A hybrid filtering technique in medical image denoising: Blending of neural network and fuzzy inference

Choubey, Abha; Sinha, G. R.; Choubey, Siddhartha

doi:10.1109/icectech.2011.5941584

Cited by 12 publications

(2 citation statements)

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“…Thus, removing noise and the immunization to data are challenging tasks in random and non-random processes. Gaussian noise [7] and speckle noise [8] which are used in used in ANFIS are examples of these challenges. Examples [9] and [10] illustrate the use of ANFIS in the field of cancer growth segmentation.…”

Section: Pre-processingmentioning

confidence: 99%

Big data simulation software for breast cancer growth repository system

Alias

Ghani²,

Saipo³

et al. 2018

IJET

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The development of the simulation software aims at anticipating the growth of breast cancer. Based on certain numerical iterative methods, this simulation works with discretization and Partial Differential Equation (PDE). As a class of Helmholtz equations, PDE approach are known to govern the growth of this type of cancer. Considering both time and place, the Helmholtz equation’s accuracy visualizes breast cancer and its growth. This growth is of breast cancer is captured and the convergence results in sequential and parallel computing environ-ment is expressed through the numerical libraries available in the repository system. Currently, both the parallel performance measurement and Numerical analysis that involve execution time, speedup, efficiency, effectiveness and temporal performance are being investigated. The process of breast cancer visualization requires a huge memory and expensive calculations. It is observed that both the distributed memory and distributed processors of the parallel computer systems development were required in most of the studies conducted on the growth of this cancer. It is considered as an important computation platform needed to the development of parallel repository system leading to an increase in the speed and a decrease in the cost. The simulation software has several beneficial characteristics such as high performance estimation, multidimensional visualization of breast cancer and being friendly. It also provides a real time solution and strength. This soft-ware is expected to increase the level of confidence in terms of computer-aided decision making which can be reflected positively on com-prehensive breast cancer screening; breast cancer diagnosis; and clinical assessments and treatment.

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Section: Pre-processingmentioning

confidence: 99%

Big data simulation software for breast cancer growth repository system

Alias

Ghani²,

Saipo³

et al. 2018

IJET

View full text Add to dashboard Cite

show abstract

“…These methods are usually composed of a wide range of combination of fuzzy logic, wavelet transformation or that of the neural network [2][3][4][5][6]. Since the mammograms show larger areas of varying contrast and brightness, thus the information is highly susceptible to being correlated [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Wavelet Transformation for Enhancing Mammographic Images

Rai

2017

Asian J Pharm Clin Res

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Objective: Mammographic images are often prone to noises and consequently make the task of radiologist to come up with the precise diagnosis. Although there are several denoising techniques for the same is available, while denoising they often suffer from the problem of eliminating the micron level details in the noise influenced images. It's a trade-off which prohibits efficient micro-classification of mammary tissues. Methods:In this study, we present a solution for the same by utilizing multi-level wavelet transformation to enable preservation of micron level details in the images. Results and Conclusion:The quality denoising without elimination of the features of the mammographic imagery data by MWTA will allow the medical practitioner to easily identify and consequently diagnose properly to cancer influenced patients.

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Introduction to artificial intelligence techniques for medical image analysis

Subaşı

2023

Applications of Artificial Intelligence in Medical Imaging

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A hybrid filtering technique in medical image denoising: Blending of neural network and fuzzy inference

Cited by 12 publications

References 13 publications

Big data simulation software for breast cancer growth repository system

Big data simulation software for breast cancer growth repository system

Wavelet Transformation for Enhancing Mammographic Images

Introduction to artificial intelligence techniques for medical image analysis

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