An Efficient Total Variation Minimization Method for Image Restoration

Pham, Cong Thang; Tran, Thi Thu Thao; Gamard, Guilhem

doi:10.15388/20-infor407

Cited by 17 publications

(5 citation statements)

References 28 publications

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“…Observed improvement of lymphocyte classification accuracy by applied relatively simple Reinhard stain normalization suggests this part of our workflow can be further explored. Structure-preserving image normalization methods (Vahadane et al, 2016;Mahapatra et al, 2020) demonstrate promising results; also, certain medical image denoising techniques (Meiniel et al, 2018;Pham et al, 2020) could appear useful in future work. Both Janowczyk and Madabhushi (2016) and Alom et al (2019) used the same dataset to train and evaluate their proposed models; therefore, to deal with overfitting, authors had to apply some sort of cross-validation.…”

Section: The Effect Of Colour Normalization On Overall Model Performancementioning

confidence: 99%

Deep Learning Model for Cell Nuclei Segmentation and Lymphocyte Identification in Whole Slide Histology Images

Budginaitė¹,

Morkunas²,

Laurinavičius³

et al. 2021

Informatica

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Anti-cancer immunotherapy dramatically changes the clinical management of many types of tumours towards less harmful and more personalized treatment plans than conventional chemotherapy or radiation. Precise analysis of the spatial distribution of immune cells in the tumourous tissue is necessary to select patients that would best respond to the treatment. Here, we introduce a deep learning-based workflow for cell nuclei segmentation and subsequent immune cell identification in routine diagnostic images. We applied our workflow on a set of hematoxylin and eosin (H&E) stained breast cancer and colorectal cancer tissue images to detect tumour-infiltrating lymphocytes. Firstly, to segment all nuclei in the tissue, we applied the multiple-image input layer architecture (Micro-Net, Dice coefficient (DC) 0.79 ± 0.02). We supplemented the Micro-Net with an introduced texture block to increase segmentation accuracy (DC = 0.80 ± 0.02). We preserved the shallow architecture of the segmentation network with only 280 K trainable parameters (e.g. U-net with ∼1900 K parameters, DC = 0.78 ± 0.03). Subsequently, we added an active contour layer to the ground truth images to further increase the performance (DC = 0.81 ± 0.02). Secondly, to discriminate lymphocytes from the set of all segmented nuclei, we explored multilayer perceptron and achieved a 0.70 classification f-score. Remarkably, the binary classification of segmented nuclei was significantly improved (f-score = 0.80) by colour normalization. To inspect model generalization, we have evaluated trained models on a public dataset that was not put to use during training. We conclude that the proposed workflow achieved promising results and, with little effort, can be employed in multi-class nuclei segmentation and identification tasks.

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Section: The Effect Of Colour Normalization On Overall Model Performancementioning

confidence: 99%

Deep Learning Model for Cell Nuclei Segmentation and Lymphocyte Identification in Whole Slide Histology Images

Budginaitė¹,

Morkunas²,

Laurinavičius³

et al. 2021

Informatica

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“…2. There are many interesting ways of adding noise to original image [27], we consider the well-known distributions such as Gaussian noise with parameter value as 0.01 and Salt & Pepper noise to the original image with parameter value as 0.05. We construct the optimization problems based on the depicted way at (10) for each images.…”

Section: Algorithmmentioning

confidence: 99%

Generalization of hyperbolic smoothing approach for non-smooth and non-Lipschitz functions

Yılmaz

Şahiner

2022

JIMO

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<p style='text-indent:20px;'>In this study, we concentrate on the hyperbolic smoothing technique for some sub-classes of non-smooth functions and introduce a generalization of hyperbolic smoothing technique for non-Lipschitz functions. We present some useful properties of this generalization of hyperbolic smoothing technique. In order to illustrate the efficiency of the proposed smoothing technique, we consider the regularization problems of image restoration. The regularization problem is recast by considering the generalization of hyperbolic smoothing technique and a new algorithm is developed. Finally, the minimization algorithm is applied to image restoration problems and the numerical results are reported.</p>

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“…Suppose that f has size MN, and let {1, …, M}{1, …, N} denote the domain of f. For i , we write f i the pixel of f at position i (and similarly u i the pixel of u at position i) [32]. Then:…”

Section: Preliminariesmentioning

confidence: 99%

Combined total variation of first and fractional orders for Poisson noise removal in digital images

Pham

Tran

Pham

et al. 2021

ICS

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Introduction: Many methods have been proposed to handle the image restoration problem with Poisson noise. A popular approach to Poissonian image reconstruction is the one based on Total Variation. This method can provide significantly sharp edges and visually fine images, but it results in piecewise-constant regions in the resulting images. Purpose: Developing an adaptive total variation-based model for the reconstruction of images contaminated by Poisson noise, and an algorithm for solving the optimization problem. Results: We proposed an effective way to restore images degraded by Poisson noise. Using the Bayesian framework, we proposed an adaptive model based on a combination of first-order total variation and fractional order total variation. The first-order total variation model is efficient for suppressing the noise and preserving the keen edges simultaneously. However, the first-order total variation method usually causes artifact problems in the obtained results. To avoid this drawback, we can use high-order total variation models, one of which is the fractional-order total variation-based model for image restoration. In the fractional-order total variation model, the derivatives have an order greater than or equal to one. It leads to the convenience of computation with a compact discrete form. However, methods based on the fractional-order total variation may cause image blurring. Thus, the proposed model incorporates the advantages of two total variation regularization models, having a significant effect on the edge-preserving image restoration. In order to solve the considered optimization problem, the Split Bregman method is used. Experimental results are provided, demonstrating the effectiveness of the proposed method. Practical relevance: The proposed method allows you to restore Poissonian images preserving their edges. The presented numerical simulation demonstrates the competitive performance of the model proposed for image reconstruction. Discussion: From the experimental results, we can see that the proposed algorithm is effective in suppressing noise and preserving the image edges. However, the weighted parameters in the proposed model were not automatically selected at each iteration of the proposed algorithm. This requires additional research.

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An Efficient Total Variation Minimization Method for Image Restoration

Cited by 17 publications

References 28 publications

Deep Learning Model for Cell Nuclei Segmentation and Lymphocyte Identification in Whole Slide Histology Images

Deep Learning Model for Cell Nuclei Segmentation and Lymphocyte Identification in Whole Slide Histology Images

Generalization of hyperbolic smoothing approach for non-smooth and non-Lipschitz functions

Combined total variation of first and fractional orders for Poisson noise removal in digital images

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