Multi-object segmentation framework using deformable models for medical imaging analysis

2015 ◽  
Vol 54 (8) ◽  
pp. 1181-1192 ◽  
Author(s):  
Rafael Namías ◽  
Juan Pablo D’Amato ◽  
Mariana del Fresno ◽  
Marcelo Vénere ◽  
Nicola Pirró ◽  
...  
Keyword(s):  
Medical Imaging ◽  
Object Segmentation ◽  
Deformable Models ◽  
Imaging Analysis ◽  
Segmentation Framework

An Unified Recurrent Video Object Segmentation Framework for Various Surveillance Environments

2021 ◽  
pp. 1-1
Author(s):  
Prashant W. Patil ◽  
Akshay Dudhane ◽  
Ashutosh Kulkarni ◽  
Subrahmanyam Murala ◽  
Anil B. Gonde ◽  
...  
Keyword(s):  
Object Segmentation ◽  
Video Object Segmentation ◽  
Video Object ◽  
Segmentation Framework

Interactive segmentation framework of the Medical Imaging Interaction Toolkit

2009 ◽  
Vol 96 (1) ◽  
pp. 72-83 ◽  
Author(s):  
D. Maleike ◽  
M. Nolden ◽  
H.-P. Meinzer ◽  
I. Wolf
Keyword(s):  
Medical Imaging ◽  
Interactive Segmentation ◽  
Segmentation Framework

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.

scholarly journals Neuroconductor: an R platform for medical imaging analysis

Biostatistics ◽  
2018 ◽  
Vol 20 (2) ◽  
pp. 218-239 ◽  
Author(s):  
John Muschelli ◽  
Adrian Gherman ◽  
Jean-Philippe Fortin ◽  
Brian Avants ◽  
Brandon Whitcher ◽  
...  
Keyword(s):  
Medical Imaging ◽  
Imaging Analysis
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