Noise removal in medical mammography images using fast non-local means denoising algorithm for early breast cancer detection: a phantom study

Lee, Sungtaek; Park, Seong Jin; Jeon, Ji Min; Lee, Mi-Hwa; Ryu, Dae Yeon; Lee, Eunbyeol; Kang, Seong-Hyeon; Lee, Youngjin

doi:10.1016/j.ijleo.2018.11.167

Cited by 21 publications

(7 citation statements)

References 5 publications

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“…Proposed structure has accomplished a superior presentation contrasted and different calculations. Lee et al (2019) proposed a Fast Non Local Means (FNLM) de-noising calculation for prior breast malignancy recognition dependent on clinical mammography. In order to correlate with ordinary denoising techniques, the Wiener channel and Total Variation (TV) denoising calculation are utilized.…”

Section: Literature Reviewmentioning

confidence: 99%

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2021

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For women, most common cause of death is Breast tumour and in worldwide, it is the second leading reason for cancer deaths. Due the requirement of breast cancer's early detection and false diagnosis impact on patients, made researchers to investigate Deep Learning (DL) techniques for mammograms. There are four stages in this proposed HIRResCNN framework, namely, Pre-processing, reduction of dimensionality, segmentation and classification. From images, noises are removed using two filtering algorithms called Median and mean filtering in pre-processing stage. Then canny edge detector is used for detecting edges. Gaussian filtering is used in canny edge detector to smoothen the images. In the next dimensionality reduction stage, attributes are correlated using Principal Component Analysis (PCA) inclusive of related features. So, this huge dataset is minimized and only few variables are used for expressing it. In order to detect the breast cancer accurately, foreground and background subtraction is done in the third stage called segmentation stage. At last, for detecting and classifying breast cancer, a Hybrid Inception Recurrent Residual Convolutional Neural Network (HIRResCNN) is introduced, which integrates Harmony Search Optimization (HSO) to tune bias and weight parameters and classification accuracy is enhanced using HIRResCNN-HSO model. Strength of Recurrent Convolutional Neural Network (RCNN), Residual Network (ResNet) and Inception Network (Inception-v4), are combined in a powerful Deep Convolutional Neural Network (DCNN) model called HIRResCNN. using Mammographic Image Analysis Society (MIAS) dataset, various experiments are conduced and results are compared with other available techniques. Around 92.6% accuracy rate is produced using this proposed HIRResCNN classifier in finding breast cancer.

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Section: Literature Reviewmentioning

confidence: 99%

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2021

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“…In [20,21], convolutional neural networks (CNNs) are applied to minimize the noise in mammograms. Recently, Total Variation (TV) and Non-Local Mean (NLM) algorithms are developed to mitigate some shortages of repeatable noise elimination in medical images [22].…”

Section: Literature Reviewmentioning

confidence: 99%

A Repeated Median Filtering Method for Denoising Mammogram Images

AlSalman¹

2020

IJACSA

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In the medical field, mammogram analysis is one of the most important breast cancer detection procedures and early diagnosis. During the image acquisition process of mammograms, the acquired images may be contained some noises due to the change of illumination and sensor error. Hence, it is necessary to remove these noises without affecting the edges and fine details, achieving an effective diagnosis of beast images. In this work, a repeated median filtering method is proposed for denoising digital mammogram images. A number of experiments are conducted on a dataset of different mammogram images to evaluate the proposed method using a set of image quality metrics. Experimental results are reported by computing the image quality metrics between the original clean images and denoised images that are corrupted by different levels of simulated speckle noise as well as salt and paper noise. Evaluation quality metrics showed that the repeated median filter method achieves a higher result than the related traditional median filter method.

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“…In paper [3], authors proposed an empirical two-dimensional decomposition mode (BEMD) is performed in digital images. There are three-dimensional cubes showing BEMD's output which are well aligned with intuition and physical perception.…”

Section: Figure 1: Traditional Thresholding Based Image Denoisingmentioning

confidence: 99%

A Novel Convolutional Autoencoders Based Multilevel Denoising Framework for Medical Image Data

2020

jcr

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Convolutional autoencoders based image denoising has emerged as a promising approach in medical imaging applications. Most of the traditional denoising methods such as Bayesian filter, non-linear median filter, wavelet based shearlet transform etc, are difficult to process medical images due to the existence of additive, multiplicative and Gaussian noise. Also, these models cannot resolve the issue of sparsity in the compressed medical images. To overcome these issues, a novelcompressive sensing based multilevel denoising technique is proposed to minimize the noise rate of medical images in the Convolution neural network(CNN) framework. This technique is implemented in convolutional autoencoders framework to reduce the noise in each deep network layer and to improve the quality of the images. This technique efficiently denoise the medical images using the inter, intra variance in the CNN framework. Experimental results demonstrated that the proposed framework has better PSNR and SSIM measures compared to the traditional state-of-the-art approaches.

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Noise removal in medical mammography images using fast non-local means denoising algorithm for early breast cancer detection: a phantom study

Cited by 21 publications

References 5 publications

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A Repeated Median Filtering Method for Denoising Mammogram Images

A Novel Convolutional Autoencoders Based Multilevel Denoising Framework for Medical Image Data

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