ANITA-NC: A code system for modelling material activation induced by neutral or charged particles

Frisoni, M.

doi:10.1016/j.fusengdes.2018.11.029

Fusion Engineering and Design

2019

DOI: 10.1016/j.fusengdes.2018.11.029

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ANITA-NC: A code system for modelling material activation induced by neutral or charged particles

M. Frisoni

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2022

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“…Such errors may hinder proper diagnosis [18]. Several other problems in medical image segmentation such as artifacts [19], noise, cluttered images [20], diffuse organ or tissue boundaries [21] also hamper segmentation [22]. A more automated segmentation method such as a deformable model could be an option to solve this problem and to improve the flexibility and accuracy [23]- [25].…”

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Spring-Charged Particles Model to Improved Shape Recovery: An Application for X-Ray Spinal Segmentation

et al. 2022

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Deformable models are widely used in medical image segmentation methods, to find not only single but also multiple objects within an image. They have the ability to follow the contours of an object of interest, define the boundary of ROI (Region Of Interest) and improve shape recovery. However, these methods still have limitations in cases of low image quality or clutter. This paper presents a new deformable model, the Spring-Charged Particles Model (SCPM). It simulates the movement of positively charged particles connected by springs, attracted towards the contour of objects of interest which is charged negatively, according to the gradient-magnitude image. Springs prevent the particles from moving away and keep the particles at appropriate distances without reducing their flexibility. SCPM was tested on simple shape images and on frontal X-ray images of scoliosis patients. Artificial noise was added to the simple images to examine the robustness of the method. Several configurations of springs and positively charged-particles were evaluated by determining the best spinal segmentation result. The performance of SCPM was compared to the Charged Fluid Model (CFM), Active Contours, and a convolutional neural network (CNN) with U-Net architecture to measure its ability for determining the curvature of the spinal column from frontal X-Ray images. The results show that SCPM is better at segmenting the spine and determining its curvature, as indicated by the highest Area Score value of 0.837, and the lowest standard deviation value of 0.028. INDEX TERMS deformable models, spinal segmentation, medical image analysis, spring-charged particle model I. INTRODUCTIONImage analysis and computer vision are interconnected fields, and provide a range of important tools in the field of biomedical engineering. Support of diagnosis carried out by doctors is very dependent on the results of the analysis and interpretation of the images represented both 2D and 3D computer vision systems. Many new image analysis and computer vision algorithms are required for computer-aided diagnosis, including image segmentation, modeling, object recognition and tracking, shape analysis, and shape recovery. However, there are still challenges especially for automatic detection of regions or objects of interest, and determining their properties for diagnostic purposes. Shape recovery is an important step to refine object

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Spring-Charged Particles Model to Improved Shape Recovery: An Application for X-Ray Spinal Segmentation

et al. 2022

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ANITA-NC: A code system for modelling material activation induced by neutral or charged particles

Cited by 1 publication

References 6 publications

Spring-Charged Particles Model to Improved Shape Recovery: An Application for X-Ray Spinal Segmentation

Spring-Charged Particles Model to Improved Shape Recovery: An Application for X-Ray Spinal Segmentation

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