A Modified Undecimated Discrete Wavelet Transform Based Approach to Mammographic Image Denoising

Matsuyama, Eri; Tsai, Du-Yih; Lee, Yongbum; Tsurumaki, Masaki; Takahashi, Noriyuki; Watanabe, H.; Chen, Hsian‐Min

doi:10.1007/s10278-012-9555-6

Cited by 30 publications

(22 citation statements)

References 51 publications

(50 reference statements)

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“…DecWT is the shift-variant and is more widely used as it is very efficient from the computational point of view. 38 UWT is the shift-invariant due to the elimination of the decimation step making it more suitable for image enhancement algorithms. 39 Although WT is not yet widely deployed in RIQA, both DecWT and UWT have been successfully applied in RIQA algorithms.…”

Section: Transform Domain Retinal Image Quality Assessmentmentioning

confidence: 99%

Retinal image quality assessment based on image clarity and content

et al. 2016

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Retinal image quality assessment (RIQA) is an essential step in automated screening systems to avoid misdiagnosis caused by processing poor quality retinal images. A no-reference transform-based RIQA algorithm is introduced that assesses images based on five clarity and content quality issues: sharpness, illumination, homogeneity, field definition, and content. Transform-based RIQA algorithms have the advantage of considering retinal structures while being computationally inexpensive. Wavelet-based features are proposed to evaluate the sharpness and overall illumination of the images. A retinal saturation channel is designed and used along with wavelet-based features for homogeneity assessment. The presented sharpness and illumination features are utilized to assure adequate field definition, whereas color information is used to exclude nonretinal images. Several publicly available datasets of varying quality grades are utilized to evaluate the feature sets resulting in area under the receiver operating characteristic curve above 0.99 for each of the individual feature sets. The overall quality is assessed by a classifier that uses the collective features as an input vector. The classification results show superior performance of the algorithm in comparison to other methods from literature. Moreover, the algorithm addresses efficiently and comprehensively various quality issues and is suitable for automatic screening systems.

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Section: Transform Domain Retinal Image Quality Assessmentmentioning

confidence: 99%

Retinal image quality assessment based on image clarity and content

et al. 2016

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“…In the medical field, two-dimensional WT has been applied to data compression, image enhancement, noise removal, etc. [24]. In the multiresolution analysis, an image is initialized at level 0 (original image without decomposition).…”

Section: Wavelet Transformsmentioning

confidence: 99%

“…The basic algorithm of the conventional RDWT is that it applies the transform at each point of the image and saves the detailed coefficients and uses the approximation coefficients for the next level. The size of the coefficients array does not diminish from level to level [24]. The wavelet basis function used in the present study was Daubechies order 2 (db2).…”

Section: Wavelet Transformsmentioning

confidence: 99%

Automated Classification of Lung Diseases in Computed Tomography Images Using a Wavelet Based Convolutional Neural Network

Matsuyama¹,

Tsai²

2018

JBiSE

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Recently, convolutional neural networks (CNNs) have been utilized in medical imaging research field and have successfully shown their ability in image classification and detection. In this paper we used a CNN combined with a wavelet transform approach for classifying a dataset of 448 lung CT images into 4 categories, e.g. lung adenocarcinoma, lung squamous cell carcinoma, metastatic lung cancer, and normal. The key difference between the commonly-used CNNs and the presented method is that in this method, we adopt the use of redundant wavelet coefficients at level 1 as inputs to the CNN, instead of using original images. One of the main advantages of the proposed method is that it is not necessary to extract regions of interest from original images. The wavelet coefficients of the entire image are used as inputs to the CNN. We compare the classification performance of the proposed method to that of an existing CNN classifier and a CNN-based support vector machine classifier. The experimental results show that the proposed method outperforms the other two methods and achieve the highest overall accuracy of 91.9%. It demonstrates the potential for use in classification of lung diseases in CT images.

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“…The background and the foreground of the mammograms are properly segmented to facilitate the computation of contrast enhancement index. In the recent times, Matsuyama et al [6] proposed the mammogram noise reduction by the hierarchical correlation of the wavelet coefficients. The proposed technique when compared with the conventional undecimated discrete wavelet transform exposes less computation time and better image quality.…”

Section: Introductionmentioning

confidence: 99%

Mammogram denoising by curvelet transform based on the information of neighbouring coefficients

Saha

Naskar

Chatterji

2015

Proceedings of the 2015 Third International Conference on Computer, Communication, Control and Information Technology (C3IT)

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We present here an experimental work on mammogram denoising by the mathematical tool called the curvelet transform. The infiltration of noise in mammogram during the X-ray screening is a common and inevitable phenomenon. And such noise is normally reduced by the curvelet transform based on conventional thresholding strategy called the hard thresholding (HT). Therefore, the motive of this experimentation is to suggest an alternate but efficient mechanism of mammogram denoising by the same transform but with different algorithms purely based on the information of the neighbouring coefficients. It is found that the curvelet transform applied with the precept of surrounding curvelet coefficients is visually and statistically better than the conventional approach based on HT.

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A Modified Undecimated Discrete Wavelet Transform Based Approach to Mammographic Image Denoising

Cited by 30 publications

References 51 publications

Retinal image quality assessment based on image clarity and content

Retinal image quality assessment based on image clarity and content

Automated Classification of Lung Diseases in Computed Tomography Images Using a Wavelet Based Convolutional Neural Network

Mammogram denoising by curvelet transform based on the information of neighbouring coefficients

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