Cargando…

A hybrid region growing tumour segmentation method for low contrast and high noise Nuclear Medicine (NM) images by combining a novel non-linear diffusion filter and global gradient measure (HNDF-GGM-RG)

Poor spatial resolution and low signal-to-noise ratio (SNR) along with the finite image sampling constraint make lesion segmentation on Nuclear Medicine (NM) images (e.g., PET–Positron Emission Tomography) a challenging task. Since the size, signal-to-background ratio (SBR) and SNR of lesion vary wi...

Descripción completa

Detalles Bibliográficos
Autor principal: Tamal, Mahbubunnabi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6920261/
https://www.ncbi.nlm.nih.gov/pubmed/31879709
http://dx.doi.org/10.1016/j.heliyon.2019.e02993
_version_ 1783480913988943872
author Tamal, Mahbubunnabi
author_facet Tamal, Mahbubunnabi
author_sort Tamal, Mahbubunnabi
collection PubMed
description Poor spatial resolution and low signal-to-noise ratio (SNR) along with the finite image sampling constraint make lesion segmentation on Nuclear Medicine (NM) images (e.g., PET–Positron Emission Tomography) a challenging task. Since the size, signal-to-background ratio (SBR) and SNR of lesion vary within and between patients, performance of the conventional segmentation methods are not consistent against statistical fluctuations. To overcome these limitations, a hybrid region growing segmentation method is proposed combining non-linear diffusion filter and global gradient measure (HNDF-GGM-RG). The performance of the algorithm is validated on PET images and compared with the 40%-fixed threshold and a state-of-the-art active contour (AC) methods. Segmented volume, dice similarity coefficient (DSC) and percentage classification error (% CE) were used as the quantitative figures of merit (FOM) using the torso NEMA phantom that contains six different sizes of spheres. A 2:1 SBR was created between the spheres and background and the phantom was scanned with a Siemens TrueV PET-CT scanner. 40T method is SNR dependent and overestimates the volumes [Formula: see text]. AC volumes match with the true volumes only for the largest three spheres. On the other hand, the proposed HNDF-GGM-RG volumes match closely with the true volumes irrespective of the size and SNR. Average DSC of 0.32 and 0.66 and % CE of 700% and 160% were achieved by the 40T and AC methods respectively. Conversely, average DSC and %CE are 0.70 and 60% for HNDF-GGM-RG and less dependent on SNR. Since two-sample t-test indicates that the performance of AC and HNDF-GGM-RG are statistically significant for the smallest three spheres and similar for the rest, HNDF-GGM-RG can be applied where the size, SBR and SNR are subject to change either due to alterations in the radiotracer uptake because of treatment or uptake variability of different radiotracers because of differences in their molecular pathways.
format Online
Article
Text
id pubmed-6920261
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher Elsevier
record_format MEDLINE/PubMed
spelling pubmed-69202612019-12-26 A hybrid region growing tumour segmentation method for low contrast and high noise Nuclear Medicine (NM) images by combining a novel non-linear diffusion filter and global gradient measure (HNDF-GGM-RG) Tamal, Mahbubunnabi Heliyon Article Poor spatial resolution and low signal-to-noise ratio (SNR) along with the finite image sampling constraint make lesion segmentation on Nuclear Medicine (NM) images (e.g., PET–Positron Emission Tomography) a challenging task. Since the size, signal-to-background ratio (SBR) and SNR of lesion vary within and between patients, performance of the conventional segmentation methods are not consistent against statistical fluctuations. To overcome these limitations, a hybrid region growing segmentation method is proposed combining non-linear diffusion filter and global gradient measure (HNDF-GGM-RG). The performance of the algorithm is validated on PET images and compared with the 40%-fixed threshold and a state-of-the-art active contour (AC) methods. Segmented volume, dice similarity coefficient (DSC) and percentage classification error (% CE) were used as the quantitative figures of merit (FOM) using the torso NEMA phantom that contains six different sizes of spheres. A 2:1 SBR was created between the spheres and background and the phantom was scanned with a Siemens TrueV PET-CT scanner. 40T method is SNR dependent and overestimates the volumes [Formula: see text]. AC volumes match with the true volumes only for the largest three spheres. On the other hand, the proposed HNDF-GGM-RG volumes match closely with the true volumes irrespective of the size and SNR. Average DSC of 0.32 and 0.66 and % CE of 700% and 160% were achieved by the 40T and AC methods respectively. Conversely, average DSC and %CE are 0.70 and 60% for HNDF-GGM-RG and less dependent on SNR. Since two-sample t-test indicates that the performance of AC and HNDF-GGM-RG are statistically significant for the smallest three spheres and similar for the rest, HNDF-GGM-RG can be applied where the size, SBR and SNR are subject to change either due to alterations in the radiotracer uptake because of treatment or uptake variability of different radiotracers because of differences in their molecular pathways. Elsevier 2019-12-11 /pmc/articles/PMC6920261/ /pubmed/31879709 http://dx.doi.org/10.1016/j.heliyon.2019.e02993 Text en © 2019 The Author(s) http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Article
Tamal, Mahbubunnabi
A hybrid region growing tumour segmentation method for low contrast and high noise Nuclear Medicine (NM) images by combining a novel non-linear diffusion filter and global gradient measure (HNDF-GGM-RG)
title A hybrid region growing tumour segmentation method for low contrast and high noise Nuclear Medicine (NM) images by combining a novel non-linear diffusion filter and global gradient measure (HNDF-GGM-RG)
title_full A hybrid region growing tumour segmentation method for low contrast and high noise Nuclear Medicine (NM) images by combining a novel non-linear diffusion filter and global gradient measure (HNDF-GGM-RG)
title_fullStr A hybrid region growing tumour segmentation method for low contrast and high noise Nuclear Medicine (NM) images by combining a novel non-linear diffusion filter and global gradient measure (HNDF-GGM-RG)
title_full_unstemmed A hybrid region growing tumour segmentation method for low contrast and high noise Nuclear Medicine (NM) images by combining a novel non-linear diffusion filter and global gradient measure (HNDF-GGM-RG)
title_short A hybrid region growing tumour segmentation method for low contrast and high noise Nuclear Medicine (NM) images by combining a novel non-linear diffusion filter and global gradient measure (HNDF-GGM-RG)
title_sort hybrid region growing tumour segmentation method for low contrast and high noise nuclear medicine (nm) images by combining a novel non-linear diffusion filter and global gradient measure (hndf-ggm-rg)
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6920261/
https://www.ncbi.nlm.nih.gov/pubmed/31879709
http://dx.doi.org/10.1016/j.heliyon.2019.e02993
work_keys_str_mv AT tamalmahbubunnabi ahybridregiongrowingtumoursegmentationmethodforlowcontrastandhighnoisenuclearmedicinenmimagesbycombininganovelnonlineardiffusionfilterandglobalgradientmeasurehndfggmrg
AT tamalmahbubunnabi hybridregiongrowingtumoursegmentationmethodforlowcontrastandhighnoisenuclearmedicinenmimagesbycombininganovelnonlineardiffusionfilterandglobalgradientmeasurehndfggmrg