Level set method coupled with Energy Image features for brain MR image segmentation

Punga, Mirela Visan; Gaurav, Rahul; Moraru, Luminița

doi:10.1515/bmt-2013-0111

Cited by 10 publications

(9 citation statements)

References 17 publications

(28 reference statements)

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“…This work considered the BRATS 2013 dataset and achieved 88% for Dice coefficient. Further, the brain tumor examination procedures can also be found in [24][25][26][27][28][29][30][31][32].…”

Section: Related Earlier Workmentioning

confidence: 99%

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Social-Group-Optimization based tumor evaluation tool for clinical brain MRI of Flair/diffusion-weighted modality

Dey

Rajinikanth

Shi

et al. 2019

Biocybernetics and Biomedical Engineering

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Brain tumor is one of the harsh diseases among human community and is usually diagnosed with medical imaging procedures. Computed-Tomography (CT) and Magnetic-Resonance-Image (MRI) are the regularly used non-invasive methods to acquire brain abnormalities for medical study. Due to its importance, a significant quantity of image assessment and decision-making procedures exist in literature. This article proposes a two-stage image assessment tool to examine brain MR images acquired using the Flair and DW modalities. The combination of the Social-Group-Optimization (SGO) and Shannon's-Entropy (SE) supported multi-thresholding is implemented to pre-processing the input images. The image post-processing includes several procedures, such as Active Contour (AC), Watershed and region-growing segmentation, to extract the tumor section. Finally, a classifier system is implemented using ANFIS to categorize the tumor under analysis into benign and malignant. Experimental investigation was executed using benchmark datasets, like ISLES and BRATS, and also clinical MR images obtained with Flair/DW modality. The outcome of this study confirms that AC offers enhanced results compared with other segmentation procedures considered in this article. The ANFIS classifier obtained an accuracy of 94.51% on the used ISLES and real clinical images.

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Section: Related Earlier Workmentioning

confidence: 99%

“…In this work, commonly used ROI mining procedures, such as AC [30], MCWS [48] and SRG [37] approaches were implemented to extract the abnormal section from brain MRI of Flair/DW modality.…”

Section: Post-processingmentioning

confidence: 99%

Social-Group-Optimization based tumor evaluation tool for clinical brain MRI of Flair/diffusion-weighted modality

Dey

Rajinikanth

Shi

et al. 2019

Biocybernetics and Biomedical Engineering

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“…To remove residual anatomical differences, an in-house software written in MATLAB for the brain tissues segmentation for skull and background was used [14,15]. Each DTI datasets were aligned using the midline inter-hemispheric fissure to compensate for individual differences in the brain.…”

Section: Image Processingmentioning

confidence: 99%

Magnetic Field Gradients and Their Effects on the Diffusion Tensor Derivate Measures

Moraru¹,

Dimitrievici²,

Ene³

et al. 2017

RAD Conference Proceedings

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Abstract. Diffusion tensor imaging (DTI) and the degree of diffusion weighting of the sequence, expressed as the bfactor, are used to investigate the effect of the magnetic field gradients on the integrity of white matter in patients with temporal intracerebral hemorrhage. The healthy patients are the gold standard. The present study investigated the changes of mean diffusivity (MD) and fractional anisotropy (FA) in a brain hemisphere approach to understand the effect of magnetic field gradients on the brain hemorrhage investigation. The artifacts induced by diffusion gradients in diffusion tensor imaging affect the accuracy of the investigation, and in order to achieve the optimal image quality, strong magnetic field gradients are recommended. The artifact effect of higher magnetic field gradients is analyzed by means of the root-mean-square FA and MD difference between left and right brain hemispheres.

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“…Many skull-stripping algorithms were developed, and extensive work was done in this area, but a standardized solution has not been proposed yet [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. Automatic and manual segmentations are the process of partitioning the image into distinct regions.…”

Section: Introductionmentioning

confidence: 99%

“…Each of the existing skull-stripping methods has its weaknesses and strengths, and this is the reason why they have not yet been adopted in the clinical environment. Today, fully or partially automatic segmentation methods are, in general, accepted, but their outcomes strongly depend on the theoretical and computation models [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Robust Skull-Stripping Segmentation Based on Irrational Mask for Magnetic Resonance Brain Images

2015

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This paper proposes a new method for simple, efficient, and robust removal of the non-brain tissues in MR images based on an irrational mask for filtration within a binary morphological operation framework. The proposed skull-stripping segmentation is based on two irrational 3 × 3 and 5 × 5 masks, having the sum of its weights equal to the transcendental number π value provided by the Gregory-Leibniz infinite series. It allows maintaining a lower rate of useful pixel loss. The proposed method has been tested in two ways. First, it has been validated as a binary method by comparing and contrasting with Otsu's, Sauvola's, Niblack's, and Bernsen's binary methods. Secondly, its accuracy has been verified against three state-of-the-art skull-stripping methods: the graph cuts method, the method based on Chan-Vese active contour model, and the simplex mesh and histogram analysis skull stripping. The performance of the proposed method has been assessed using the Dice scores, overlap and extra fractions, and sensitivity and specificity as statistical methods. The gold standard has been provided by two neurologist experts. The proposed method has been tested and validated on 26 image series which contain 216 images from two publicly available databases: the Whole Brain Atlas and the Internet Brain Segmentation Repository that include a highly variable sample population (with reference to age, sex, healthy/diseased). The approach performs accurately on both standardized databases. The main advantage of the proposed method is its robustness and speed.

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Level set method coupled with Energy Image features for brain MR image segmentation

Cited by 10 publications

References 17 publications

Social-Group-Optimization based tumor evaluation tool for clinical brain MRI of Flair/diffusion-weighted modality

Social-Group-Optimization based tumor evaluation tool for clinical brain MRI of Flair/diffusion-weighted modality

Magnetic Field Gradients and Their Effects on the Diffusion Tensor Derivate Measures

Robust Skull-Stripping Segmentation Based on Irrational Mask for Magnetic Resonance Brain Images

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