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Author

Dang N. H. Thanh

Other affiliations: Tula State University
Bio: Dang N. H. Thanh is an academic researcher from University of Economics, Ho Chi Minh City. The author has contributed to research in topics: Image restoration & Noise reduction. The author has an hindex of 14, co-authored 62 publications receiving 516 citations. Previous affiliations of Dang N. H. Thanh include Tula State University.

Papers published on a yearly basis

Papers
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Journal ArticleDOI
TL;DR: An Iterative Mean Filter (IMF) is proposed to eliminate the salt-and-pepper noise by using the mean of gray values of noise-free pixels in a fixed-size window and outperforms the other state-of-the-art methods.
Abstract: We propose an Iterative Mean Filter (IMF) to eliminate the salt-and-pepper noise. IMF uses the mean of gray values of noise-free pixels in a fixed-size window. Unlike other nonlinear filters, IMF does not enlarge the window size. A large size reduces the accuracy of noise removal. Therefore, IMF only uses a window with a size of $3\times3$ . This feature is helpful for IMF to be able to more precisely evaluate a new gray value for the center pixel. To process high-density noise effectively, we propose an iterative procedure for IMF. In the experiments, we operationalize Peak Signal-to-Noise Ratio (PSNR), Visual Information Fidelity, Image Enhancement Factor, Structural Similarity (SSIM), and Multiscale Structure Similarity to assess image quality. Furthermore, we compare denoising results of IMF with ones of the other state-of-the-art methods. A comprehensive comparison of execution time is also provided. The qualitative results by PSNR and SSIM showed that IMF outperforms the other methods such as Based-on Pixel Density Filter (BPDF), Decision-Based Algorithm (DBA), Modified Decision-Based Untrimmed Median Filter (MDBUTMF), Noise Adaptive Fuzzy Switching Median Filter (NAFSMF), Adaptive Weighted Mean Filter (AWMF), Different Applied Median Filter (DAMF), Adaptive Type-2 Fuzzy Filter (FDS): for the IMAGESTEST dataset - BPDF (25.36/0.756), DBA (28.72/0.8426), MDBUTMF (25.93/0.8426), NAFSMF (29.32/0.8735), AWMF (32.25/0.9177), DAMF (31.65/0.9154), FDS (27.98/0.8338), and IMF (33.67/0.9252); and for the BSDS dataset - BPDF (24.95/0.7469), DBA (26.84/0.8061), MDBUTMF (26.25/0.7732), NAFSMF (27.26/0.8191), AWMF (28.89/0.8672), DAMF (29.11/0.8667), FDS (26.85/0.8095), and IMF (30.04/0.8753).

77 citations

Journal ArticleDOI
TL;DR: Results on melanoma skin cancer detection indicates that the proposed method has high accuracy, and overall, a good performance: for the segmentation stage, the accuracy, Dice, Jaccard scores are 96.6%, 93.9% and 88.7%, respectively; and for the melanoma detection stage,The accuracy is up to 100% for a selected subset of the ISIC dataset.
Abstract: According to statistics of the American Cancer Society, in 2015, there are about 91,270 American adults diagnosed with melanoma of the skin. For the European Union, there are over 90,000 new cases of melanoma annually. Although melanoma only accounts for about 1% of all skin cancers, it causes most of the skin cancer deaths. Melanoma is considered one of the fastest-growing forms of skin cancer, and hence the early detection is crucial, as early detection is helpful and can provide strong recommendations for specific and suitable treatment regimens. In this work, we propose a method to detect melanoma skin cancer with automatic image processing techniques. Our method includes three stages: pre-process images of skin lesions by adaptive principal curvature, segment skin lesions by the colour normalisation and extract features by the ABCD rule. We provide experimental results of the proposed method on the publicly available International Skin Imaging Collaboration (ISIC) skin lesions dataset. The acquired results on melanoma skin cancer detection indicates that the proposed method has high accuracy, and overall, a good performance: for the segmentation stage, the accuracy, Dice, Jaccard scores are 96.6%, 93.9% and 88.7%, respectively; and for the melanoma detection stage, the accuracy is up to 100% for a selected subset of the ISIC dataset.

70 citations

Journal ArticleDOI
TL;DR: The proposed hybrid pre-trained network outperforms other compared Convolutional Neural Networks and can be considered as an effective tool for radiologists to decrease the false negative and false positive rates of mammograms.
Abstract: Breast cancer is a common cancer for women. Early detection of breast cancer can considerably increase the survival rate of women. This paper mainly focuses on transfer learning process to detect breast cancer. Modified VGG (MVGG), residual network, mobile network is proposed and implemented in this paper. DDSM dataset is used in this paper. Experimental results show that our proposed hybrid transfers learning model (Fusion of MVGG16 and ImageNet) provides an accuracy of 88.3% where the number of epoch is 15. On the other hand, only modified VGG 16 architecture (MVGG 16) provides an accuracy 80.8% and MobileNet provides an accuracy of 77.2%. So, it is clearly stated that the proposed hybrid pre-trained network outperforms well compared to single architecture. This architecture can be considered as an effective tool for the radiologists in order to reduce the false negative and false positive rate. Therefore, the efficiency of mammography analysis will be improved.

61 citations

Book ChapterDOI
TL;DR: This work focuses on different classification techniques implementation for data mining in predicting malignant and benign breast cancer using Breast Cancer Wisconsin data set from the UCI repository.
Abstract: As much as data science is playing a pivotal role everywhere, health care also finds its prominent application. Breast Cancer is the top-rated type of cancer amongst women; which alone took away 627,000 lives. This high mortality rate due to breast cancer does need attention, for early detection so that prevention can be done in time. As a potential contributor to state-of-the-art technology development, data mining finds a multi-fold application in predicting Brest cancer. This work focuses on different classification techniques implementation for data mining in predicting malignant and benign breast cancer. Breast Cancer Wisconsin data set from the UCI repository has been used as an experimental dataset while attribute clump thickness being used as an evaluation class. The performances of these twelve algorithms: Ada Boost M1, Decision Table, J-Rip, J48, Lazy IBK, Lazy K-star, Logistics Regression, Multiclass Classifier, Multilayer–Perceptron, Naive Bayes, Random Forest, and Random Tree is analyzed on this data set.

54 citations

Journal ArticleDOI
01 Apr 2020-Optik
TL;DR: The adaptive TV denoising method is developed based on the general regularized image restoration model with L1 fidelity for handling salt and pepper noise model and results indicate the authors obtain artifact free edge preserving restorations.

51 citations


Cited by
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01 Jan 2016
TL;DR: The two dimensional signal and image processing is universally compatible with any devices to read and is available in the book collection an online access to it is set as public so you can download it instantly.
Abstract: Thank you for downloading two dimensional signal and image processing. As you may know, people have look hundreds times for their chosen novels like this two dimensional signal and image processing, but end up in malicious downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they juggled with some infectious virus inside their computer. two dimensional signal and image processing is available in our book collection an online access to it is set as public so you can download it instantly. Our digital library spans in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Kindly say, the two dimensional signal and image processing is universally compatible with any devices to read.

253 citations

Journal ArticleDOI
TL;DR: DAMF could be successfully removed SAP noise at all densities and was compared with other methods by using Peak Signal to Noise Ratio (PSNR) and Structural Similarity (SSIM) for some images such as Cameraman and Lena.

145 citations

Journal ArticleDOI
TL;DR: A new method to remove salt and pepper noise, which is based on pixel density filter (BPDF), which shows that BPDF produces better results than the above-mentioned methods at low and medium noise density.
Abstract: In this paper, we deliver a new method to remove salt and pepper noise, which we refer to as based on pixel density filter (BPDF) The first step of the method is to determine whether or not a pixel is noisy, and then we decide on an adaptive window size that accepts the noisy pixel as the center The most repetitive noiseless pixel value within the window is set as the new pixel value By using 18 test images, we give the results of peak signal-to-noise ratio (PSNR), structural similarity (SSIM), image enhancement factor (IEF), standard median filter (SMF), adaptive median filter (AMF), adaptive fuzzy filter (AFM), progressive switching median filter (PSMF), decision-based algorithm (DBA), modified decision-based unsymmetrical trimmed median filter (MDBUTMF), noise adaptive fuzzy switching median filter (NAFSM), and BPDF The results show that BPDF produces better results than the above-mentioned methods at low and medium noise density

89 citations

Journal ArticleDOI
TL;DR: The goal of this paper is to provide an apt choice of denoising method that suits to CT and X-ray images and to provide a review of the following important Poisson removal methods.
Abstract: In medical imaging systems, denoising is one of the important image processing tasks. Automatic noise removal will improve the quality of diagnosis and requires careful treatment of obtained imagery. Com-puted tomography (CT) and X-Ray imaging systems use the X radiation to capture images and they are usually corrupted by noise following a Poisson distribution. Due to the importance of Poisson noise re-moval in medical imaging, there are many state-of-the-art methods that have been studied in the image processing literature. These include methods that are based on total variation (TV) regularization, wave-lets, principal component analysis, machine learning etc. In this work, we will provide a review of the following important Poisson removal methods: the method based on the modified TV model, the adaptive TV method, the adaptive non-local total variation method, the method based on the higher-order natural image prior model, the Poisson reducing bilateral filter, the PURE-LET method, and the variance stabi-lizing transform-based methods. Our task focuses on methodology overview, accuracy, execution time and their advantage/disadvantage assessments. The goal of this paper is to provide an apt choice of denoising method that suits to CT and X-ray images. The integration of several high-quality denoising methods in image processing software for medical imaging systems will be always excellent option and help further image analysis for computer-aided diagnosis.

80 citations

Journal ArticleDOI
TL;DR: An ensemble classification mechanism is proposed based on a majority voting mechanism for breast cancer classification based on simple logistic regression learning, support vector machine learning with stochastic gradient descent optimization and multilayer perceptron network.
Abstract: Breast cancer is the most common cause of death for women worldwide. Thus, the ability of artificial intelligence systems to detect possible breast cancer is very important. In this paper, an ensemble classification mechanism is proposed based on a majority voting mechanism. First, the performance of different state-of-the-art machine learning classification algorithms were evaluated for the Wisconsin Breast Cancer Dataset (WBCD). The three best classifiers were then selected based on their F3 score. F3 score is used to emphasize the importance of false negatives (recall) in breast cancer classification. Then, these three classifiers, simple logistic regression learning, support vector machine learning with stochastic gradient descent optimization and multilayer perceptron network, are used for ensemble classification using a voting mechanism. We also evaluated the performance of hard and soft voting mechanism. For hard voting, majority-based voting mechanism was used and for soft voting we used average of probabilities, product of probabilities, maximum of probabilities and minimum of probabilities-based voting methods. The hard voting (majority-based voting) mechanism shows better performance with 99.42%, as compared to the state-of-the-art algorithm for WBCD.

72 citations