A Novel empirical mode decomposition based system for medical image enhancement

Bakhtiari, Somayeh; Agaian, Sos S.; Jamshidi, Mo

doi:10.1109/syscon.2011.5929111

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…Qin et al [20] used BEMD to decompose the medical images and the high frequency information was then expanded to obtain the enhanced images. Çelebi et al [21] utilized BEMD to separately decompose the color channels of underwater color images into IMFs; then, the weighted IMFs of each channel were combined to reconstruct the enhanced images [22]. Other image enhancement methods based on ensemble EMD [23] and fast and adaptive BEMD [24] were developed by Bakhtiari et al in [22] and [25], respectively.…”

Section: Introductionmentioning

confidence: 99%

“…Çelebi et al [21] utilized BEMD to separately decompose the color channels of underwater color images into IMFs; then, the weighted IMFs of each channel were combined to reconstruct the enhanced images [22]. Other image enhancement methods based on ensemble EMD [23] and fast and adaptive BEMD [24] were developed by Bakhtiari et al in [22] and [25], respectively. In this study, a novel method which is a combination of BEMD and PCA fusion technique [26] is proposed to enhance the thermal images of machine conditions.…”

Section: Introductionmentioning

confidence: 99%

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Thermal image enhancement using bi-dimensional empirical mode decomposition in combination with relevance vector machine for rotating machinery fault diagnosis

Tran

Yang

et al. 2013

Mechanical Systems and Signal Processing

111

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In this study, a novel fault diagnosis system for rotating machinery using thermal imaging is proposed. This system consists of bi-dimensional empirical mode decomposition (BEMD) for image enhancement, a generalized discriminant analysis (GDA) for feature reduction, and a relevance vector machine (RVM) for fault classification. Firstly, the thermal image obtained from machine conditions is decomposed into intrinsic mode functions (IMFs) by using BEMD.At each decomposed level, the IMF is expanded and fused with the residue by grey-scale transformation and principal component analysis fusion technique, respectively. The enhanced image is then formed by the improved IMFs in reconstruction process. Subsequently, feature extraction is applied for the enhanced images to obtain histogram features which characterize the thermal image and contain useful information for diagnosis. The high dimensionality of the achieved feature set can be reduced by GDA implementation. Moreover, GDA also assists in the increase of the feature cluster separation. Finally, the diagnostic results are performed by RVM.The proposed system is applied and validated with the thermal images of a fault simulator. A comparative study of the classification results obtained from RVM, support vector machines, and adaptive neuro-fuzzy inference system is also performed to appraise the accuracy of these models. The results show that the proposed diagnosis system is capable of improving the classification accuracy and efficiently assisting in rotating machinery fault diagnosis.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Thermal image enhancement using bi-dimensional empirical mode decomposition in combination with relevance vector machine for rotating machinery fault diagnosis

Tran

Yang

et al. 2013

Mechanical Systems and Signal Processing

111

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An enhanced Empirical Mode Decomposition based method for image enhancement

Bakhtiari

Agaian²,

Jamshidi³

2011

2011 IEEE International Conference on Systems, Man, and Cybernetics

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In this paper, a new extension of Bidimensional Empirical Mode Decomposition (BEMD) based on the median filtering is presented. The new scheme is compared with the traditional Empirical Mode Decomposition (EMD) and Fast and Adaptive Bidimensional EMD (FABEMD) techniques. The new scheme manifests to be superior to both of them in image enhancement and particularly in edge enhancement. The comparison is performed, objectively, using the measure of enhancement (EME) and also visually. Furthermore, we show that by applying different thresholds in stopping condition for each Intrinsic Mode Function (IMF), we can derive more accurate IMFs.

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A Novel empirical mode decomposition based system for medical image enhancement

Cited by 2 publications

References 12 publications

Thermal image enhancement using bi-dimensional empirical mode decomposition in combination with relevance vector machine for rotating machinery fault diagnosis

Thermal image enhancement using bi-dimensional empirical mode decomposition in combination with relevance vector machine for rotating machinery fault diagnosis

An enhanced Empirical Mode Decomposition based method for image enhancement

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