scholarly journals Nonlocal Means-Based Denoising for Medical Images

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Ke Lu ◽  
Ning He ◽  
Liang Li
Keyword(s):  
Medical Imaging ◽  
Medical Images ◽  
Random Noise ◽  
Imaging Analysis ◽  
Low Contrast ◽  
Nonlocal Means ◽  
Noisy Pixel ◽  
Improved Performance ◽  
Adaptive Denoising ◽  
State Of Art

Medical images often consist of low-contrast objects corrupted by random noise arising in the image acquisition process. Thus, image denoising is one of the fundamental tasks required by medical imaging analysis. Nonlocal means (NL-means) method provides a powerful framework for denoising. In this work, we investigate an adaptive denoising scheme based on the patch NL-means algorithm for medical imaging denoising. In contrast with the traditional NL-means algorithm, the proposed adaptive NL-means denoising scheme has three unique features. First, we use a restricted local neighbourhood where the true intensity for each noisy pixel is estimated from a set of selected neighbouring pixels to perform the denoising process. Second, the weights used are calculated thanks to the similarity between the patch to denoise and the other patches candidates. Finally, we apply the steering kernel to preserve the details of the images. The proposed method has been compared with similar state-of-art methods over synthetic and real clinical medical images showing an improved performance in all cases analyzed.

Data Hiding in Digitized Medical Images

2011 ◽  
pp. 29-47
Author(s):  
Mehul S. Raval
Keyword(s):  
Medical Imaging ◽  
Data Hiding ◽  
Medical Images ◽  
Reversible Watermarking ◽  
Security Requirements ◽  
Art Methods ◽  
Medical Domain ◽  
State Of Art

This chapter envelops data hiding techniques applied to medical images for improving their security. It covers types of medical images, their security requirements and types of threats to them. This provides a sufficient background and reasoning for applying data hiding techniques to the medical images. The purpose of this chapter is to study requirements of data hiding techniques with respect to medical imaging and to cover state of art methods in this domain. These techniques are developed from different application perspectives helping to understand their limitations and strengths. The chapter culminates with study of algorithms for reversible watermarking techniques and discussion on future of watermarking in medical domain.

scholarly journals Image Enhancement using Wavelet Fusion for Medical Image Processing

2019 ◽  
Vol 9 (1) ◽  
pp. 2663-2667
Keyword(s):  
Image Enhancement ◽  
Medical Image ◽  
Medical Images ◽  
Discrete Wavelet ◽  
Low Contrast ◽  
Adaptive Fusion ◽  
Improved Performance ◽  
Medical Image Enhancement ◽  
Contrast Image

Medical Image Enhancement Low contrast is the active study area that the obtained pictures suffer from noise and low contrast. Age of capturing equipment, bad illumination circumstances are the low contrast medical images. Thus, techniques of contrast improved performance are used before being used to enhance the contrast of medical images. Within a tiny range of pixel concentrations, contrast improvement algorithms enhance low contrast image. Low contrast image enhancement is accomplished using Equalization of Contrast Limited Adaptive Histogram. CLAHE image enhancement is used to enhance the quality of medical images with low contrast. DWT image, sub-bands such as LL, LH, HL, HH are decomposed. 2D Adaptive fusion image on discrete wavelet transformation is used to fuse the main and CLAHE output images. The efficiency of the output is calculated using merged image entropy and PSNR. It is discovered that the visual content of low contrast medical pictures is enhanced effectively on the basis of 2D DWT and adaptive Fusion.

FEATURES

2016 ◽  
Vol 20 (12) ◽  
pp. 12-22
Keyword(s):  
Medical Imaging ◽  
Fluorescence Microscopy ◽  
Fluorescence Spectroscopy ◽  
Medical Images ◽  
Light Sheet ◽  
The Future ◽  
Light Sheet Fluorescence Microscopy

Scanning the Future of Medical Imaging Putting Numbers into Biology: The Combination of Light Sheet Fluorescence Microscopy and Fluorescence Spectroscopy Abyss Processing – Exploring the Deep in Medical Images

scholarly journals Three-Dimensional Liver Image Segmentation Using Generative Adversarial Networks Based on Feature Restoration

2022 ◽  
Vol 8 ◽  
Author(s):  
Runnan He ◽  
Shiqi Xu ◽  
Yashu Liu ◽  
Qince Li ◽  
Yang Liu ◽  
...  
Keyword(s):  
Medical Imaging ◽  
Random Noise ◽  
Three Dimensional ◽  
Poor Quality ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Liver Segmentation ◽  
Deep Convolutional Neural Networks ◽  
Adversarial Networks ◽  
Liver Region

Medical imaging provides a powerful tool for medical diagnosis. In the process of computer-aided diagnosis and treatment of liver cancer based on medical imaging, accurate segmentation of liver region from abdominal CT images is an important step. However, due to defects of liver tissue and limitations of CT imaging procession, the gray level of liver region in CT image is heterogeneous, and the boundary between the liver and those of adjacent tissues and organs is blurred, which makes the liver segmentation an extremely difficult task. In this study, aiming at solving the problem of low segmentation accuracy of the original 3D U-Net network, an improved network based on the three-dimensional (3D) U-Net, is proposed. Moreover, in order to solve the problem of insufficient training data caused by the difficulty of acquiring labeled 3D data, an improved 3D U-Net network is embedded into the framework of generative adversarial networks (GAN), which establishes a semi-supervised 3D liver segmentation optimization algorithm. Finally, considering the problem of poor quality of 3D abdominal fake images generated by utilizing random noise as input, deep convolutional neural networks (DCNN) based on feature restoration method is designed to generate more realistic fake images. By testing the proposed algorithm on the LiTS-2017 and KiTS19 dataset, experimental results show that the proposed semi-supervised 3D liver segmentation method can greatly improve the segmentation performance of liver, with a Dice score of 0.9424 outperforming other methods.

scholarly journals Effect of morphological algorithms on medical imaging

2021 ◽  
Vol 8 (3) ◽  
pp. 1-8
Author(s):  
Cuong Phan Viet ◽  
Thao Ho Thi ◽  
Anh Le Tuan ◽  
Ha Nguyen Hong ◽  
Thanh Ha Quang
Keyword(s):  
Medical Imaging ◽  
Edge Detection ◽  
Medical Images ◽  
Computer Software ◽  
Diagnosis And Treatment ◽  
Detection Methods ◽  
Morphological Operators

Handling and improving the quality of medical images with the help of computer software is one of the important stages in the diagnosis and treatment. In this article, we focus on describing the new morphological algorithms by ITK (Insight Segmentation and Registration Toolkit). These morphological operators eliminate noise, detect good edges, and overcome the drawback of traditional edge detection methods.

Open Source Applications for Image Visualization and Processing in Neuroimaging Training

2015 ◽  
pp. 1319-1332
Author(s):  
Juan A. Juanes ◽  
Pablo Ruisoto ◽  
Alberto Prats-Galino ◽  
Andrés Framiñán
Keyword(s):  
Magnetic Resonance ◽  
Medical Imaging ◽  
Open Source ◽  
Medical Images ◽  
Three Dimensional ◽  
Mr Images ◽  
Image Visualization ◽  
3D Slicer ◽  
Volumetric Reconstruction ◽  
Key Features

The aim of this paper is to demonstrate the major role and potential of three of the most powerful open source computerized tools for the advanced processing of medical images, in the study of neuroanatomy. DICOM images were acquired with radiodiagnostic equipment using 1.5 Tesla Magnetic Resonance (MR) images. Images were further processed using the following applications: first, OsiriXTM version 4.0 32 bits for OS; Second, 3D Slicer version 4.3; and finally, MRIcron, version 6. Advanced neuroimaging processing requires two key features: segmentation and three-dimensional or volumetric reconstruction. Examples of identification and reconstruction of some of the most complex neuroimaging elements such vascular ones and tractographies are included in this paper. The three selected applications represent some of the most versatile technologies within the field of medical imaging.

scholarly journals Effective Diagnosis and Treatment through Content-Based Medical Image Retrieval (CBMIR) by Using Artificial Intelligence

2019 ◽  
Vol 8 (4) ◽  
pp. 462 ◽  
Author(s):  
Muhammad Owais ◽  
Muhammad Arsalan ◽  
Jiho Choi ◽  
Kang Ryoung Park
Keyword(s):  
Artificial Intelligence ◽  
Medical Imaging ◽  
Image Retrieval ◽  
Medical Image ◽  
Medical Images ◽  
Diagnosis And Treatment ◽  
Imaging Modalities ◽  
Imaging Data ◽  
Medical Database ◽  
Medical Image Retrieval

Medical-image-based diagnosis is a tedious task‚ and small lesions in various medical images can be overlooked by medical experts due to the limited attention span of the human visual system, which can adversely affect medical treatment. However, this problem can be resolved by exploring similar cases in the previous medical database through an efficient content-based medical image retrieval (CBMIR) system. In the past few years, heterogeneous medical imaging databases have been growing rapidly with the advent of different types of medical imaging modalities. Recently, a medical doctor usually refers to various types of imaging modalities all together such as computed tomography (CT), magnetic resonance imaging (MRI), X-ray, and ultrasound, etc of various organs in order for the diagnosis and treatment of specific disease. Accurate classification and retrieval of multimodal medical imaging data is the key challenge for the CBMIR system. Most previous attempts use handcrafted features for medical image classification and retrieval, which show low performance for a massive collection of multimodal databases. Although there are a few previous studies on the use of deep features for classification, the number of classes is very small. To solve this problem, we propose the classification-based retrieval system of the multimodal medical images from various types of imaging modalities by using the technique of artificial intelligence, named as an enhanced residual network (ResNet). Experimental results with 12 databases including 50 classes demonstrate that the accuracy and F1.score by our method are respectively 81.51% and 82.42% which are higher than those by the previous method of CBMIR (the accuracy of 69.71% and F1.score of 69.63%).

scholarly journals Multicomponent MR Image Denoising

2009 ◽  
Vol 2009 ◽  
pp. 1-10 ◽  
Author(s):  
José V. Manjón ◽  
Neil A. Thacker ◽  
Juan J. Lull ◽  
Gracian Garcia-Martí ◽  
Luís Martí-Bonmatí ◽  
...  
Keyword(s):  
Random Noise ◽  
Principal Component ◽  
Magnetic Resonance Images ◽  
Image Resolution ◽  
Single Image ◽  
Mr Images ◽  
Mr Image ◽  
Automatic Feature Extraction ◽  
Improved Performance ◽  
Multicomponent Nature

Magnetic Resonance images are normally corrupted by random noise from the measurement process complicating the automatic feature extraction and analysis of clinical data. It is because of this reason that denoising methods have been traditionally applied to improve MR image quality. Many of these methods use the information of a single image without taking into consideration the intrinsic multicomponent nature of MR images. In this paper we propose a new filter to reduce random noise in multicomponent MR images by spatially averaging similar pixels using information from all available image components to perform the denoising process. The proposed algorithm also uses a local Principal Component Analysis decomposition as a postprocessing step to remove more noise by using information not only in the spatial domain but also in the intercomponent domain dealing in a higher noise reduction without significantly affecting the original image resolution. The proposed method has been compared with similar state-of-art methods over synthetic and real clinical multicomponent MR images showing an improved performance in all cases analyzed.

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