Magnetic Resonance Brain Imaging Segmentation Based on Cascaded Fractional-Order Darwinian Particle Swarm Optimization and Mean Shift Clustering

Ali, Hesham A.; Elmogy, Mohammed; El-Daydamony, Eman; Atwan, Ahmed; Soliman, Hassan

doi:10.1007/978-3-319-33793-7_3

Cited by 5 publications

(6 citation statements)

References 28 publications

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“…In our work, we compare the results of multilevel segmentation obtained by our proposed approach with the Darwinian PSO (DPSO) and Fractional‐order DPSO (FODPSO) method . All these methods have been implemented on the MATLAB environment.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…In our work, we compare the results of multilevel segmentation obtained by our proposed approach with the Darwinian PSO (DPSO) and Fractional-order DPSO (FODPSO) method. 22,23 All these methods have been implemented on the MATLAB environment. This experiment was carried out on a T1-weighted image with contrast enhancement (T1-C), T2-weighted image, and T2-weighted image with FLAIR with 158 × 160 × 130 voxels.…”

Section: Experiments and Resultsmentioning

confidence: 99%

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3D brain tumor segmentation in MRI images based on a modified PSO technique

Gtifa

Hamdaoui

Sakly

2019

Int J Imaging Syst Tech

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Three‐dimensional (3D) brain tumor segmentation is a clinical requirement for brain tumor diagnosis and radiotherapy planning. This is a challenging task due to variation in type, size, location, and shape of tumors. Several methods such as particle swarm optimization (PSO) algorithm formed a topological relationship for the slices that converts 2D images into 3D magnetic resonance imaging (MRI) images which does not provide accurate results and they depend on the number of input sections, positions, and the shape of the MRI images. In this article, we propose an efficient 3D brain tumor segmentation technique called modified particle swarm optimization. Also, segmentation results are compared with Darwinian particle swarm optimization (DPSO) and fractional‐order Darwinian particle swarm optimization (FODPSO) approaches. The experimental results show that our method succeeded 3D segmentation with 97.6% of accuracy rate more efficient if compared with the DPSO and FODPSO methods with 78.1% and 70.21% for the case of T1‐C modality.

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Experiments and Resultsmentioning

confidence: 99%

3D brain tumor segmentation in MRI images based on a modified PSO technique

Gtifa

Hamdaoui

Sakly

2019

Int J Imaging Syst Tech

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“…In the path of PSO evolution, the next existence was the fractional‐order particle swarm Darwinian optimisation (FO DPSO) proposed by Couceiro et al 32 . where a fractional computation is implemented to control the convergence rate of this algorithm.…”

Section: Methodsmentioning

confidence: 99%

Automated brain tumour segmentation from multi‐modality magnetic resonance imaging data based on new particle swarm optimisation segmentation method

Gtifa

Hamdaoui

Sakly

2022

Robotics Computer Surgery

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Background: Segmentation of brain tumours is a complex problem in medical image processing and analysis. It is a time-consuming and error-prone task. Therefore, computer-aided detection systems need to be developed to decrease physicians' workload and improve the accuracy of segmentation. Methods:This paper proposes a level set method constrained by an intuitive artificial intelligence-based approach to perform brain tumour segmentation. By studying 3D brain tumour images, a new segmentation technique based on the Modified Particle Swarm Optimisation (MPSO), Darwin Particle Swarm Optimisation (DPSO), and Fractional Order Darwinian Particle Swarm Optimisation (FOD-PSO) algorithms were developed. Results: The introduced technique was verified according to the MICCAI RASTS 2013 database for high-grade glioma patients. The three algorithms were evaluated using different performance measures: accuracy, sensitivity, specificity, and Dice similarity coefficient to prove the performance and robustness of our 3D segmentation technique. Conclusion:The result is that the MPSO algorithm consistently outperforms the DPSO and FO DPSO.

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“…In extension, a multi-level thresholding based on FODPSO was proposed in [12] where the results were proved to be in favour of FODPSO. In literature [13], magnetic resonance brain image segmentation based on FODPSO was proposed and it grabbed an accuracy of 99.45%, whereas DPSO achieved only 97.08%. The concept of the fractional differential with a fractional coefficient σ ∈ C of a general signal x (t) proposed by Grunwald-Letnikov definition is represented as follows…”

Section: ) Fractional Order Darwinian Particle Swarm Optimization (Fodpso)mentioning

confidence: 99%

“…Ali et al [13] coupled Fractional-Order Darwinian Particle Swarm Optimization and Mean Shift Clustering algorithm for MRI Brain image segmentation and evaluated by metrics like Jaccard coefficient and accuracy, better results were produced when compared with Fuzzy C Means Clustering (FCM), Mean Shift (MS), PSO, and DPSO techniques. In [14], multithresholding based on various optimization algorithms have been analysed, Brownian heuristic optimization generates efficient results.…”

Section: Introductionmentioning

confidence: 99%

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

Ahilan¹,

et al. 2019

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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.

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Magnetic Resonance Brain Imaging Segmentation Based on Cascaded Fractional-Order Darwinian Particle Swarm Optimization and Mean Shift Clustering

Cited by 5 publications

References 28 publications

3D brain tumor segmentation in MRI images based on a modified PSO technique

3D brain tumor segmentation in MRI images based on a modified PSO technique

Automated brain tumour segmentation from multi‐modality magnetic resonance imaging data based on new particle swarm optimisation segmentation method

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

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