978-613-8-82748-1

Analysis of Cardiac MRI to recognize cardiovascular disease by AST

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Summary:

We are presenting a framework for the analysis of cardiac MRI to recognise cardiovascular diseases using Simple Iterative Object Extraction Techniques (SIOX) for Semi-Automatic Segmentation. This is one kind of segmentation methodology, in which the user outlines the region of interest with the mouse clicks and algorithms are applied so that the path that best fits the edge of the image is shown. SIOX techniques are used in this kind of segmentation. In an alternative kind of Semi-Automatic Segmentation, the algorithms return a spatial-taxon (i.e. foreground, object-group, object or object-part) selected by the user or designated via prior probabilities. The first contribution involves the introduction of a new algorithm for fitting 3-D Shape and Binary Mask of Endo and Epi Segmentation on cardiac MRI, using the inverse compositional image alignment algorithm. The observe 73 – double increase in fitting speed and accuracy that is on balance with Gauss-Newton Optimization. We show the high-quality results that are derived by the use of and KNN and describe the ways in which it could improve the automated analysis of medical images.

Author:

Jaypalsing Kayte

Biographie:

Jaypalsing N. Kayte is currently pursuing the PhD degree in Computer Science and IT from the Department of Computer Science and IT, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, Maharashtra India. He is currently working as BSR Research Fellow sanctioned by UGC. His research interest includes AI, Image Processing, Remote Sensing & GIS.

Author:

Ratnadeep R. Deshmukh

Biographie:

Author:

Charansing N. Kayte

Biographie:

Number of Pages:

108

Book language:

English

Published On:

2019-03-28

ISBN:

978-613-8-82748-1

Publishing House:

Scholars' Press

Keywords:

CARDIAC, MRI, Cardiovascular, diseases, Annotation, Anatomy, Sunnybrook, Segmentation, MICCAI, OnlineDataDICOM, ValidationDataDICOM, SIOX

Product category:

COMPUTERS / General