Comparison of Denoising Filters on Greyscale TEM Image for Different Noise

Goyal, Garima; Bansal, Ajay Kumar; Singhal, Manish

doi:10.9790/0661-0543744

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…Medical imaging is a crucial part of healthcare for the identification of diseases. There are various imaging techniques for different applications to observe the anatomical and physiological conditions of the patient [2]; however, these techniques can produce noise during the medical image processing. Clear CT or magnetic resonance (MR) images are significantly important for the analysis of the disease.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Noisy Pixels Detection Model for CT Images using Extreme Learning Machines

Çalışkan

Çevik

2018

Teh. vjesn.

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In this study, a new and rapid hidden resource decomposition method has been proposed to determine noisy pixels by adopting the extreme learning machines (ELM) method. The goal of this method is not only to determine noisy pixels, but also to protect critical structural information that can be used for disease diagnosis. In order to facilitate the diagnosis and also the treatment of patients in medicine, two-dimensional (2-D) images were calculated tomography (CT) which is obtained using medical imaging techniques. Utilizing a large number of CT images, promising results have been obtained from these experiments. The proposed method has shown a significant improvement on mean squared error and peak signal-to-noise ratio. The experimental results indicate that the proposed method is statistically efficient, and it has a good performance with a high learning speed. In the experiments, the results demonstrated that remarkable successive rates were obtained through the ELM method.

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Section: Introductionmentioning

confidence: 99%

An Efficient Noisy Pixels Detection Model for CT Images using Extreme Learning Machines

Çalışkan

Çevik

2018

Teh. vjesn.

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“…Human body whose temperature is above zero Kelvin has the ability to emit infra-red radiations as an exponential function of temperature. ideal body is an object which notice all radiations occurrence on it and release them back.The wavelength of emission is dependent on temperature [3] and spectral emissivity, e (1"). For ideal body e:1, grey objects e < l. The spectral emissivity of any objects will be in the range of 0 < e < l. According to radiation law Planks, the radiation power and its wavelength distribution is given as,…”

Section: Introductionmentioning

confidence: 99%

Performance of segmentation in infrared breast thermograms using level set method

Raj¹,

Hariharan²

2018

IJET

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Breast cancer is the overall most basic obtrusive tumour in females. The breast cancer can be dealt with successfully that they are analyzed at a beginning time. It can watch the principal indication of shaping up disease before mammography can recognize. The thermal data can be appeared in a pseudo shading where each shading speaks to a particular scope of temperature. Different techniques can be connected to extricate hot districts for distinguishing associated areas with interests in the thermograms and possibly suspicious tissues. in this paper at first the pre process of the thermogram pictures are done then they are improved. The upgraded pictures are divided by two picture segmentation strategy: K-means and level set technique are study and compared. The highlights have been extracted and classification for both the segmentation techniques.

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Comparison of Denoising Filters on Greyscale TEM Image for Different Noise

Abstract: TEM (Transmission Electron Microscopy)

Cited by 2 publications

References 6 publications

An Efficient Noisy Pixels Detection Model for CT Images using Extreme Learning Machines

An Efficient Noisy Pixels Detection Model for CT Images using Extreme Learning Machines

Performance of segmentation in infrared breast thermograms using level set method

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