MIFAS: Medical Image File Accessing System in Co-allocation Data Grids

2008 ◽  
Author(s):  
Chao-Tung Yang ◽  
Chiu-Hsiung Chen ◽  
Ming-Feng Yang ◽  
Wen-Chung Chiang
Keyword(s):  
Medical Image ◽  
Data Grids ◽  
Image File

Implementation of a medical image file accessing system in co-allocation data grids

2010 ◽  
Vol 26 (8) ◽  
pp. 1127-1140 ◽  
Author(s):  
Chao-Tung Yang ◽  
Chiu-Hsiung Chen ◽  
Ming-Feng Yang
Keyword(s):  
Medical Image ◽  
Data Grids ◽  
Image File

Managing multiple medical image file formats and conventions

2003 ◽  
Author(s):  
Scott C. Neu ◽  
Daniel J. Valentino ◽  
Keith R. Ouellette ◽  
Arthur W. Toga
Keyword(s):  
Medical Image ◽  
Image File ◽  
File Formats

Accessing medical image file with co-allocation HDFS in cloud

2015 ◽  
Vol 43-44 ◽  
pp. 61-73 ◽  
Author(s):  
Chao-Tung Yang ◽  
Wen-Chung Shih ◽  
Lung-Teng Chen ◽  
Cheng-Ta Kuo ◽  
Fuu-Cheng Jiang ◽  
...  
Keyword(s):  
Medical Image ◽  
Image File

scholarly journals Medical Image File Formats

2013 ◽  
Vol 27 (2) ◽  
pp. 200-206 ◽  
Author(s):  
Michele Larobina ◽  
Loredana Murino
Keyword(s):  
Medical Image ◽  
Image File ◽  
File Formats

scholarly journals An Medical Image File Formats and Digital Image Conversion

2019 ◽  
Vol 9 (1S3) ◽  
pp. 74-78
Keyword(s):  
Digital Image ◽  
Medical Image ◽  
Imaging Informatics ◽  
Technology Initiative ◽  
Image File ◽  
Image Conversion ◽  
File Formats ◽  
Processing Step ◽  
Neuro Imaging ◽  
Image Format

Medical image file formats make the confusing aspect to young researchers who start work with medical images. Medical image format conversion is still a tedious task due to the different structure of files. Digital image conversion from the medical image is an important pre-processing step to the process and visualizes the data. This article presents an overview of the major medical image file formats such as Analyze, neuro-imaging informatics technology initiative (NIFTI), MINC, and digital imaging and communications in medicine (DICOM). Then the characteristics and strengths of the various formats are discussed. Also the article describes the easiest way of digital image conversion from medical image format.

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.

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