A Hybrid Approach of Using Particle Swarm Optimization and Volumetric Active Contour without Edge for Segmenting Brain Tumors in MRI Scan

Hasan, Ali M.

doi:10.11591/ijeei.v6i3.592

Cited by 10 publications

(12 citation statements)

References 27 publications

(44 reference statements)

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“…Regularly, the competence of sophisticated image processing techniques [1]- [4], such as super resolution [5], remote sensing [6], and medical imaging [7]- [9], are definitely susceptible from noise thereupon noise suppressing technique [10]- [20] are become an irresistible momentous process. Theoretically, the noise suppressing technique regularly constructs the undesirable effect such as blurring effect or detail losing thereupon the fundamental intention of noise suppressing technique is for concealing noise from noisy photograph whereas protecting detail.…”

Section: Liturature Reviewmentioning

confidence: 99%

A computational experimental of noise suppressing technique stand on hard decision threshold dissimilarity

Patanavijit

Thakulsukanant

2021

IJEECS

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Due to the extreme insistence for digital image processing, plentiful modern noise suppressing techniques are embodied of dissimilarity process and suppressing process. One of the extreme capability dissimilarity is hard decision threshold (HDT) dissimilarity, which has been recently declared in 2012, for suppressing the impulsive noisy photographs thus the computer experimental statement attempts to investigate the capability of the noise suppressing technique that is stand on HDT dissimilarity for the processed photographs, which are corrupted by fixed-intensity impulse noise (FIIN). This paper proposes the noise suppressing technique stand on HDT dissimilarity for FIIN. There are 3 primary contributions of this paper. The first contribution is the statistical average of the HDT dissimilarity of noise-free elements, which are computed from plentiful ground-truth photographs by varying window size for the best HDT window size. The second contribution is the statistical average of the HDT dissimilarity of corrupted elements, which are computed from plentiful corrupted photographs by varying outlier density for the best HDT window size. The final contribution is the statistical interrelation of the capability of the noise suppressing technique and hard consistent of HDT dissimilarity are investigated by varying the outlier denseness for the best HDT hard consistence.

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Section: Liturature Reviewmentioning

confidence: 99%

A computational experimental of noise suppressing technique stand on hard decision threshold dissimilarity

Patanavijit

Thakulsukanant

2021

IJEECS

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“…Then PSO is performed to identify the pathological regions followed by contour without edge method. The method showed an accuracy of 92% in comparison to the manual description [15]. Karegowda et al in 2018 evaluated various image segmentation approaches for precise identification of tumor region in MRI scans.…”

Section: Eai Endorsed Transactions On Pervasive Health and Technologymentioning

confidence: 99%

Enhanced Brain Tumour MRI Segmentation using K-means with machine learning based PSO and Firefly Algorithm

Kapoor¹,

Agarwal²

2021

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: Medical image segmentation is usually integrated as a critical step in medical image analysis, often associated with numerous clinical applications. Magnetic Resonance Imaging (MRI) provides detailed visualization of the various anatomical structures decisive for interventions and surgical plans. OBJECTIVES: The objective of this paper is to design and apply an enhanced brain tumor MRI segmentation using Kmean with K-means as machine learning based Particle Swarm Optimization (PSO) and Firefly Algorithm (FA). METHODS: A novel fitness function of Swarm Based PSO works on velocity variation is introduced, which enhances the segmented regions. The traditional k-means algorithm is enhanced by applying PSO to the segmented part. Another extension of Swarm Intelligence named Firefly is applied to compare the results of the PSO based segmentation, and Firefly based segmentation is used. RESULTS: The simulation results are evaluated in terms of precision (98%), recall (0.95), f-measure (0.96), accuracy (97%), and segmentation time (2.63s) to measure the image segmentation the quality of main results obtained. CONCLUSION: Comparative studies have shown that the proposed design using k-means combined with FA exhibited high accuracy and precision in detecting brain tumor RoI.

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“…Prior to subjecting individual slices of MRI scans to any type of statistical analysis, a set of pre-processing algorithms are commonly implemented to reduce the impact of random variations in intensity of MRI slices and noise that may result from patient motion, respiration, anxiety or from the scanner itself. Generally, image preprocessing includes image enhancement; MRI slices resizing, which is essentially needed when the images are collected from different MRI scanners; as well as the intensity normalization, which is used to reduce the impact of intra-scan and inter-scan variations [2], [25]- [27]. Moreover, sometimes mid-sagittal plane detection and correction (MSP) is required and considered as a prior step for estimating the tumor detection.…”

Section: A Mri Scan Preprocessingmentioning

confidence: 99%

Combining Deep and Handcrafted Image Features for MRI Brain Scan Classification

et al. 2019

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Progresses in the areas of artificial intelligence, machine learning, and medical imaging technologies have allowed the development of the medical image processing field with some astonishing results in the last two decades. These innovations enabled the clinicians to view the human body in highresolution or three-dimensional cross-sectional slices, which resulted in an increase in the accuracy of the diagnosis and the examination of patients in a non-invasive manner. The fundamental step for magnetic resonance imaging (MRI) brain scans classifiers is their ability to extract meaningful features. As a result, many works have proposed different methods for features extraction to classify the abnormal growths in the brain MRI scans. More recently, the application of deep learning algorithms to medical imaging leads to impressive performance enhancements in classifying and diagnosing complicated pathologies, such as brain tumors. In this paper, a deep learning feature extraction algorithm is proposed to extract the relevant features from MRI brain scans. In parallel, handcrafted features are extracted using the modified gray level co-occurrence matrix (MGLCM) method. Subsequently, the extracted relevant features are combined with handcrafted features to improve the classification process of MRI brain scans with support vector machine (SVM) used as the classifier. The obtained results proved that the combination of the deep learning approach and the handcrafted features extracted by MGLCM improves the accuracy of classification of the SVM classifier up to 99.30%.

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A Hybrid Approach of Using Particle Swarm Optimization and Volumetric Active Contour without Edge for Segmenting Brain Tumors in MRI Scan

Cited by 10 publications

References 27 publications

A computational experimental of noise suppressing technique stand on hard decision threshold dissimilarity

A computational experimental of noise suppressing technique stand on hard decision threshold dissimilarity

Enhanced Brain Tumour MRI Segmentation using K-means with machine learning based PSO and Firefly Algorithm

Combining Deep and Handcrafted Image Features for MRI Brain Scan Classification

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