scholarly journals Validation of medical image processing in image-guided therapy

2002 ◽  
Vol 21 (12) ◽  
pp. 1445-1449 ◽  
Author(s):  
P. Jannin ◽  
J.M. Fitzpatrick ◽  
D.J. Hawkes ◽  
X. Pennec ◽  
R. Shahidl ◽  
...  
Keyword(s):  
Image Processing ◽  
Medical Image ◽  
Medical Image Processing ◽  
Image Guided Therapy ◽  
Image Guided ◽  
Guided Therapy

White paper: validation of medical image processing in image-guided therapy

2002 ◽  
pp. 299-305 ◽  
Author(s):  
P. Jannin ◽  
J. M. Fitzpatrick ◽  
D. J. Hawkes ◽  
X. Pennec ◽  
R. Shahidi ◽  
...  
Keyword(s):  
Image Processing ◽  
Medical Image ◽  
Medical Image Processing ◽  
White Paper ◽  
Image Guided Therapy ◽  
Image Guided ◽  
Guided Therapy

scholarly journals Simulink Libraries for Visual Programming of VTK and ITK

2010 ◽  
Author(s):  
David Gobbi ◽  
Parvin Mousavi ◽  
Karen Li ◽  
Jing Xiang ◽  
Adam Campigotto ◽  
...  
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Open Source ◽  
Visual Programming ◽  
Visual Environment ◽  
Image Guided Therapy ◽  
Image Guided ◽  
Guided Therapy ◽  
Drag And Drop

We have created open-source Simulink block libraries for ITK and VTK that allow pipelines for these toolkits to be built in a visual, drag-and-drop style within MATLAB. Each block contains an instance of an ITK or VTK class. Any block connections and parameters that the user makes within MATLAB’s Simulink visual environment are converted into connections and parameters for the ITK and VTK pipelines. In addition, we provide conversion of images to and from MATLAB arrays to allow MATLAB image processing blocks to be mixed with ITK and VTK blocks. The code for our block libraries is generated automatically from XML descriptions of the inputs, outputs, and parameters of the ITK and VTK classes. We have used these block libraries to build some example pipelines and believe that they will be useful for developing applications in image analysis and image-guided therapy.

A Comparative Study of Ant and Genetic Algorithms in Digital Mammography

2018 ◽  
pp. 194-200
Author(s):  
J. Magelin Mary ◽  
Chitra K. ◽  
Y. Arockia Suganthi
Keyword(s):  
Genetic Algorithm ◽  
Image Processing ◽  
Medical Image ◽  
Medical Image Processing ◽  
Processing Technique ◽  
Input Image ◽  
Image Processing Technique ◽  
Computational Time ◽  
Image Mining ◽  
The Individual

Image processing technique in general, involves the application of signal processing on the input image for isolating the individual color plane of an image. It plays an important role in the image analysis and computer version. This paper compares the efficiency of two approaches in the area of finding breast cancer in medical image processing. The fundamental target is to apply an image mining in the area of medical image handling utilizing grouping guideline created by genetic algorithm. The parameter using extracted border, the border pixels are considered as population strings to genetic algorithm and Ant Colony Optimization, to find out the optimum value from the border pixels. We likewise look at cost of ACO and GA also, endeavors to discover which one gives the better solution to identify an affected area in medical image based on computational time.

scholarly journals The Constantly Evolving Role of Medical Image Processing in Oncology: From Traditional Medical Image Processing to Imaging Biomarkers and Radiomics

2021 ◽  
Vol 7 (8) ◽  
pp. 124
Author(s):  
Kostas Marias
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Medical Image ◽  
Biomarker Discovery ◽  
Medical Image Processing ◽  
Machine Learning Techniques ◽  
Imaging Biomarker ◽  
Imaging Biomarkers ◽  
Precision Oncology

The role of medical image computing in oncology is growing stronger, not least due to the unprecedented advancement of computational AI techniques, providing a technological bridge between radiology and oncology, which could significantly accelerate the advancement of precision medicine throughout the cancer care continuum. Medical image processing has been an active field of research for more than three decades, focusing initially on traditional image analysis tasks such as registration segmentation, fusion, and contrast optimization. However, with the advancement of model-based medical image processing, the field of imaging biomarker discovery has focused on transforming functional imaging data into meaningful biomarkers that are able to provide insight into a tumor’s pathophysiology. More recently, the advancement of high-performance computing, in conjunction with the availability of large medical imaging datasets, has enabled the deployment of sophisticated machine learning techniques in the context of radiomics and deep learning modeling. This paper reviews and discusses the evolving role of image analysis and processing through the lens of the abovementioned developments, which hold promise for accelerating precision oncology, in the sense of improved diagnosis, prognosis, and treatment planning of cancer.

Recent advances in medical image processing for the evaluation of chronic kidney disease

2021 ◽  
Vol 69 ◽  
pp. 101960
Author(s):  
Israa Alnazer ◽  
Pascal Bourdon ◽  
Thierry Urruty ◽  
Omar Falou ◽  
Mohamad Khalil ◽  
...  
Keyword(s):  
Image Processing ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Medical Image ◽  
Medical Image Processing ◽  
Recent Advances

Research on nano-CT medical image processing based on embedded system

2021 ◽  
Vol 82 ◽  
pp. 103755
Author(s):  
Shengyan Cai ◽  
Fangyuan Chai ◽  
Chunhuan Hu ◽  
Xue Han ◽  
Shuyu Liu
Keyword(s):  
Image Processing ◽  
Embedded System ◽  
Medical Image ◽  
Medical Image Processing
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