Enhancement and denoising method for low-quality MRI, CT images via the sequence decomposition Retinex model, and haze removal algorithm

2021 ◽  
Author(s):  
Lei Chen ◽  
Chen Tang ◽  
Min Xu ◽  
Zhenkun Lei
Keyword(s):  
Ct Images ◽  
Denoising Method ◽  
Haze Removal

scholarly journals A Method of CT Image Denoising Based on Residual Encoder-Decoder Network

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yali Liu
Keyword(s):  
Image Denoising ◽  
Loss Function ◽  
Radiation Risk ◽  
Ct Images ◽  
Algorithm Complexity ◽  
Ct Image ◽  
Experimental Conditions ◽  
Denoising Method ◽  
Data Set ◽  
Image Domain

Low-dose computed tomography (CT) has proved effective in lowering radiation risk for the patients, but the resultant noise and bar artifacts in CT images can be a disturbance for medical diagnosis. The difficulty of modeling statistical features in the image domain makes it impossible for the existing methods that directly process reconstructed images to maintain the detailed texture structure of images while reducing noise, which accounts for the failure in CT diagnostic images in practical application. To overcome this defect, this paper proposes a CT image-denoising method based on an improved residual encoder-decoder network. Firstly, in our approach, the notion of recursion is integrated into the original residual encoder-decoder network to lower the algorithm complexity and boost efficiency in image denoising. The original CT images and the postrecursion result graph output after recursion are used as the input for the next recursion simultaneously, and the shallow encoder-decoder network is recycled. Secondly, the root-mean-square error loss function and perceptual loss function are introduced to ensure the texture of denoised CT images. On this basis, the tissue processing technology based on clustering segmentation is optimized considering that the images after improved RED-CNN training will still have certain artifacts. Finally, the experimental results of the TCGA-COAD clinical data set show that under the same experimental conditions, our method outperforms WGAN in average postdenoising PSNR and SSIM of CT images. Moreover, with a lower algorithm complexity and shorter execution time, our method is a significant improvement on RED-CNN and is applicable for actual scenarios.

scholarly journals Low-Dose CT Image Denoising using Image Decomposition and Sparse Representation

2019 ◽  
Author(s):  
Nguyen Thanh Trung ◽  
Trinh Dinh Hoan ◽  
Nguyen Linh Trung ◽  
Marie Luong
Keyword(s):  
Low Dose ◽  
Imaging Modality ◽  
Image Decomposition ◽  
Ct Images ◽  
Noisy Image ◽  
Denoising Method ◽  
X Ray ◽  
Low Dose Ct ◽  
Noise Data ◽  
X Ray Computed

X-ray computed tomography (CT) is now a widely used imaging modality for numerous medical purposes. The risk of high X-ray radiation may induce genetic, cancerous and other diseases, demanding the development of new image processing methods that are able to enhance the quality of low-dose CT images. However, lowering the radiation dose increases the noise in acquired images and hence affects important diagnostic information. This paper contributes an efficient denoising method for low-dose CT images. A noisy image is decomposed into three component images of low, medium and high frequency bands; noise is mainly presented in the medium and high component images. Then, by exploiting the fact that a small image patch of the noisy image can be approximated by a linear combination of several elements in a given dictionary of noise-free image patches generated from noise-free images taken at nearly the same position with the noisy image, noise in these medium and high component images are effectively eliminated.Specifically, we give new solutions for image decomposition to easily control the filter parameters, for dictionary construction to improve the effectiveness and reduce the running-time. Instead of using a large dataset of patches, only a structured small part of patches extracted from the raw data is used to form a dictionary, to be used in sparse coding. In addition, we illustrate the effectiveness of the proposed method in preserving image details which are subtle but clinically important. Experimental results conducted on both synthetic and real noise data demonstrate that the proposed method is competitive with the state-of-the-art methods.

Dilatation of Hemashield prostheses – a new method for measuring the true prosthetic diameter based on ECG-gated multislice CT images

2013 ◽  
Vol 61 (S 01) ◽  
Author(s):  
M Hamiko ◽  
M Endlich ◽  
C Krämer ◽  
C Probst ◽  
A Welz ◽  
...  
Keyword(s):  
Multislice Ct ◽  
Ct Images ◽  
New Method

Fully automatic quotient image generation using V/Q SPECT/CT images

2019 ◽  
Author(s):  
K Herdinai ◽  
S Urbán ◽  
Z Besenyi ◽  
L Pávics ◽  
N Zsótér ◽  
...  
Keyword(s):  
Ct Images ◽  
Image Generation ◽  
Fully Automatic

Fully automatic lung lobe segmentation using V/Q SPECT/CT images

2020 ◽  
Author(s):  
A Király ◽  
S Urbán ◽  
Z Besenyi ◽  
L Pávics ◽  
N Zsótér ◽  
...  
Keyword(s):  
Ct Images ◽  
Lung Lobe ◽  
Fully Automatic ◽  
Lobe Segmentation

Pseudocolor Transformation for the Visualization of Stapes in CT Images

Skull Base ◽  
2007 ◽  
Vol 16 (S 2) ◽  
Author(s):  
Moon Suh Park ◽  
Jae Yong Byun ◽  
Seung Gun Yeo ◽  
Chang Il Cha
Keyword(s):  
Ct Images

Clinical Manifestations and Radiological Characteristics on Rosai-Dorfman Disease: A Retrospective Observational Study

2020 ◽  
Vol 5 (5) ◽  
Keyword(s):  
Blood Vessels ◽  
Abdominal Cavity ◽  
Clinical Manifestations ◽  
Ct Images ◽  
High Signal ◽  
Radiological Features ◽  
Imaging Characteristics ◽  
Rosai Dorfman Disease ◽  
Heterogeneous Enhancement ◽  
The Masses

Background and Objective: Rosai-Dorfman disease (RDD) are usually misdiagnosed because of rarity and nonspecific clinical and radiological features. The aim of our study is to explore the clinical and imaging characteristics of RDD to improve diagnostic accuracy. Methods: Clinical and imaging data in 10 patients with RDD were retrospectively analyzed. 7 patients were underwent CT scanning and 3 patients were underwent MR examination. Results: 8 (8/10) patients presented with painless enlarged lymph nodes (LNs) or mass. 3 cases were involved with LNs, 5 cases were involved with extra-nodal tissues, and the remaining 2 cases were involved with LNs and extra-nodal tissue simultaneously. In enhanced CT images, enlarged LNs displayed mild or moderate enhancement, and 2 cases showed heterogeneous ring-enhancement. MR features of 3 patients with extra-nodal RDD, 2 cases showed a mass located in the subcutaneous and anterior abdominal wall respectively, and 1 case showed an intracranial mass. Besides, all lesions showed high signal foci on DWI images, and were characterized by marked heterogeneous enhancement with blurred edge. The dural/fascia tail sign and dilated blood vessels could be seen around all the lesions on enhanced MRI. Radiological features of 2 cases with LN and extranodal tissue involved, one case presented with the swelling and thickening of pharyngeal lymphoid ring and nasopharynx, meanwhile with enlarged LNs in bilateral submandibular area, neck and abdominal cavity, and also companied with osteolytic lesion in right proximal humerus. All these LNs displayed mild and moderate enhancement on CT images. Another case showed enlarged LNs in bilateral neck accompanied with soft tissue mass in the sinuses. Conclusions: RDD occurred commonly in young and middle-aged men and presented with painless enlarged LNs or mass.RDD had a huge diversity of imaging findings, which varied with different location. The radiological features, such as small patches of high signal foci in the masses on DWI images, heterogeneous enhancement and blood vessels around the masses, are helpful in diagnosis of extranodal RDD.

CT Images of Gossypiboma

1994 ◽  
Vol 30 (4) ◽  
pp. 723 ◽  
Author(s):  
Hae Jeong Jeon ◽  
Jeong Hee Park ◽  
Young Chil Choi ◽  
Jong Nam Lira
Keyword(s):  
Ct Images
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