Isotropic versus anisotropic techniques in cardiac computed tomography images processing

Barrera, Doris; Valbuena, Oscar; Vera, Miguel; Huérfano, Yoleidy; Gelvez, Elkin; Salazar, Juan; Molina, Valentín; Sáenz, Frank; Vera, Miguel; Salazar, Williams

doi:10.1088/1742-6596/1160/1/012006

Cited by 1 publication

(1 citation statement)

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“…Statistics powered computational neurological tumor systems possess the possibilities to offer trial and error tumor surgeon by way of this kind of quantitative and as well , economical applications to create and check hypotheses on cancerous growth development, and to provide primary functioning guidelines regulating bidirectional transmission diffusion in multi-cellular malignancy models [1,2,3,4].An enhanced recognizing of the untouched difficulty of these types of malignancy models needs reinforced interdisciplinary exploration in which usually the subsequent technology of progressive computational designs, knowledgeable by means of and then continually modified with experimental statistics, will certainly perform an essential part of leading trial and error model and pattern in heading ahead. Various interesting and as well, crucial complications in scientific research and technological know-how provoke to inverse conditions; that is certainly to identify an unfamiliar action or to approximate variables out of over-specified information and facts [5,6].…”

Section: Introductionmentioning

confidence: 99%

Development and Porting Of Mathematical Model to Design a PixTra Algorithm for Detection of Brain Tumor

Khalate,

Basotia,

Ghule

2019

IJEAT

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The goal of applied mathematics is to develop and implement the automated systems, services and support to execute engineering goals. Game theory is always a domain of choice for development of strategy to enhance the usability of mathematical applications. Today, there is rarely any system which works without keeping mathematics behind the curtain. A recent trend in medical imaging leads via mathematical algorithmic development for brain tumor analysis. The deep learning technology is a mathematical strategic solution to identify the brain tumor from medical resonance imaging. The game theory strategic development is necessary for mathematical modeling of image technologies as pixel level calculations are required. Hence, this paper shows the newly developed mathematical modeling for efficient brain tumor detection using game theory and set theory. This paper also suggests the new algorithm which is ported from mathematical modeling for deep learning brain tumor image processing.

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Section: Introductionmentioning

confidence: 99%