automatic Seeded Region Growing (SRG) based on Particle Swarm Optimization (PSO) image clustering will be presented. The paper is focused on Magnetic Resonance Imaging (MRI) breast tumour segmentation. The PSO clusters’ intensities are involved in the proposed algorithms of the automated SRG initial seed and threshold value selection. Prior to that, some pre-processing methodologies are involved. And breast skin is detected and deleted using the integration of two algorithms, i.e. Level Set Active Contour and Morphological Thinning. The system is applied and tested on the RIDER breast MRI dataset, and the results are evaluated and presented in comparison to the Ground Truths of the dataset. The results show higher performance compared to the previous segmentation approaches that have been tested on the same dataset.
Magnetic Resonance Image segmentation is an important image analysis task in medical image processing for diagnosis of diseases. Brain MRI segmentation is done for the proper diagnosis of lesions. In this paper, a new segmentation method using partitional clustering algorithm with Grammatical Swarm Based-Adaptable Particle Swarm Optimizer is proposed for lesion detection of brain MR images. Difficulty in use of segmentation occurs due to presence of noise in the M-R images. Therefore, noise is removed using non-local means filter. After segmentation of T2-weighted MR images using the proposed clustering method, lesions are extracted from the MR images. A comparative study has been made with PSO based method using quantitative measurement indices. The experimental results show that proposed method performs better than PSO based method.
Evaluation of Particle Swarm Optimisation for Medical Image Segmentation
Evaluation of Particle Swarm Optimisation for Medical Image Segmentation, 2016
Otsu's criteria is a popular image segmentation approach that selects a threshold to maximise the inter-class variance of the distribution of intensity levels in the image. The algorithm finds the optimum threshold by performing an exhaustive search, but this is time-consuming, particularly for medical images employing 16-bit quantisa-tion. This paper investigates particle swarm optimisation (PSO), Dar-winian PSO and Fractional Order Darwinian PSO to speed up the algorithm. We evaluate the algorithms in medical imaging applications concerned with volume reconstruction, with a particular focus on addressing artefacts due to immobilisation masks, commonly worn by patients undergoing radiotherapy treatment for head-and-neck cancer. We find that the Fractional-Order Darwinian PSO algorithm outperforms other PSO algorithms in terms of accuracy, stability and speed which makes it the favourite choice when the accuracy and time-of-execution are a concern.
Hyper-heuristical Particle Swarm Method for MR Images Segmentation
2018
An important factor in the recognition of magnetic resonance images is not only the accuracy, but also the speed of the segmentation procedure. In some cases, the speed of the procedure is more important than the accuracy and the choice is made in favor of a less accurate, but faster procedure. This means that the segmentation method must be fully adaptive to different image models, that reduces its accuracy. These requirements are satisfied by developed hyper-heuristical particle swarm method for image segmentation. The main idea of the proposed hyper-heuristical method is the application of several heuristics, each of which has its strengths and weaknesses, and then their use depending on the current state of the solution. Hyper-heuristical particle swarm segmentation method is a management system, in the subordination of which there are three bioinspired heuristics: PSO-K-means, Modified Exponential PSO, Elitist Exponential PSO. Developed hyper-heuristical method was tested using...