Reducing noise component on medical images

2018 ◽  
Author(s):  
Evgeny A. Semenishchev ◽  
Viacheslav Voronin ◽  
Oksana Balabaeva ◽  
Vladimir Dub
Keyword(s):  
Medical Images ◽  
Noise Component

Wavelet-Based Medical Image Denoising and Enhancement

2012 ◽  
Vol 195-196 ◽  
pp. 515-520
Author(s):  
Hui Qin Jiang ◽  
Zhong Yong Wang ◽  
Ling Ma ◽  
Yu Min Liu ◽  
Ping Li
Keyword(s):  
Image Fusion ◽  
High Frequency ◽  
Medical Image ◽  
Dynamic Range ◽  
Medical Images ◽  
Frequency Component ◽  
Noise Removal ◽  
High Frequency Component ◽  
High Signal ◽  
Noise Component

The visual quality of medical images is an important aspect in PACS implementation. In this study, on the basis of wavelet analysis, a denoising and enhancement algorithm for medical image is proposed. The algorithm mainly includes six steps. At first, an effcient method is investigated for Poisson Noise remove. Second, diagnosis features of the denoised image are enhanced by compressing the dynamic range. Third, we extract the high frequency component of the original image by the designed lowpass filter. Fourth, the extracted high frequency component are segment into diagnosis feature component in the high signal range, the diagnosis feature component in the low signal range, and the noise component. Five, we reconstruct an image using image fusion. Finally, we make DICOM calibration for used display and decide parameters of the image fusion, resulting in the diagnosis image. Experimental results show that this new scheme offers effective noise removal in medical images and enhancing sharpness. More importantly, this scheme can improve the diagnostic value of the display image on the commercial display successfully.

Editor’s Pick: Regulatory and Ethical Issues in the New Era of Radiomics and Radiogenomics

EMJ Radiology ◽  
2020 ◽  
Author(s):  
Filippo Pesapane
Keyword(s):  
Ethical Issues ◽  
Medical Images ◽  
Practical Significance ◽  
Innovative Technology ◽  
New Era ◽  
Naked Eye ◽  
Disease Characteristics ◽  
The Future ◽  
Using Data ◽  
The Relationship

Radiomics is a science that investigates a large number of features from medical images using data-characterisation algorithms, with the aim to analyse disease characteristics that are indistinguishable to the naked eye. Radiogenomics attempts to establish and examine the relationship between tumour genomic characteristics and their radiologic appearance. Although there is certainly a lot to learn from these relationships, one could ask the question: what is the practical significance of radiogenomic discoveries? This increasing interest in such applications inevitably raises numerous legal and ethical questions. In an environment such as the technology field, which changes quickly and unpredictably, regulations need to be timely in order to be relevant.  In this paper, issues that must be solved to make the future applications of this innovative technology safe and useful are analysed.

Noise properties of thin-film Ni-P resistors embedded in printed circuit boards

2013 ◽  
Vol 61 (3) ◽  
pp. 731-735
Author(s):  
A.W. Stadler ◽  
Z. Zawiślak ◽  
W. Stęplewski ◽  
A. Dziedzic
Keyword(s):  
Thin Film ◽  
Printed Circuit Boards ◽  
Circuit Boards ◽  
Noise Component ◽  
Printed Circuit ◽  
Resistance Bridge ◽  
Different Types ◽  
Signal Path ◽  
Resistance Fluctuations ◽  
Made In

Abstract. Noise studies of planar thin-film Ni-P resistors made in/on Printed Circuit Boards, both covered with two different types of cladding or uncladded have been described. The resistors have been made of the resistive-conductive-material (Ohmega-Ply©) of 100 Ώ/sq. Noise of the selected pairs of samples has been measured in the DC resistance bridge with a transformer as the first stage in a signal path. 1/f noise caused by resistance fluctuations has been found to be the main noise component. Parameters describing noise properties of the resistors have been calculated and then compared with the parameters of other previously studied thin- and thick-film resistive materials.

Comparative Study of Different Medical Images with Noise Based On Morphology

2012 ◽  
Vol 1 (2) ◽  
pp. 27-35 ◽  
Author(s):  
V. Kalpana ◽  
T. Surendra Nath ◽  
V. Vijaya Kishore
Keyword(s):  
Comparative Study ◽  
Medical Images

Using the Sonification for Hardly Detectable Details in Medical Images

2019 ◽  
Vol 9 (7) ◽  
pp. p9146
Author(s):  
Veturia Chiroiu ◽  
Ligia Munteanu ◽  
Rodica Ioan ◽  
Ciprian Dragne ◽  
Cristian Rugină ◽  
...  
Keyword(s):  
Medical Images

Algorithms for segmentation and recognition of objects on medical images based on chiarlet transformation and neural networks

2020 ◽  
pp. 35-45
Author(s):  
Y.A. Hamad ◽  
K.V. Simonov ◽  
A.S. Kents
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Computer Vision ◽  
Edge Detection ◽  
Medical Images ◽  
Classification Algorithms ◽  
Visual Data ◽  
Recognition Of Objects

The paper considers general approaches to image processing, analysis of visual data and computer vision. The main methods for detecting features and edges associated with these approaches are presented. A brief description of modern edge detection and classification algorithms suitable for isolating and characterizing the type of pathology in the lungs in medical images is also given.

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