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...

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Main Author: Mahbubunnabi Tamal
Format: Article
Language:English
Published: Elsevier 2019-12-01
Series:Heliyon
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S2405844019366526
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spelling doaj-126f74b7942440468a349945ecc309f42020-11-25T03:26:44ZengElsevierHeliyon2405-84402019-12-01512e02993A 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)Mahbubunnabi Tamal0Corresponding author.; Department of Biomedical Engineering, Imam Abdulrahman Bin Faisal University, PO Box 1982, Dammam, 31441, Saudi ArabiaPoor 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 (≈4.5 times). 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.http://www.sciencedirect.com/science/article/pii/S2405844019366526Medical imagingNuclear medicineBiomedical engineeringPositron emission tomography (PET)ThresholdRegion growing
collection DOAJ
language English
format Article
sources DOAJ
author Mahbubunnabi Tamal
spellingShingle Mahbubunnabi Tamal
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)
Heliyon
Medical imaging
Nuclear medicine
Biomedical engineering
Positron emission tomography (PET)
Threshold
Region growing
author_facet Mahbubunnabi Tamal
author_sort Mahbubunnabi Tamal
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_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_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_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)
publisher Elsevier
series Heliyon
issn 2405-8440
publishDate 2019-12-01
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 (≈4.5 times). 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.
topic Medical imaging
Nuclear medicine
Biomedical engineering
Positron emission tomography (PET)
Threshold
Region growing
url http://www.sciencedirect.com/science/article/pii/S2405844019366526
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