RETRACTED ARTICLE: Medical Image Enhancement by a Bilateral Filter Using Optimization Technique

Anoop, V; Bipin, P. R.

doi:10.1007/s10916-019-1370-x

Cited by 37 publications

(11 citation statements)

References 31 publications

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“…A bilateral filter is applied to the input patch. The bilateral filter is a non-linear filter which reduces noise and preserves edge [43]. In this filter, the intensity value of each pixel is replaced by a weighted average of nearby pixels as follows:…”

Section: System I (Finding Critical Nuclei + Cnn-based System)mentioning

confidence: 99%

“…A bilateral filter is applied to the input patch. The bilateral filter is a non‐linear filter which reduces noise and preserves edge [43]. In this filter, the intensity value of each pixel is replaced by a weighted average of nearby pixels as follows:

I^{f i l t e r e d} = \frac{1}{W_{p}} \sum_{q_{i} ϵ normalΩ} I (q_{i}) G_{σ_{s}} (false‖ q_{i} badbreak- q false‖) G_{σ_{r}} (false‖ I (q_{i}) badbreak- I (q) false‖)

W_{p} = \sum_{q_{i} ϵ normalΩ} G_{σ_{s}} (false‖ q_{i} badbreak- q false‖) G_{σ_{r}} (false‖ I (q_{i}) badbreak- I (q) false‖)

where

I^{f i l t e r e d}

is the filtered image,

I

is the original image,

q

is the coordinates of the current pixel,

normalΩ

is the window centred in

q

G_{σ_{r}}

is the range kernel for smoothing intensity differences and

G_{σ_{s}}

…”

Section: The Proposed Systemsmentioning

confidence: 99%

See 1 more Smart Citation

Nuclear atypia grading in breast cancer histopathological images based on CNN feature extraction and LSTM classification

Jafarbigloo

Danyali

2021

CAAI Trans on Intel Tech

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Early diagnosis of breast cancer, the most common disease among women around the world, increases the chance of treatment and is highly important. Nuclear atypia grading in histopathological images plays an important role in the final diagnosis and grading of breast cancer. Grading images by pathologists is a time consuming and subjective task. Therefore, the existence of a computer-aided system for nuclear atypia grading is very useful and necessary. In this study, two automatic systems for grading nuclear atypia in breast cancer histopathological images based on deep learning methods are proposed. A patch-based approach is introduced due to the large size of the histopathological images and restriction of the training data. In the proposed system I, the most important patches in the image are detected first and then a three-hidden-layer convolutional neural network (CNN) is designed and trained for feature extraction and to classify the patches individually. The proposed system II is based on a combination of the CNN for feature extraction and a twolayer Long short-term memory (LSTM) network for classification. The LSTM network is utilised to consider all patches of an image simultaneously for image grading. The simulation results show the efficiency of the proposed systems for automatic nuclear atypia grading and outperform the current related studies in the literature. K E Y W O R D S breast cancer, CNN, histopathological image, LSTM networks, nuclear atypia | INTRODUCTIONBreast cancer is caused due to an outgrowth of abnormal cells in the breast. This disease is the second leading cause of death among women around the world [1] and is a serious problem for most human societies. Early diagnosis of this disease increases the chance of efficient treatment. After performing mammography and ultrasound imaging, if abnormal tissues are found, a biopsy examination is required. In this test, a small sample of the breast tissue is placed under a microscope to acquire histopathological images for further investigation. Histopathology refers to the visual examination of tissue [2]. Histopathological images are available in digital format which is analysed to detect the type and grade of cancer [3]. Various magnifications of histological images are available such as 10X, 20X and 40X.Traditional methods for grading breast cancer are manually performed by pathologists. Pathologists examine histologicalThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: System I (Finding Critical Nuclei + Cnn-based System)mentioning

confidence: 99%

I^{f i l t e r e d} = \frac{1}{W_{p}} \sum_{q_{i} ϵ normalΩ} I (q_{i}) G_{σ_{s}} (false‖ q_{i} badbreak- q false‖) G_{σ_{r}} (false‖ I (q_{i}) badbreak- I (q) false‖)

W_{p} = \sum_{q_{i} ϵ normalΩ} G_{σ_{s}} (false‖ q_{i} badbreak- q false‖) G_{σ_{r}} (false‖ I (q_{i}) badbreak- I (q) false‖)

where

I^{f i l t e r e d}

is the filtered image,

I

is the original image,

q

is the coordinates of the current pixel,

normalΩ

is the window centred in

q

G_{σ_{r}}

is the range kernel for smoothing intensity differences and

G_{σ_{s}}

…”

Section: The Proposed Systemsmentioning

confidence: 99%

Nuclear atypia grading in breast cancer histopathological images based on CNN feature extraction and LSTM classification

Jafarbigloo

Danyali

2021

CAAI Trans on Intel Tech

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show abstract

“…Anoop et al [11] proposed improved grasshopper simulation methodology which is used to refine the BF variables for obtained input images. The occurred impulse and Rician noises are with varying differences in order to get the filter specifications efficiently.…”

Section: Related Workmentioning

confidence: 99%

“…i) The Iso-lateral technique provides segmentation with definite textures and color patches without any prior knowledge about regions whereas the existing pre-processing technique focusses the noise as in [11].…”

Section: Proposed Research Work -Contributionmentioning

confidence: 99%

Diagnosis of Brain Tumor Using Nano Segmentation and Advanced-cnn Classification

DEEPA¹,

S²,

Geisa

2020

Preprint

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Background: In recent days, the field of nano-technology is becoming popular due to increased rate of accurate detection and effectiveness in clinical patients using Computer-Aided Diagnosis (CAD). The high rate of precision with accuracy and classification of brain tumor as benign or malignant can be achieved with nano-scale imaging technology. This helps to enhance the superiority of life for brain tumor diseased patients. Results: In this work, we propose the novel Semantic nano-segmentation for the detection of brain tumors even in nano scale range. Proposed Semantic Nano-segmentation based on Advanced - Convolutional Neural Networks will help the radiologists to find the brain cancer even at early stages with nodules at very smaller size. The proposed method of Advanced - Convolutional Neural Networks (A-CNN) uses ResNet-50. Here the nano-image is taken as input and tumor image is segmented using Semantic Nano-segmentation which carries an average dice and SSIM values to be 0.2133 and 0.9704 respectively. The accuracy of 93.2% and 92.7% is obtained by the proposed Semantic nano segmentation for benign and malignant tumor images respectively. A-CNN method of automatic classification has an average accuracy of 99.57% and 95.7% for benign and malignant images respectively. Conclusion: This novel nano-method is created for effective detection of tumor area in nanometers (nm) and thus evaluates the disease perfectly. Closeness of the Proposed method at ROC curve with reference to True Positive values indicates higher performance than other methods. Comparative analysis on ResNet-50 with testing and training data at rate of 90% -10%, 80%-20% and 70%-30% respectively is made which proves the effectiveness of the proposed work.

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“…However, hyperparameters-fundamentally determining the filter performance-usually have to be hand-picked. Multiple works have been proposed aiming for automatically finding optimal filter parameters 22,23,24,25,26 using risk estimators or grasshopper optimization. All these techniques are not suitable for integration into an endto-end, data-driven, optimization pipeline and are based on different statistical assumptions on the noise.…”

Section: Introductionmentioning

confidence: 99%

Ultra Low-Parameter Denoising: Trainable Bilateral Filter Layers in Computed Tomography

Wagner¹,

Thies²,

Gu³

et al. 2022

Preprint

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Background: Computed tomography is widely used as an imaging tool to visualize three-dimensional structures with expressive bone-soft tissue contrast. However, CT resolution and radiation dose are tightly entangled, highlighting the importance of lowdose CT combined with sophisticated denoising algorithms. Purpose: Most data-driven denoising techniques are based on deep neural networks and, therefore, contain hundreds of thousands of trainable parameters, making them incomprehensible and prone to prediction failures. Developing understandable and robust denoising algorithms achieving state-of-the-art performance helps to minimize radiation dose while maintaining data integrity. Methods: This work presents an open-source CT denoising framework based on the idea of bilateral filtering. We propose a bilateral filter that can be incorporated into a deep learning pipeline and optimized in a purely data-driven way by calculating the gradient flow toward its hyperparameters and its input. Denoising in pure image-to-image pipelines and across different domains such as raw detector data and reconstructed volume, using a differentiable backprojection layer, is demonstrated. Results:Although only using three spatial parameters and one range parameter per filter layer, the proposed denoising pipelines can compete with deep state-of-the-art denoising architectures with several hundred thousand parameters. Competitive denoising performance is achieved on x-ray microscope bone data (0.7053 and 33.10) and the 2016 Low Dose CT Grand Challenge dataset (0.9674 and 43.07) in terms of SSIM and PSNR.

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RETRACTED ARTICLE: Medical Image Enhancement by a Bilateral Filter Using Optimization Technique

Cited by 37 publications

References 31 publications

Nuclear atypia grading in breast cancer histopathological images based on CNN feature extraction and LSTM classification

Nuclear atypia grading in breast cancer histopathological images based on CNN feature extraction and LSTM classification

Diagnosis of Brain Tumor Using Nano Segmentation and Advanced-cnn Classification

Ultra Low-Parameter Denoising: Trainable Bilateral Filter Layers in Computed Tomography

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