scholarly journals Cross-Modal Guidance Assisted Hierarchical Learning Based Siamese Network for MR Image Denoising

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2855
Author(s):  
Rabia Naseem ◽  
Faouzi Alaya Cheikh ◽  
Azeddine Beghdadi ◽  
Khan Muhammad ◽  
Muhammad Sajjad
Keyword(s):  
Deep Learning ◽  
Medical Imaging ◽  
Medical Image Analysis ◽  
Similarity Index ◽  
Structural Similarity ◽  
Magnetic Resonance Images ◽  
Perceptual Quality ◽  
Hierarchical Learning ◽  
Siamese Network ◽  
The Impact

Cross-modal medical imaging techniques are predominantly being used in the clinical suite. The ensemble learning methods using cross-modal medical imaging adds reliability to several medical image analysis tasks. Motivated by the performance of deep learning in several medical imaging tasks, a deep learning-based denoising method Cross-Modality Guided Denoising Network CMGDNet for removing Rician noise in T1-weighted (T1-w) Magnetic Resonance Images (MRI) is proposed in this paper. CMGDNet uses a guidance image, which is a cross-modal (T2-w) image of better perceptual quality to guide the model in denoising its noisy T1-w counterpart. This cross-modal combination allows the network to exploit complementary information existing in both images and therefore improve the learning capability of the model. The proposed framework consists of two components: Paired Hierarchical Learning (PHL) module and Cross-Modal Assisted Reconstruction (CMAR) module. PHL module uses Siamese network to extract hierarchical features from dual images, which are then combined in a densely connected manner in the CMAR module to finally reconstruct the image. The impact of using registered guidance data is investigated in removing noise as well as retaining structural similarity with the original image. Several experiments were conducted on two publicly available brain imaging datasets available on the IXI database. The quantitative assessment using Peak Signal to noise ratio (PSNR), Structural Similarity Index (SSIM), and Feature Similarity Index (FSIM) demonstrates that the proposed method exhibits 4.7% and 2.3% gain (average), respectively, in SSIM and FSIM values compared to other state-of-the-art denoising methods that do not integrate cross-modal image information in removing various levels of noise.

ANALISIS SENSITIVITAS VIDEO MPEG-4 BERDASARKAN STRUKTUR FRAME PADA TRANSMISI DVB-T

2020 ◽  
Vol 25 (2) ◽  
pp. 86-97
Author(s):  
Sandy Suryo Prayogo ◽  
Tubagus Maulana Kusuma
Keyword(s):  
Deep Learning ◽  
Bit Error Rate ◽  
Error Rate ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Signal To Noise ◽  
Structural Similarity Index ◽  
Noise Ratio

DVB merupakan standar transmisi televisi digital yang paling banyak digunakan saat ini. Unsur terpenting dari suatu proses transmisi adalah kualitas gambar dari video yang diterima setelah melalui proses transimisi tersebut. Banyak faktor yang dapat mempengaruhi kualitas dari suatu gambar, salah satunya adalah struktur frame dari video. Pada tulisan ini dilakukan pengujian sensitifitas video MPEG-4 berdasarkan struktur frame pada transmisi DVB-T. Pengujian dilakukan menggunakan simulasi matlab dan simulink. Digunakan juga ffmpeg untuk menyediakan format dan pengaturan video akan disimulasikan. Variabel yang diubah dari video adalah bitrate dan juga group-of-pictures (GOP), sedangkan variabel yang diubah dari transmisi DVB-T adalah signal-to-noise-ratio (SNR) pada kanal AWGN di antara pengirim (Tx) dan penerima (Rx). Hasil yang diperoleh dari percobaan berupa kualitas rata-rata gambar pada video yang diukur menggunakan metode pengukuran structural-similarity-index (SSIM). Dilakukan juga pengukuran terhadap jumlah bit-error-rate BER pada bitstream DVB-T. Percobaan yang dilakukan dapat menunjukkan seberapa besar sensitifitas bitrate dan GOP dari video pada transmisi DVB-T dengan kesimpulan semakin besar bitrate maka akan semakin buruk nilai kualitas gambarnya, dan semakin kecil nilai GOP maka akan semakin baik nilai kualitasnya. Penilitian diharapkan dapat dikembangkan menggunakan deep learning untuk memperoleh frame struktur yang tepat di kondisi-kondisi tertentu dalam proses transmisi televisi digital.

scholarly journals Data augmentation for computed tomography angiography via synthetic image generation and neural domain adaptation

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Malte Seemann ◽  
Lennart Bargsten ◽  
Alexander Schlaefer
Keyword(s):  
Computed Tomography ◽  
Neural Networks ◽  
Deep Learning ◽  
Medical Imaging ◽  
Computed Tomography Angiography ◽  
Data Augmentation ◽  
Domain Adaptation ◽  
Synthetic Image ◽  
Wide Range ◽  
The Impact

AbstractDeep learning methods produce promising results when applied to a wide range of medical imaging tasks, including segmentation of artery lumen in computed tomography angiography (CTA) data. However, to perform sufficiently, neural networks have to be trained on large amounts of high quality annotated data. In the realm of medical imaging, annotations are not only quite scarce but also often not entirely reliable. To tackle both challenges, we developed a two-step approach for generating realistic synthetic CTA data for the purpose of data augmentation. In the first step moderately realistic images are generated in a purely numerical fashion. In the second step these images are improved by applying neural domain adaptation. We evaluated the impact of synthetic data on lumen segmentation via convolutional neural networks (CNNs) by comparing resulting performances. Improvements of up to 5% in terms of Dice coefficient and 20% for Hausdorff distance represent a proof of concept that the proposed augmentation procedure can be used to enhance deep learning-based segmentation for artery lumen in CTA images.

scholarly journals Implementation of Iterative bilateral filtering for removal of Rician noise in MR images using FPGA

2020 ◽  
Vol 9 (3) ◽  
pp. 279-284
Keyword(s):  
Magnetic Resonance ◽  
Real Time ◽  
Visual Analysis ◽  
Similarity Index ◽  
Structural Similarity ◽  
Bilateral Filter ◽  
Magnetic Resonance Images ◽  
Rician Noise ◽  
Diagnostic Quality ◽  
Index Matrix

Magnetic resonance image noise reduction is important to process further and visual analysis. Bilateral filter is denoises image and also preserves edge. It proposes Iterative bilateral filter which reduces Rician noise in the magnitude magnetic resonance images and retains the fine structures, edges and it also reduces the bias caused by Rician noise. The visual and diagnostic quality of the image is retained. The quantitative analysis is based on analysis of standard quality metrics parameters like peak signal-to-noise ratio and mean structural similarity index matrix reveals that these methods yields better results than the other proposed denoising methods for MRI. Problem associated with the method is that it is computationally complex hence time consuming. It is not recommended for real time applications. To use in real time application a parallel implantation of the same using FPGA is proposed.

Deep Learning and Medical Imaging

2019 ◽  
pp. 101-147
Author(s):  
Nourhan Mohamed Zayed ◽  
Heba A. Elnemr
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Medical Imaging ◽  
Medical Image Analysis ◽  
Future Research ◽  
Research Directions ◽  
Conventional Machine ◽  
Future Research Directions ◽  
Nested Hierarchy ◽  
Great Potency

Deep learning (DL) is a special type of machine learning that attains great potency and flexibility by learning to represent input raw data as a nested hierarchy of essences and representations. DL consists of more layers than conventional machine learning that permit higher levels of abstractions and improved prediction from data. More abstract representations computed in terms of less abstract ones. The goal of this chapter is to present an intensive survey of existing literature on DL techniques over the last years especially in the medical imaging analysis field. All these techniques and algorithms have their points of interest and constraints. Thus, analysis of various techniques and transformations, submitted prior in writing, for plan and utilization of DL methods from medical image analysis prospective will be discussed. The authors provide future research directions in DL area and set trends and identify challenges in the medical imaging field. Furthermore, as quantity of medicinal application demands increase, an extended study and investigation in DL area becomes very significant.

scholarly journals Deep Learning Image Processing Enables 40% Faster Spinal MR Scans Which Match or Exceed Quality of Standard of Care

2021 ◽  
Author(s):  
S. Bash ◽  
B. Johnson ◽  
W. Gibbs ◽  
T. Zhang ◽  
A. Shankaranarayanan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Standard Of Care ◽  
Structural Similarity ◽  
Image Features ◽  
Scan Time ◽  
Magnetic Resonance Imaging Mri ◽  
Display Order ◽  
Spine Mri

Abstract Objective This prospective multicenter multireader study evaluated the performance of 40% scan-time reduced spinal magnetic resonance imaging (MRI) reconstructed with deep learning (DL). Methods A total of 61 patients underwent standard of care (SOC) and accelerated (FAST) spine MRI. DL was used to enhance the accelerated set (FAST-DL). Three neuroradiologists were presented with paired side-by-side datasets (666 series). Datasets were blinded and randomized in sequence and left-right display order. Image features were preference rated. Structural similarity index (SSIM) and per pixel L1 was assessed for the image sets pre and post DL-enhancement as a quantitative assessment of image integrity impact. Results FAST-DL was qualitatively better than SOC for perceived signal-to-noise ratio (SNR) and artifacts and equivalent for other features. Quantitative SSIM was high, supporting the absence of image corruption by DL processing. Conclusion DL enables 40% spine MRI scan time reduction while maintaining diagnostic integrity and image quality with perceived benefits in SNR and artifact reduction, suggesting potential for clinical practice utility.

scholarly journals Survey on deep learning for pulmonary medical imaging

2019 ◽  
Vol 14 (4) ◽  
pp. 450-469 ◽  
Author(s):  
Jiechao Ma ◽  
Yang Song ◽  
Xi Tian ◽  
Yiting Hua ◽  
Rongguo Zhang ◽  
...  
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Medical Imaging ◽  
Language Processing ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Rapid Evolution ◽  
Comprehensive Overview ◽  
Feature Representations ◽  
Learning Techniques

AbstractAs a promising method in artificial intelligence, deep learning has been proven successful in several domains ranging from acoustics and images to natural language processing. With medical imaging becoming an important part of disease screening and diagnosis, deep learning-based approaches have emerged as powerful techniques in medical image areas. In this process, feature representations are learned directly and automatically from data, leading to remarkable breakthroughs in the medical field. Deep learning has been widely applied in medical imaging for improved image analysis. This paper reviews the major deep learning techniques in this time of rapid evolution and summarizes some of its key contributions and state-of-the-art outcomes. The topics include classification, detection, and segmentation tasks on medical image analysis with respect to pulmonary medical images, datasets, and benchmarks. A comprehensive overview of these methods implemented on various lung diseases consisting of pulmonary nodule diseases, pulmonary embolism, pneumonia, and interstitial lung disease is also provided. Lastly, the application of deep learning techniques to the medical image and an analysis of their future challenges and potential directions are discussed.

Integrating Morphological Edge Detection and Mutual Information for Nonrigid Registration of Medical Images

2019 ◽  
Vol 15 (3) ◽  
pp. 292-300 ◽  
Author(s):  
Vivek Aggarwal ◽  
Anupama Gupta
Keyword(s):  
Mutual Information ◽  
Edge Detection ◽  
Visual Information ◽  
Medical Images ◽  
Similarity Index ◽  
Structural Similarity ◽  
Magnetic Resonance Images ◽  
Registration Accuracy ◽  
Measure Function ◽  
Improved Performance

Background: Medical images are widely used within healthcare and medical research. There is an increased interest in precisely correlating information in these images through registration techniques for investigative and therapeutic purposes. This work proposes and evaluates an improved measure function for registration of carotid ultrasound and magnetic resonance images (MRI) taken at different times. Methods: To achieve this, a morphological edge detection operator has been designed to extract the vital edge information from images which is integrated with the Mutual Information (MI) to carry out the registration process. The improved performance of proposed registration measure function is demonstrated using four quality metrics: Correlation Coefficient (CC), Structural Similarity Index (SSIM), Visual Information Fidelity (VIF) and Gradient Magnitude Similarity Deviation (GMSD). The qualitative validation has also been done through visual inspection of the registered image pairs by clinical radiologists. Results: The experimental results showed that the proposed method outperformed the existing method (based on integrated MI and standard edge detection) for both ultrasound and MR images in terms of CC by about 4.67%, SSIM by 3.21%, VIF by 18.5%, and decreased GMSD by 37.01%. Whereas, in comparison to the standard MI based method, the proposed method has increased CC by 16.29%, SSIM by 16.13%, VIF by 52.56% and decreased GMSD by 66.06%, approximately. Conclusion: Thus, the proposed method improves the registration accuracy when the original images are corrupted by noise, have low intensity values or missing data.

scholarly journals Training confounder-free deep learning models for medical applications

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Qingyu Zhao ◽  
Ehsan Adeli ◽  
Kilian M. Pohl
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Bone Age ◽  
Magnetic Resonance Images ◽  
Data Sets ◽  
Large Set ◽  
Learning Models ◽  
End To End ◽  
The Impact ◽  
The Relationship

AbstractThe presence of confounding effects (or biases) is one of the most critical challenges in using deep learning to advance discovery in medical imaging studies. Confounders affect the relationship between input data (e.g., brain MRIs) and output variables (e.g., diagnosis). Improper modeling of those relationships often results in spurious and biased associations. Traditional machine learning and statistical models minimize the impact of confounders by, for example, matching data sets, stratifying data, or residualizing imaging measurements. Alternative strategies are needed for state-of-the-art deep learning models that use end-to-end training to automatically extract informative features from large set of images. In this article, we introduce an end-to-end approach for deriving features invariant to confounding factors while accounting for intrinsic correlations between the confounder(s) and prediction outcome. The method does so by exploiting concepts from traditional statistical methods and recent fair machine learning schemes. We evaluate the method on predicting the diagnosis of HIV solely from Magnetic Resonance Images (MRIs), identifying morphological sex differences in adolescence from those of the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA), and determining the bone age from X-ray images of children. The results show that our method can accurately predict while reducing biases associated with confounders. The code is available at https://github.com/qingyuzhao/br-net.

scholarly journals The ANTsX ecosystem for quantitative biological and medical imaging

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Nicholas J. Tustison ◽  
Philip A. Cook ◽  
Andrew J. Holbrook ◽  
Hans J. Johnson ◽  
John Muschelli ◽  
...  
Keyword(s):  
Deep Learning ◽  
Medical Imaging ◽  
Open Source Software ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Brain Mri ◽  
Network Architectures ◽  
Imaging Data ◽  
Software Libraries ◽  
Learning Capabilities

AbstractThe Advanced Normalizations Tools ecosystem, known as ANTsX, consists of multiple open-source software libraries which house top-performing algorithms used worldwide by scientific and research communities for processing and analyzing biological and medical imaging data. The base software library, ANTs, is built upon, and contributes to, the NIH-sponsored Insight Toolkit. Founded in 2008 with the highly regarded Symmetric Normalization image registration framework, the ANTs library has since grown to include additional functionality. Recent enhancements include statistical, visualization, and deep learning capabilities through interfacing with both the R statistical project (ANTsR) and Python (ANTsPy). Additionally, the corresponding deep learning extensions ANTsRNet and ANTsPyNet (built on the popular TensorFlow/Keras libraries) contain several popular network architectures and trained models for specific applications. One such comprehensive application is a deep learning analog for generating cortical thickness data from structural T1-weighted brain MRI, both cross-sectionally and longitudinally. These pipelines significantly improve computational efficiency and provide comparable-to-superior accuracy over multiple criteria relative to the existing ANTs workflows and simultaneously illustrate the importance of the comprehensive ANTsX approach as a framework for medical image analysis.

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