Volume Estimation of Pathology Zones in 3D Medical Images

Krechetova, Katrina; Glazs, Aleksandrs

doi:10.1007/978-3-540-89208-3_147

Cited by 2 publications

(1 citation statement)

References 2 publications

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“…In this work a method of ROI extraction is proposed. It is based on the modification of a method described in [1]. The modification includes combining the method with the clockwise point selection [1], and implementing an adaptive threshold T ij for each part between the adjacent points i, j of the adaptive contour, in a way that a lesser distance would require a stricter threshold (1):…”

Section: Region Of Interest Extractionmentioning

confidence: 99%

Medical Image Region Extraction and 3D Modeling Based on Approximating Curves

Bolochko

Sisojevs

Glazs

et al. 2011

AMR

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This work describes several methods that intend to solve such medical image processing tasks as extraction and 3D visualization of the region of interest (ROI). The proposed methods were tested on the medical images of a brain acquired by computer tomography and proven to be applicable to different types of ROI, resulting in a possible visualization of several ROI at once, i.e. pathology and the head of a patient. The results can be used to provide practical improvements to the reliability of medical diagnostics.

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Section: Region Of Interest Extractionmentioning

confidence: 99%

Medical Image Region Extraction and 3D Modeling Based on Approximating Curves

Bolochko

Sisojevs

Glazs

et al. 2011

AMR

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3D Model Visualization for Brain Tumour using MRI Images

Hamzah

Alias

Wahab

et al. 2019

J. Phys.: Conf. Ser.

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The inaccurate prediction of tumour is because of the 2D images do not present the complete natural tumour representation as in 3D visualization. In order to construct the 3D model, the contour of 2D MRI images must be merged together. In this work, the edge detection of brain tumour on 2D images is done using the Geodesic Active Contour (GAC) model based on Additive Operator Splitting (AOS), then 3D model is visualized using Image Manifold (IM) and Volume Estimation (VE) methods. The comparison of performance for both methods is calculated. In conclusion, the execution time for VE method is higher than IM method since more images are used in VE method to calculate the volume of the tumour accurately. In terms of iteration number, Volume Estimation has a higher number of iterations, but for error and root mean square error, both methods have roughly the same value.

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Volume Estimation of Pathology Zones in 3D Medical Images

Cited by 2 publications

References 2 publications

Medical Image Region Extraction and 3D Modeling Based on Approximating Curves

Medical Image Region Extraction and 3D Modeling Based on Approximating Curves

3D Model Visualization for Brain Tumour using MRI Images

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