Technologies of medicine and (original) (raw)
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International Journal of Computer Applications, 2012
In this paper we develop an adaptive algorithm for decomposition and filtering of grayscales images. This method is highly adaptive decomposition image called Bidimensional Empirical Mode Decomposition (BEMD) based in blocks. This proposed approach decomposes image into a basis functions named Intrinsic Mode Function (IMF) and residue. This method offers a good result in visual quality but it consumes an important execution time. To overcome this problem we propose a new approach using Block based BEMD method where the input image is subdivided into blocks. Then the conventional BEMD is applied on each of the four blocks separately. This proposed extended method gives a solution in reduction of execution time. This approach shows the good results in the field of image filtering. Denoised image is obtained by summing the residue and the filtered first IMFs (the detail) using a wavelet technique. Experimental results positively show that this proposed methodology removes Gaussian and Impulsive noises from the images.
… Conference on Systems, …, 2010
This paper proposes a method for image denoising in the filter domain based on the characteristics of the Empirical Mode Decomposition (EMD) and the wavelet technique. The proposed method uses the EMD to the decomposition and double density wavelet to filter components. Our experimental results show that these image denoising methods are more efficient than the wavelet denoising method. Finally, the PSNR (peak signal noise ratio) and the visualization of the denoising image are used as performance comparison indexes.
The Modified Bidimensional Empirical Mode Decomposition for Color Image Decomposition
2011
This paper presents two proposed approaches to color image decomposition with Bidimensional Empirical Mode Decomposition (BEMD) technique. The first one applies the BEMD on each channel separately and the second is based on interpolation of each channel in the sifting process. The application of the two methods shows the same performance of each approach in terms of PSNR and visual quality, but they do not provide the same results in execution time which presents the most important criterion in real time applications. It was shown that the second BEMD approach based on interpolation of each channel in the sifting process, gives a gain in the point of view the execution time.
International Journal of Computer Science, Engineering and Information Technology, 2012
This paper presents two different approaches in color image decomposition domain with Bidimensional Empirical Mode Decomposition (BEMD). The first approach applies the BEMD on each channel separately and the second is based on interpolation of each channel in the sifting process. The comparison of two approaches shows the same performance of each approach in terms of visual quality, but they do not provide the same results in execution time which presents the most important criteron in real time applications. It was shown that the second BEMD approach based on interpolation of each channel in the sifting process, gives a gain in the point of view the execution time.
Image analysis by bidimensional empirical mode decomposition
Image and Vision Computing, 2003
Recent developments in analysis methods on the non-linear and non-stationary data have received large attention by the image analysts. In 1998, Huang introduced the empirical mode decomposition (EMD) in signal processing. The EMD approach, fully unsupervised, proved reliable monodimensional (seismic and biomedical) signals. The main contribution of our approach is to apply the EMD to texture extraction and image filtering, which are widely recognized as a difficult and challenging computer vision problem. We developed an algorithm based on bidimensional empirical mode decomposition (BEMD) to extract features at multiple scales or spatial frequencies. These features, called intrinsic mode functions, are extracted by a sifting process. The bidimensional sifting process is realized using morphological operators to detect regional maxima and thanks to radial basis function for surface interpolation. The performance of the texture extraction algorithms, using BEMD method, is demonstrated in the experiment with both synthetic and natural images. q
Noise Reduction Method for Oct Images Based on Empirical Mode Decomposition
Journal of Innovative Optical Health Sciences, 2013
In this paper, the new method for OCT images denoizing based on empirical mode decomposition (EMD) is proposed. The noise reduction is a very important process for following operations to analyze and recognition of tissue structure. Our method does not require any additional operations and hardware modi¯cations. The basics of proposed method is described. Quality improvement of noise suppression on example of edge-detection procedure using the classical Canny's algorithm without any additional pre-and post-processing operations is demonstrated. Improvement of rawsegmentation in the automatic diagnostic process between a tissue and a mesh implant is shown.
Fast bidimensional empirical mode decomposition based on an adaptive block partitioning
2008
The decomposition of images with great size using the Bidimensional Empirical Mode Decomposition (BEMD) necessitates an important calculation time. To overcome this problem we first proposed in [1] a new approach using Blockbased BEMD method (BBEMD) where an input image is subdivided into four subblocks, and then the BEMD is applied on each of the four blocks separately. This method offered a good solution to the calculation time, unfortunately blocking artefact is noticeable in the resulting modes, this is a result of ignoring the interblock correlation during the BEMD process and every block was taken as an independent entity when interpolating the blocks borders in the sifting process. The Lapped Block-based BEMD (LBBEMD) is then proposed to whelm the artefact problem using for every block the neighbouring information by enlarging the block neighbourhood related to the two adjacent blocks inside the image during the blocks decomposition. Enlarging neighbourhood blocks necessitate intensive simulations to have for each block the corresponding enlargement size, thing that affects remarkably on the calculation time. Experimentation shows that the LBBEMD can not handle correctly the artefact problems, because the enlargement size can not be enough "or too big" to surround all the concerned pixels for each block in the image. The problem with the LBBEMD is to exactly define, a priori, for each block the size of the neighbouring enlargement size. In this paper, we will present an adaptive and fast Block Empirical mode decomposition to eliminate definitively the artefacts problems when subdividing an image into lapped blocks and we will prove, by simulations, that our method offers a good trade-off between computational time and decomposition quality.
A bidimensional empirical mode decomposition method for color image processing
2010
This study introduces a new approach based on Bidimensional Empirical Mode Decomposition (BEMD) to extract texture features at multiple scales or spatial frequencies. Moreover, it can resolve the intrawave frequency modulation provided the frequency modulation. This decomposition, obtained by the bidimensional sifting process, plays an important role in the characterization of regions in textured images. The sifting process is realized using morphological operators to analyze the spatial frequencies and thanks to radial basis functions (RBF) for surface interpolation. We modified the original sifting algorithm to permit a pseudo bandpass decomposition of images by inserting scale criterion. Its effectiveness is demonstrated on synthetic and natural textures. In particular, we show that many different elements in textures can be extracted through the bidimensional empirical mode decomposition, which is fully unsupervised.
Procedia Computer Science, 2020
Electrocardiogram, popularly known as ECG is a diagnostic test which detects the heart's electrical system. It is an inevitable diagnostic technique in the medical field which helps to detect any heart related diseases accurately. But there are high chances of addition of any random unwanted signals or noises during the recording process. This hinders the exact diagnosis and hence it is important to denoise these signals. There exist various denoising techniques including Empirical mode decomposition (EMD), Non local means (NLM), various filters, etc. A modified method of EMD is presented here which performs smaller number of iterations and forms only a few Intrinsic mode functions (IMF) when compared with traditional EMD method.. The presented work is a performance comparison study between the modified version of EMD which has lesser latency with the traditional EMD.
Empirical Mode Decomposition Based Denoising by Customized Thresholding
World Academy of Science, Engineering and Technology, International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering, 2017
Abstract—This paper presents a denoising method called EMDCustom that was based on Empirical Mode Decomposition (EMD) and the modified Customized Thresholding Function (Custom) algorithms. EMD was applied to decompose adaptively a noisy signal into intrinsic mode functions (IMFs). Then, all the noisy IMFs got threshold by applying the presented thresholding function to suppress noise and to improve the signal to noise ratio (SNR). The method was tested on simulated data and real ECG signal, and the results were compared to the EMD-Based signal denoising methods using the soft and hard thresholding. The results showed the superior performance of the proposed EMD-Custom denoising over the traditional approach. The performances were evaluated in terms of SNR in dB, and Mean Square Error (MSE).