scholarly journals A Comparative Study of Testing Parameters of Medical Images Processing

2017 ◽  
Vol 10 (1) ◽  
pp. 243-258
Author(s):  
Fatima Qamar ◽  
Mehreen Sirshar
Keyword(s):  
Comparative Study ◽  
Medical Images ◽  
Images Processing

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

The quest for 'diagnostically lossless' medical image compression: a comparative study of objective quality metrics for compressed medical images

2014 ◽  
Author(s):  
Ilona Kowalik-Urbaniak ◽  
Dominique Brunet ◽  
Jiheng Wang ◽  
David Koff ◽  
Nadine Smolarski-Koff ◽  
...  
Keyword(s):  
Image Compression ◽  
Comparative Study ◽  
Medical Image ◽  
Medical Images ◽  
Quality Metrics ◽  
Medical Image Compression ◽  
Objective Quality

scholarly journals Parallel Digital Watermarking Process on Ultrasound Medical Images in Multicores Environment

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Hui Liang Khor ◽  
Siau-Chuin Liew ◽  
Jasni Mohd. Zain
Keyword(s):  
Digital Watermarking ◽  
Medical Image ◽  
Medical Images ◽  
Security Threats ◽  
Public Network ◽  
Elapsed Time ◽  
Single Frame ◽  
Internal Network ◽  
Speed Up ◽  
Images Processing

With the advancement of technology in communication network, it facilitated digital medical images transmitted to healthcare professionals via internal network or public network (e.g., Internet), but it also exposes the transmitted digital medical images to the security threats, such as images tampering or inserting false data in the images, which may cause an inaccurate diagnosis and treatment. Medical image distortion is not to be tolerated for diagnosis purposes; thus a digital watermarking on medical image is introduced. So far most of the watermarking research has been done on single frame medical image which is impractical in the real environment. In this paper, a digital watermarking on multiframes medical images is proposed. In order to speed up multiframes watermarking processing time, a parallel watermarking processing on medical images processing by utilizing multicores technology is introduced. An experiment result has shown that elapsed time on parallel watermarking processing is much shorter than sequential watermarking processing.

scholarly journals Design of Multivariate Hotelling’s T2 Control Chart Based on Medical Images Processing

2019 ◽  
Vol 16 (Special Issue) ◽  
Author(s):  
Mahmood Shahrabi ◽  
Amirhossein Amiri ◽  
Hamidreza Saligheh Rad ◽  
Sedigheh Ghofrani
Keyword(s):  
Control Chart ◽  
Medical Images ◽  
Hotelling's T2 ◽  
Hotelling’S T2 ◽  
Images Processing ◽  
T2 Control Chart

scholarly journals Application of the transfer learning to the medical images texture classification task

2020 ◽  
Vol 224 ◽  
pp. 01020
Author(s):  
M Privalov ◽  
M Stupina
Keyword(s):  
Neural Networks ◽  
Transfer Learning ◽  
Medical Images ◽  
Texture Classification ◽  
Classification Task ◽  
Radiotherapy Planning ◽  
Digital Radiograph ◽  
Magnetic Resonance Imaging Mri ◽  
Images Processing ◽  
Brachy Therapy

This study is conducted to determine effectiveness and perspectives of application of the transfer learning approach to the medical images classification task. There are a lot of medical studies that involve image acquisition, such as XRay radiography, ultrasonic scanning, computer tomography (CT), magnetic resonance imaging (MRI) etc. Besides those medical procedures there are different operations that use medical images processing including but not limited to digital radiograph reconstruction (DRR), radiotherapy planning, brachy therapy planning. All those tasks could be effectively performed with help of software capable to perform segmentation, classification and object recognition. Those capabilities are naturally depend on neural classifiers. Presented work investigates different approaches to solving image classification task with neural networks, specifically, using pre-processing for feature extraction and end-to-end application of convolutional neural networks (CNN). Due to requirement of significantly big datasets and large computing power CNNs sometimes may appear difficult to train, so our results pay attention to application of transfer learning technique that can potentially relax requirements to classifier training. The conclusions of this study state that transfer learning can be effectively used for classification tasks, especially texture classification.

Segmentation and Extraction of GBM Tumor in Brain MRI Medical Images: Comparative Study

2021 ◽  
pp. 477-487
Author(s):  
M. Z. Doghmane ◽  
M. Driss ◽  
S. Eladj
Keyword(s):  
Comparative Study ◽  
Medical Images ◽  
Brain Mri

scholarly journals A comparative study of some methods for color medical images segmentation

2011 ◽  
Vol 2011 (1) ◽  
Author(s):  
Liana Stanescu ◽  
Dumitru Dan Burdescu ◽  
Marius Brezovan
Keyword(s):  
Comparative Study ◽  
Medical Images

scholarly journals Comparative study of computational visual attention models on two-dimensional medical images

2017 ◽  
Vol 4 (2) ◽  
pp. 025503 ◽  
Author(s):  
Gezheng Wen ◽  
Brenda Rodriguez-Niño ◽  
Furkan Y. Pecen ◽  
David J. Vining ◽  
Naveen Garg ◽  
...  
Keyword(s):  
Visual Attention ◽  
Comparative Study ◽  
Medical Images ◽  
Two Dimensional
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