Volume Definition Tools for Medical Imaging Applications

Karangelis, Grigorios; Zimeras, Stelios; Firle, Evelyn; Wang, Min; Sakas, Georgios

doi:10.1007/3-540-45468-3_201

Cited by 3 publications

(1 citation statement)

References 2 publications

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“…Any remaining errors introduced by automatic segmentation methods may be corrected by manual editing. In this work, a boundary tracking algorithm [ 26 – 30 ] was implemented for the segmentation part.…”

Section: Segmentation Techniquesmentioning

confidence: 99%

Interactive Tele-Radiological Segmentation Systems for Treatment and Diagnosis

Zimeras

Gortzis

2012

International Journal of Telemedicine and Applications

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Telehealth is the exchange of health information and the provision of health care services through electronic information and communications technology, where participants are separated by geographic, time, social and cultural barriers. The shift of telemedicine from desktop platforms to wireless and mobile technologies is likely to have a significant impact on healthcare in the future. It is therefore crucial to develop a general information exchange e-medical system to enables its users to perform online and offline medical consultations through diagnosis. During the medical diagnosis, image analysis techniques combined with doctor's opinions could be useful for final medical decisions. Quantitative analysis of digital images requires detection and segmentation of the borders of the object of interest. In medical images, segmentation has traditionally been done by human experts. Even with the aid of image processing software (computer-assisted segmentation tools), manual segmentation of 2D and 3D CT images is tedious, time-consuming, and thus impractical, especially in cases where a large number of objects must be specified. Substantial computational and storage requirements become especially acute when object orientation and scale have to be considered. Therefore automated or semi-automated segmentation techniques are essential if these software applications are ever to gain widespread clinical use. The main purpose of this work is to analyze segmentation techniques for the definition of anatomical structures under telemedical systems.

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Section: Segmentation Techniquesmentioning

confidence: 99%

Interactive Tele-Radiological Segmentation Systems for Treatment and Diagnosis

Zimeras

Gortzis

2012

International Journal of Telemedicine and Applications

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Registration of 3D U/S and CT images of the prostate

Firle

Chen

Wesarg

2002

CARS 2002 Computer Assisted Radiology and Surgery

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Advance Techniques in Medical Imaging under Big Data Analysis: Covid-19 Images

Zimeras¹

2021

ACT

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Quantitative analysis of digital images requires detection and segmentation of the borders of the object of interest. Accurate segmentation is required for volume determination, 3D rendering, radiation therapy, and surgery planning. In medical images, segmentation has traditionally been done by human experts. Substantial computational and storage requirements become especially acute when object orientation and scale have to be considered. Therefore, automated or semi-automated segmentation techniques are essential if these software applications are ever to gain widespread clinical use. Many methods have been proposed to detect and segment 2D shapes, most of which involve template matching. Advanced segmentation techniques called Snakes or active contours have been used, considering deformable models or templates. The main purpose of this work is to apply segmentation techniques for the definition of 3D organs (anatomical structures) when big data information has been stored and must be organized by the doctors for medical diagnosis. The processes would be implemented in the CT images from patients with COVID-19.

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Volume Definition Tools for Medical Imaging Applications

Abstract: Central focus of this work, are techniques used to improve time and interaction needed for a user when defining one or more structures. These techniques involve interpolation methods for the manual volume definition and methods for the semi-automatic organ shape extraction.

Cited by 3 publications

References 2 publications

Interactive Tele-Radiological Segmentation Systems for Treatment and Diagnosis

Interactive Tele-Radiological Segmentation Systems for Treatment and Diagnosis

Registration of 3D U/S and CT images of the prostate

Advance Techniques in Medical Imaging under Big Data Analysis: Covid-19 Images

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