scholarly journals Projection Methods for Uniformly Convex Expandable Sets

Mathematics ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. 1108
Author(s):  
Stéphane Chrétien ◽  
Pascal Bondon
Keyword(s):  
Medical Image ◽  
Theoretical Study ◽  
Projection Methods ◽  
Signal Denoising ◽  
Projection Algorithms ◽  
General Projection ◽  
Medical Image Reconstruction ◽  
Magnetic Resonance Imaging Mri ◽  
Successive Projection ◽  
Constraint Sets

Many problems in medical image reconstruction and machine learning can be formulated as nonconvex set theoretic feasibility problems. Among efficient methods that can be put to work in practice, successive projection algorithms have received a lot of attention in the case of convex constraint sets. In the present work, we provide a theoretical study of a general projection method in the case where the constraint sets are nonconvex and satisfy some other structural properties. We apply our algorithm to image recovery in magnetic resonance imaging (MRI) and to a signal denoising in the spirit of Cadzow’s method.

scholarly journals Design of Cascaded CNN for Medical Image Super Resolution

2021 ◽  
Vol 7 (3) ◽  
pp. 22-29
Author(s):  
Kajol Singh ◽  
Manish Saxena
Keyword(s):  
Neural Networks ◽  
Medical Image ◽  
Research Work ◽  
Super Resolution ◽  
Primary Objective ◽  
Lighting Conditions ◽  
Proposed Model ◽  
Filter Size ◽  
Medical Image Reconstruction ◽  
Image Super Resolution

The images captured through a camera usually belong to over or under exposed conditions. The reason may be inappropriate lighting conditions or camera resolution. Hence, it is of utmost importance to have a few enhancement techniques that could make these artefacts look better. Hence, the primary objective pertaining to the adjustment and enhancement techniques is to enhance the characteristics of an image. The initial numeric values related to an image get distorted when an image is enhanced. Therefore, enhancement techniques should be designed in such a way that the image quality isn’t compromised. This research work is focused on proposed a network design for deep convolution neural networks for application of super resolution techniques. To improve the complexity of existing techniques this work is intended towards network designs, different filter size and CNN architecture. The CNN model is most effective model for detection and segmentation in image. This model will improve the efficiency of medical image reconstruction from LR to HR. The proposed model showed its efficiency not only PET medical images but also on retinal database and achieved advance results as compared to existing works.

scholarly journals A Web Simulation of Medical Image Reconstruction and Processing as an Educational Tool

2014 ◽  
Vol 28 (1) ◽  
pp. 24-31 ◽  
Author(s):  
Dimitrios Papamichail ◽  
Evaggelos Pantelis ◽  
Panagiotis Papagiannis ◽  
Pantelis Karaiskos ◽  
Evangelos Georgiou
Keyword(s):  
Image Reconstruction ◽  
Medical Image ◽  
Educational Tool ◽  
Medical Image Reconstruction

Research and Developments in Medical Image Reconstruction Methods and Its Applications

2017 ◽  
pp. 491-535
Author(s):  
Shailendra Tiwari ◽  
Rajeev Srivastava
Keyword(s):  
Computed Tomography ◽  
Image Reconstruction ◽  
Medical Imaging ◽  
Medical Image ◽  
Ct Scanner ◽  
Image Reconstruction Techniques ◽  
Reconstruction Methods ◽  
Dimensional Reconstruction ◽  
Development Technology ◽  
Medical Image Reconstruction

Image reconstruction from projection is the field that lays the foundation for Medical Imaging or Medical Image Processing. The rapid and proceeding progress in medical image reconstruction, and the related developments in analysis methods and computer-aided diagnosis, has promoted medical imaging into one of the most important sub-fields in scientific imaging. Computer technology has enabled tomographic and three-dimensional reconstruction of images, illustrating both anatomical features and physiological functioning, free from overlying structures. In this chapter, the authors share their opinions on the research and development in the field of Medical Image Reconstruction Techniques, Computed Tomography (CT), challenges and the impact of future technology developments in CT, Computed Tomography Metrology in industrial research & development, technology, and clinical performance of different CT-scanner generations used for cardiac imaging, such as Electron Beam CT (EBCT), single-slice CT, and Multi-Detector row CT (MDCT) with 4, 16, and 64 simultaneously acquired slices. The authors identify the limitations of current CT-scanners, indicate potential of improvement and discuss alternative system concepts such as CT with area detectors and Dual Source CT (DSCT), recent technology with a focus on generation and detection of X-rays, as well as image reconstruction are discussed. Furthermore, the chapter includes aspects of applications, dose exposure in computed tomography, and a brief overview on special CT developments. Since this chapter gives a review of the major accomplishments and future directions in this field, with emphasis on developments over the past 50 years, the interested reader is referred to recent literature on computed tomography including a detailed discussion of CT technology in the references section.

Learning deconvolutional deep neural network for high resolution medical image reconstruction

2018 ◽  
Vol 468 ◽  
pp. 142-154 ◽  
Author(s):  
Hui Liu ◽  
Jun Xu ◽  
Yan Wu ◽  
Qiang Guo ◽  
Bulat Ibragimov ◽  
...  
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Image Reconstruction ◽  
Medical Image ◽  
Deep Neural Network ◽  
Medical Image Reconstruction

Automatic Segmentation of the Skull in MRI Sequences Using Level Set Method

2011 ◽  
Vol 58-60 ◽  
pp. 2370-2375
Author(s):  
Wei Li Ding ◽  
Feng Jiang ◽  
Jia Qing Yan
Keyword(s):  
Level Set ◽  
Medical Image ◽  
Automatic Segmentation ◽  
Image Sequences ◽  
Initial Contour ◽  
Single Image ◽  
Mri Sequences ◽  
Magnetic Resonance Imaging Mri ◽  
Mri Image

Magnetic Resonance Imaging (MRI) has been widely used in clinical diagnose. Segmentation of these images obtained by MRI is a necessary procedure in medical image processing. In this paper, an improved level set algorithm was proposed to optimize the segmentation of MRI image sequences based on article [1]. Firstly, we add an area term and the edge indicator function to the total energy function for single image segmentation. Secondly, we presented a new method which uses the circumscribed polygon of the previous segmentation result as the initial contour of the next image to achieve automatic segmentation of image sequences. The algorithm was tested on MRI image sequences provided by Chuiyanliu Hospital, Chaoyang District of Beijing; the results have indicated that the proposed algorithm can effectively enhance the segmentation speed and quality of MRI sequences.

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