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The 3D EdgeRunner Pipeline: a novel shape-based analysis for neoplasms characterization
Author(s) -
Fernando Yepes-C,
Rebecca Johnson,
Yi Lao,
Darryl Hwang,
Julie Coloigner,
Felix Y. Yap,
Desai Bushan,
Phillip M. Cheng,
Inderbir S. Gill,
Vinay Duddalwar,
Natasha Leporé
Publication year - 2016
Publication title -
proceedings of spie, the international society for optical engineering/proceedings of spie
Language(s) - English
Resource type - Conference proceedings
SCImago Journal Rank - 0.192
H-Index - 176
eISSN - 1996-756X
pISSN - 0277-786X
DOI - 10.1117/12.2217238
Subject(s) - pipeline (software) , computer science , process (computing) , characterization (materials science) , point (geometry) , enhanced data rates for gsm evolution , artificial intelligence , pattern recognition (psychology) , field (mathematics) , data mining , computer vision , mathematics , geometry , physics , optics , programming language , operating system , pure mathematics
The characterization of tumors after being imaged is currently a qualitative process performed by skilled professionals. If we can aid their diagnosis by identifying quantifiable features associated with tumor classification, we may avoid invasive procedures such as biopsies and enhance efficiency. The aim of this paper is to describe the 3D EdgeRunner Pipeline which characterizes the shape of a tumor. Shape analysis is relevant as malignant tumors tend to be more lobular and benign ones tare generally more symmetrical. The method described considers the distance from each point on the edge of the tumor to the centre of a synthetically created field of view. The method then determines coordinates where the measured distances are rapidly changing (peaks) using a second derivative found by five point differentiation. The list of coordinates considered to be peaks can then be used as statistical data to compare tumors quantitatively. We have found this process effectively captures the peaks on a selection of kidney tumors.

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