Efficient image denoising method based on mathematical morphology reconstruction and the Non-Local Means filter for the MRI of the head

Hassani, Abdelhamid El; Majda, Aicha

doi:10.1109/cist.2016.7805084

Cited by 8 publications

(7 citation statements)

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“…To remove these points without affecting the segmentation results, we use a de-noising method based on morphological filters. Compared with other de-noising methods like the spherical coordinates system [25], the noise standard deviation (SD) estimation method [26], and the multi-wavelet transform method [27], the morphological filters have a better performance in dealing with details [28]. We now introduce the principle of this method.…”

Section: De-noise Operation Based On Morphological Filtersmentioning

confidence: 99%

Recognition of Wheat Spike from Field Based Phenotype Platform Using Multi-Sensor Fusion and Improved Maximum Entropy Segmentation Algorithms

et al. 2018

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Abstract:To obtain an accurate count of wheat spikes, which is crucial for estimating yield, this paper proposes a new algorithm that uses computer vision to achieve this goal from an image. First, a home-built semi-autonomous multi-sensor field-based phenotype platform (FPP) is used to obtain orthographic images of wheat plots at the filling stage. The data acquisition system of the FPP provides high-definition RGB images and multispectral images of the corresponding quadrats. Then, the high-definition panchromatic images are obtained by fusion of three channels of RGB. The Gram-Schmidt fusion algorithm is then used to fuse these multispectral and panchromatic images, thereby improving the color identification degree of the targets. Next, the maximum entropy segmentation method is used to do the coarse-segmentation. The threshold of this method is determined by a firefly algorithm based on chaos theory (FACT), and then a morphological filter is used to de-noise the coarse-segmentation results. Finally, morphological reconstruction theory is applied to segment the adhesive part of the de-noised image and realize the fine-segmentation of the image. The computer-generated counting results for the wheat plots, using independent regional statistical function in Matlab R2017b software, are then compared with field measurements which indicate that the proposed method provides a more accurate count of wheat spikes when compared with other traditional fusion and segmentation methods mentioned in this paper.

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Section: De-noise Operation Based On Morphological Filtersmentioning

confidence: 99%

Recognition of Wheat Spike from Field Based Phenotype Platform Using Multi-Sensor Fusion and Improved Maximum Entropy Segmentation Algorithms

et al. 2018

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“…Magnetic resonance imaging (MRI) is a powerful medical imaging modality used to produce detailed images of soft tissues and anatomical body structures that can be visualized non-invasively at the millimeter scale [ 1 , 2 ]. MRI processing provides detailed quantitative brain analysis for accurate disease diagnosis [ 3 , 4 ] (i.e., brain tumor diagnosis [ 5 ], Alzheimer’s disease (AD), Parkinson’s disease, multiple sclerosis [ 6 ], dementia, schizophrenia, brain disorder identification and whole brain analysis of traumatic injury), detection, treatment planning and classification of abnormalities (i.e., extracting tissues like white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF)) [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

“…In clinical evaluation and neuroscience research, MRI images are often corrupted by several artifact sources, such as intensity inhomogeneity, abnormal tissues with heterogeneous signal intensities, non-ideal hardware characteristics and the poor choice of scanning parameters [ 2 , 3 , 7 ]. In order to improve the quality of noisy MRI images to facilitate clinical diagnosis, the MRI pre-processing operations are introduced to improve the qualities of other MRI applications such as segmentation [ 8 ], detection [ 9 ] and classification [ 2 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

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Local Complexity Estimation Based Filtering Method in Wavelet Domain for Magnetic Resonance Imaging Denoising

Orea-Flores

Gallegos‐Funes

Arellano-Reynoso

2019

Entropy

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In this paper, we propose the local complexity estimation based filtering method in wavelet domain for MRI (magnetic resonance imaging) denoising. A threshold selection methodology is proposed in which the edge and detail preservation properties for each pixel are determined by the local complexity of the input image. In the proposed filtering method, the current wavelet kernel is compared with a threshold to identify the signal- or noise-dominant pixels in a scale providing a good visual quality avoiding blurred and over smoothened processed images. We present a comparative performance analysis with different wavelets to find the optimal wavelet for MRI denoising. Numerical experiments and visual results in simulated MR images degraded with Rician noise demonstrate that the proposed algorithm consistently outperforms other denoising methods by balancing the tradeoff between noise suppression and fine detail preservation. The proposed algorithm can enhance the contrast between regions allowing the delineation of the regions of interest between different textures or tissues in the processed images. The proposed approach produces a satisfactory result in the case of real MRI denoising by balancing the detail preservation and noise removal, by enhancing the contrast between the regions of the image. Additionally, the proposed algorithm is compared with other approaches in the case of Additive White Gaussian Noise (AWGN) using standard images to demonstrate that the proposed approach does not need to be adapted specifically to Rician or AWGN noise; it is an advantage of the proposed approach in comparison with other methods. Finally, the proposed scheme is simple, efficient and feasible for MRI denoising.

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“…Uma técnica de filtragem de ruído amplamente utilizadaé o filtro da mediana [Pitas and Venetsanopoulos 1992]. Esse filtro tenta restaurar o valor da intensidade do pixel corrompido calculando a mediana dos valores das intensidades dos pixels vizinhos [El Hassani and Majda 2016]. Entretanto, esse tipo de filtragem não apenas reduz o ruído presente como também danifica detalhes estruturais [Feng et al 2014].…”

Section: Introductionunclassified

Método Baseado em Morfologia Matemática e BM3D para Redução de Ruı́do em Imagens Dentais de TC de Baixa Radiação

Stringhini

Welfer

2019

Anais Do Simpósio Brasileiro De Computação Aplicada À Saúde (SBCAS 2019)

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O impacto em reduzir a dose de radiação em exames de tomogra- fia computadorizada (TC) está diretamente relacionado à qualidade das ima- gens. Tais imagens são degradadas por artefatos indesejáveis, conhecidos como ruı́do. Diante disso, para melhorar a qualidade destas imagens e fornecer um diagnóstico médico preciso, é necessário aplicar técnicas que sejam capazes de reduzir ruı́do. Neste artigo, é proposto um método para filtrar ruı́do em imagens dentais de TC de baixa radiação, utilizando operadores da morfologia matemática e filtragem Block-Matching 3D (BM3D). Os resultados do método proposto foram comparados com diversos filtros existentes e validados utili- zando as métricas PSNR, SSIM e MSE. Através de diversos experimentos, o método proposto demonstrou performance superior aos filtros analisados, re- duzindo ruı́do e preservando detalhes de forma mais satisfatória.

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Efficient image denoising method based on mathematical morphology reconstruction and the Non-Local Means filter for the MRI of the head

Cited by 8 publications

References 10 publications

Recognition of Wheat Spike from Field Based Phenotype Platform Using Multi-Sensor Fusion and Improved Maximum Entropy Segmentation Algorithms

Recognition of Wheat Spike from Field Based Phenotype Platform Using Multi-Sensor Fusion and Improved Maximum Entropy Segmentation Algorithms

Local Complexity Estimation Based Filtering Method in Wavelet Domain for Magnetic Resonance Imaging Denoising

Método Baseado em Morfologia Matemática e BM3D para Redução de Ruı́do em Imagens Dentais de TC de Baixa Radiação

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