Evolutionary Design of Morphology-Based Homomorphic Filter for Feature Enhancement of Medical Images

Hwang, Han Sung; Oh, Jin Seok

doi:10.5391/ijfis.2009.9.3.172

Cited by 3 publications

(2 citation statements)

References 10 publications

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“…In this paper we evaluate the performance of several denoising approaches of fuzzy filtering and convolutional denoising autoencoding (CDAE) and their combination on MRI images of the human brain [9,10]. The performance comparison has been done using PSNR (Peak Signal to Noise Ratio) [11].…”

Section: Introductionmentioning

confidence: 99%

Denoising Approaches Using Fuzzy Logic and Convolutional Autoencoders for Human Brain MRI Image

Chauhan

Choi

2019

IJFIS

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In biomedical images, one of the serious issues is noise which affects their coherent nature. To analyze the results for the detection and treatment of disease, it is essential to remove the noise. The advancement in brain imaging technologies requires reasonable techniques for pre-processing steps like denoising, deblurring, contrast enhancement, etc. Magnetic resonance imaging (MRI) images of the human brain are often corrupted with noises due to the application of various image acquisition techniques, operator performance, and types of equipment. In this paper, we evaluate several fuzzy logic based denoising filters. A combined approach of fuzzy logic and a convolutional autoencoder has been also used on a brain image dataset for the performance evaluation. The experimental results show that the combined approach performs better than other methods.

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Section: Introductionmentioning

confidence: 99%

Denoising Approaches Using Fuzzy Logic and Convolutional Autoencoders for Human Brain MRI Image

Chauhan

Choi

2019

IJFIS

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“…Denoising and enhancement of the medical images can be useful in feature extraction, image restoration and reducing distortion of complex images like MRI of human brain [4][5][6][7]. The noise infected may be Gaussian noise, speckle noise, Poisson noise, etc.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Denoising Algorithms for Human Brain Image

Chauhan¹,

Choi²

2018

IJFIS

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Digital images have an important role in areas like X-ray, face recognition, and so on. Different image capturing devices inherit different types of noises. Sometimes low image quality is an obstacle for analysis and measurement. The denoising of an image is an important task. An ideal denoising technique should be able to remove the noise while preserving the quality of the image. In this paper, we examine four noise removal algorithms and present a performance analysis of PSNR and RMSE of several filters for various noises and simulate the performance using MATLAB for human brain image.

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An Automated Technique for Detecting Axon Structure in Time-Lapse Neural Image Sequence

Kim¹

2014

Journal of Korean Institute of Intelligent Systems

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The purpose of the neural image analysis is to trace the velocities and the directions of moving mitochondria migrating through axons. This paper proposes an automated technique for detecting axon structure. Previously, the detection process has been carried out using a partially automated technique combined with some human intervention. In our algorithm, a consolidated image is built by taking the maximum intensity value on the all image frames at each pixel Axon detection is performed through vessel enhancement filtering followed by a peak detection procedure. In order to remove errors contained in ridge points, a filtering process is devised using a local reliability measure. Experiments have been performed using real neural image sequences and ground truth data extracted manually. It has been turned out that the proposed algorithm results in high detection rate and precision.

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Evolutionary Design of Morphology-Based Homomorphic Filter for Feature Enhancement of Medical Images

Cited by 3 publications

References 10 publications

Denoising Approaches Using Fuzzy Logic and Convolutional Autoencoders for Human Brain MRI Image

Denoising Approaches Using Fuzzy Logic and Convolutional Autoencoders for Human Brain MRI Image

Performance Analysis of Denoising Algorithms for Human Brain Image

An Automated Technique for Detecting Axon Structure in Time-Lapse Neural Image Sequence

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