scholarly journals Noise Issues Prevailing in Various Types of Medical Images

2018 ◽  
Vol 11 (3) ◽  
pp. 1227-1237 ◽  
Author(s):  
Bhawna Goyal ◽  
Ayush Dogra ◽  
Sunil Agrawal ◽  
B.S. Sohi

The current literature documents a plethora of image denoising techniques in the fields of medical imaging, remote sensing, biometrics, surveillance and vegetation mapping. Therefore it is important to have brief insight into various types of noises in different type of images, for instance medical images, remote sensing images and natural images. This article encompasses the basic definition, history, usage and type of noise affecting some of the major types of imaging modalities. Besides this a brief discussion on the type of noise prevailing in remote sensing and natural images is also given. While designing an effective image denoising algorithm, one needs to be acquainted with the prior information about the noise prevalent in various types of images. Further, a brief idea about the basic principle, outlook, contrast levels and application of medical imaging modalities has also been presented in the context of this article.­­­­­­

2019 ◽  
Vol 8 (4) ◽  
pp. 462 ◽  
Author(s):  
Muhammad Owais ◽  
Muhammad Arsalan ◽  
Jiho Choi ◽  
Kang Ryoung Park

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%).


2019 ◽  
Vol 63 (7) ◽  
pp. 1084-1098
Author(s):  
Haijiang Wang ◽  
Jingpu Wang ◽  
Fuqi Yao ◽  
Yongqiang Ma ◽  
Lihong Li ◽  
...  

Abstract The ability to remove noise from remote sensing images, while retaining the important features of the images, is becoming increasingly important. In this paper, we introduce the multi-band contourlet transform, a new method for adaptively denoising remote sensing images. We describe existing methods that use multi-resolution analysis transforms for denoising images and discuss their respective advantages and disadvantages. We then introduce our novel denoising method, which exploits the advantages of existing methods. We summarize the results of a comprehensive set of experiments designed to evaluate the performance of our method and compare it with the performance of existing methods. The results demonstrate that our method is superior to existing methods, both in terms of its ability to denoise images and to retain salient features of those images following denoising.


2012 ◽  
Vol 03 (04) ◽  
pp. 475-487 ◽  
Author(s):  
Y. Ge ◽  
H.T. Nguyen ◽  
T.A. Arcury ◽  
A.J. Johnson ◽  
W. Hwang ◽  
...  

SummaryBackground: Scant knowledge exists describing health care providers’ and staffs’ experiences sharing imaging studies. Additional research is needed to determine the extent to which imaging studies are shared in diverse health care settings, and the extent to which provider or practice characteristics are associated with barriers to viewing external imaging studies on portable media.Objective: This analysis uses qualitative data to 1) examine how providers and their staff accessed outside medical imaging studies, 2) examine whether use or the desire to use imaging studies conducted at outside facilities varied by provider specialty or location (urban, suburban, and small town) and 3) delineate difficulties experienced by providers or staff as they attempted to view and use imaging studies available on portable media.Methods: Semi-structured interviews were conducted with 85 health care providers and medical facility staff from urban, suburban, and small town medical practices in North Carolina and Virginia. The interviews were audio recorded, transcribed, then systematically analyzed using ATLAS.ti.Results: Physicians at family and pediatric medicine practices rely primarily on written reports for medical studies other than X-rays; and thus do not report difficulties accessing outside imaging studies. Subspecialists in urban, suburban, and small towns view imaging studies through internal communication systems, internet portals, or portable media. Many subspecialists and their staff report experiencing difficulty and time delays in accessing and using imaging studies on portable media.Conclusion: Subspecialists have distinct needs for viewing imaging studies that are not shared by typical primary care providers. As development and implementation of technical strategies to share medical records continue, this variation in need and use should be noted. The sharing and viewing of medical imaging studies on portable media is often inefficient and fails to meet the needs of many subspeciality physicians, and can lead to repeated imaging studies.Citation: Sandberg JC, Ge Y, Nguyen HT, Arcury TA, Johnson AJ, Hwang W, Gage HD, Reynolds T, Carr JJ. Insight into the sharing of medical images. Physician, other health care providers, and staff experience in a variety of medical settings. Appl Clin Inf 2012; 3: 475–487http://dx.doi.org/10.4338/ACI-2012-06-RA-0022


Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2618
Author(s):  
Qifan Wu ◽  
Daqiang Feng ◽  
Changqing Cao ◽  
Xiaodong Zeng ◽  
Zhejun Feng ◽  
...  

In recent years, remote sensing images has become one of the most popular directions in image processing. A small feature gap exists between satellite and natural images. Therefore, deep learning algorithms could be applied to recognize remote sensing images. We propose an improved Mask R-CNN model, called SCMask R-CNN, to enhance the detection effect in the high-resolution remote sensing images which contain the dense targets and complex background. Our model can perform object recognition and segmentation in parallel. This model uses a modified SC-conv based on the ResNet101 backbone network to obtain more discriminative feature information and adds a set of dilated convolutions with a specific size to improve the instance segmentation effect. We construct WFA-1400 based on the DOTA dataset because of the shortage of remote sensing mask datasets. We compare the improved algorithm with other state-of-the-art algorithms. The object detection AP50 and AP increased by 1–2% and 1%, respectively, objectively proving the effectiveness and the feasibility of the improved model.


2015 ◽  
Vol 74 (20) ◽  
pp. 1803-1821 ◽  
Author(s):  
V. V. Lukin ◽  
S. K. Abramov ◽  
R.A. Kozhemiakin ◽  
Benoit Vozel ◽  
B. Djurovic ◽  
...  

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