Improvement of Basal Ganglia Detectability in Brain Single Photon Emission Computerized Tomography by Wavelet Transformation in Image Processing Domain: A XCAT Phantom Study
Abstract
Purpose: Noise in brain Single Photon Emission Computed Tomography (SPECT) images limits an early diagnosis of Parkinson's Disease (PD). To overcome the limitation, as an image processing approach, wavelet transformation was used to denoising the images also with a segmentation method to differentiate the basal ganglia in brain SPECT.
Materials and Methods: The brain scans of the human XCAT phantom through the Simulating Medical Imaging Nuclear Detectors (SIMIND) simulated SPECT system were imported to the MATLAB toolkit for image processing. The reconstructed brain images by iterative reconstruction were de-noised through 9 methods of wavelet transformation at different levels, and then six segmentation methods were applied to differentiate the caudate and putamen. The Dice coefficient, Specificity, and Sensitivity evaluation criteria were calculated based on the adaptive thresholding of the selected images from segmentation. A ground truth image was manually marked by a clinical nuclear medicine specialist.
Results: The dice coefficient was obtained in a range from 0.3979 to 0.6299, as well as the specificity criterion from 0.7682 to 0.8168 and the sensitivity from 0.9049 to 0.9871.The results from adaptive threshold
segmentation and the evaluation criteria showed that the best levels of the nucleuses detectability were provided by level 7 of Biorthogonal, levels 4 and 7 of Coiflet, level 6 of Daubechies, level 5 of Haar, level 6 of Morlet and level 6 of Symlet methods.
Conclusion: Parkinson’s disease may be diagnosed in the early stage by an image processing approach to improve the quality of brain SPECT images.
1- TC Booth, M Nathan, AD Waldman, A-M Quigley, AH Schapira, and J Buscombe, "The role of functional dopamine-transporter SPECT imaging in parkinsonian syndromes, part 1." American Journal of Neuroradiology, Vol. 36 (No. 2), pp. 229-35, (2015).
2- Seong-Jin Son, Mansu Kim, and Hyunjin Park, "Imaging analysis of Parkinson’s disease patients using SPECT and tractography." Scientific reports, Vol. 6 (No. 1), pp. 1-11, (2016).
3- Dun-Hui Li, Ya-Chao He, Jun Liu, and Sheng-Di Chen, "Diagnostic accuracy of transcranial sonography of the substantia nigra in Parkinson’s disease: a systematic review and meta-analysis." Scientific reports, Vol. 6 (No. 1), pp. 1-9, (2016).
4- R Prashanth, Sumantra Dutta Roy, Pravat K Mandal, and Shantanu Ghosh, "Automatic classification and prediction models for early Parkinson’s disease diagnosis from SPECT imaging." Expert Systems with Applications, Vol. 41 (No. 7), pp. 3333-42, (2014).
5- Payam Sasannezhad et al., "99mTc-TRODAT-1 SPECT imaging in early and late onset Parkinson’s disease." Asia Oceania Journal of Nuclear Medicine and Biology, Vol. 5 (No. 2), p. 114, (2017).
6- John P Seibyl, "Imaging studies in movement disorders." in Seminars in nuclear medicine, (2003), Vol. 33 (No. 2): Elsevier, pp. 105-13.
7- David J Brooks, "Imaging approaches to Parkinson disease." Journal of Nuclear Medicine, Vol. 51 (No. 4), pp. 596-609, (2010).
8- Ludovico Minati, M Grisoli, F Carella, T De Simone, MG Bruzzone, and M Savoiardo, "Imaging degeneration of the substantia nigra in Parkinson disease with inversion-recovery MR imaging." American Journal of Neuroradiology, Vol. 28 (No. 2), pp. 309-13, (2007).
9- Ling Wang, Qi Zhang, Huanbin Li, and Hong Zhang, "SPECT molecular imaging in Parkinson's disease." Journal of Biomedicine and Biotechnology, Vol. 2012(2012).
10- Gunjan Pahuja, TN Nagabhushan, and Bhanu Prasad, "Early detection of Parkinson’s disease by using SPECT imaging and biomarkers." Journal of Intelligent Systems, Vol. 29 (No. 1), pp. 1329-44, (2020).
11- Hiroki Nosaka et al., "Influence of Brain Atrophy using Semi-quantitative Analysis in [123I] FP-CIT Single Photon Emission Computed Tomography: A Monte Carlo Simulation Study." (2021).
12- Aprajita Sharma and Ram Nivas Giri, "An Elegant Approach for Diagnosis of Parkinson's disease on MRI Brain Images by Means of a Neural Network." International Journal of Engineering Sciences & Research Technology, Vol. 2 (No. 9), pp. 2553-57, (2013).
13- Mou-Fa Guo, Xiao-Dan Zeng, Duan-Yu Chen, and Nien-Che Yang, "Deep-learning-based earth fault detection using continuous wavelet transform and convolutional neural network in resonant grounding distribution systems." IEEE Sensors Journal, Vol. 18 (No. 3), pp. 1291-300, (2017).
14- Arpan Zaeni, Tria Kasnalestari, and Umar Khayam, "Application of wavelet transformation symlet type and coiflet type for partial discharge signals denoising." in 2018 5th International Conference on Electric Vehicular Technology (ICEVT), (2018): IEEE, pp. 78-82.
15- Anja Borsdorf, Rainer Raupach, Thomas Flohr, and Joachim Hornegger, "Wavelet based noise reduction in CT-images using correlation analysis." IEEE transactions on medical imaging, Vol. 27 (No. 12), pp. 1685-703, (2008).
16- Maha Alafeef and Mohammad Fraiwan, "On the diagnosis of idiopathic Parkinson’s disease using continuous wavelet transform complex plot." Journal of Ambient Intelligence and Humanized Computing, Vol. 10 (No. 7), pp. 2805-15, (2019).
17- Michael Ljungberg, “The SIMIND Monte Carlo program. In: M Ljungberg S E Strand, M A King, editors. Monte Carlo calculations in nuclear medicine: Applications in diagnostic imaging.” 2nd ed. Boca Raton: CRC Press; p.111–128, (2012).
18- William Paul Segars, et al., “4D XCAT phantom for multimodality imaging research.” Medical Physics, Vol. 37(9):4902-15, (2010).
19- Mohammad Taghi Bahreyni Toossi, et al., “SIMIND Monte Carlo simulation of a single photon emission CT.” Journal of Medical Physics, Vol.35(1):42-7, (2010).
20- PMK Prasad and G Umamadhuri, "Biorthogonal wavelet-based image compression." in Artificial Intelligence and Evolutionary Computations in Engineering Systems: Springer, (2018), pp. 391-404.
21- Rahul Sahu and MP Parsai, "Comparison of digital image denoising method using various Transforms on Satellite Imagery." in 2019 2nd International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT), (2019), Vol. 1: IEEE, pp. 1391-99.
22- Nihaal Mehta et al., "Impact of binarization thresholding and brightness/contrast adjustment methodology on optical coherence tomography angiography image quantification." American journal of ophthalmology, Vol. 205pp. 54-65, (2019).
23- N Senthilkumaran and S Vaithegi, "Image segmentation by using thresholding techniques for medical images." Computer Science & Engineering: An International Journal, Vol. 6 (No. 1), pp. 1-13, (2016).
24- Diego Marcos et al., "Learning deep structured active contours end-to-end." in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (2018), pp. 8877-85.
25- Yunyi Tang and Yuanpeng Zhu, "Image zooming based on two classes of C1-continuous coons patches construction with shape parameters over triangular domain." Symmetry, Vol. 12 (No. 4), p. 661, (2020).
26- Boliang Yu et al., "HybraPD atlas: Towards precise subcortical nuclei segmentation using multimodality medical images in patients with Parkinson disease." Human brain mapping, Vol. 42 (No. 13), pp. 4399-421, (2021).
27- Nooshin Nabizadeh and Miroslav Kubat, "Brain tumors detection and segmentation in MR images: Gabor wavelet vs. statistical features." Computers & Electrical Engineering, Vol. 45pp. 286-301, (2015).
28- Yousif Abdallah, "Delineation of brain tumours for radiotherapy patients using image segmentation techniques." Onkologia i Radioterapia, Vol. 14 (No. 5), pp. 1-5, (2020).
29- Nilesh Bhaskarrao Bahadure, Arun Kumar Ray, and Har Pal Thethi, "Image analysis for MRI based brain tumor detection and feature extraction using biologically inspired BWT and SVM." International Journal of Biomedical Imaging, Vol. 2017(2017).
30- Andrius Sakalauskas, Vita Špečkauskienė, Kristina Laučkaitė, Rytis Jurkonis, Daiva Rastenytė, and Arūnas Lukoševičius, "Transcranial ultrasonographic image analysis system for decision support in parkinson disease." Journal of Ultrasound in Medicine, Vol. 37 (No. 7), pp. 1753-61, (2018).
2- Seong-Jin Son, Mansu Kim, and Hyunjin Park, "Imaging analysis of Parkinson’s disease patients using SPECT and tractography." Scientific reports, Vol. 6 (No. 1), pp. 1-11, (2016).
3- Dun-Hui Li, Ya-Chao He, Jun Liu, and Sheng-Di Chen, "Diagnostic accuracy of transcranial sonography of the substantia nigra in Parkinson’s disease: a systematic review and meta-analysis." Scientific reports, Vol. 6 (No. 1), pp. 1-9, (2016).
4- R Prashanth, Sumantra Dutta Roy, Pravat K Mandal, and Shantanu Ghosh, "Automatic classification and prediction models for early Parkinson’s disease diagnosis from SPECT imaging." Expert Systems with Applications, Vol. 41 (No. 7), pp. 3333-42, (2014).
5- Payam Sasannezhad et al., "99mTc-TRODAT-1 SPECT imaging in early and late onset Parkinson’s disease." Asia Oceania Journal of Nuclear Medicine and Biology, Vol. 5 (No. 2), p. 114, (2017).
6- John P Seibyl, "Imaging studies in movement disorders." in Seminars in nuclear medicine, (2003), Vol. 33 (No. 2): Elsevier, pp. 105-13.
7- David J Brooks, "Imaging approaches to Parkinson disease." Journal of Nuclear Medicine, Vol. 51 (No. 4), pp. 596-609, (2010).
8- Ludovico Minati, M Grisoli, F Carella, T De Simone, MG Bruzzone, and M Savoiardo, "Imaging degeneration of the substantia nigra in Parkinson disease with inversion-recovery MR imaging." American Journal of Neuroradiology, Vol. 28 (No. 2), pp. 309-13, (2007).
9- Ling Wang, Qi Zhang, Huanbin Li, and Hong Zhang, "SPECT molecular imaging in Parkinson's disease." Journal of Biomedicine and Biotechnology, Vol. 2012(2012).
10- Gunjan Pahuja, TN Nagabhushan, and Bhanu Prasad, "Early detection of Parkinson’s disease by using SPECT imaging and biomarkers." Journal of Intelligent Systems, Vol. 29 (No. 1), pp. 1329-44, (2020).
11- Hiroki Nosaka et al., "Influence of Brain Atrophy using Semi-quantitative Analysis in [123I] FP-CIT Single Photon Emission Computed Tomography: A Monte Carlo Simulation Study." (2021).
12- Aprajita Sharma and Ram Nivas Giri, "An Elegant Approach for Diagnosis of Parkinson's disease on MRI Brain Images by Means of a Neural Network." International Journal of Engineering Sciences & Research Technology, Vol. 2 (No. 9), pp. 2553-57, (2013).
13- Mou-Fa Guo, Xiao-Dan Zeng, Duan-Yu Chen, and Nien-Che Yang, "Deep-learning-based earth fault detection using continuous wavelet transform and convolutional neural network in resonant grounding distribution systems." IEEE Sensors Journal, Vol. 18 (No. 3), pp. 1291-300, (2017).
14- Arpan Zaeni, Tria Kasnalestari, and Umar Khayam, "Application of wavelet transformation symlet type and coiflet type for partial discharge signals denoising." in 2018 5th International Conference on Electric Vehicular Technology (ICEVT), (2018): IEEE, pp. 78-82.
15- Anja Borsdorf, Rainer Raupach, Thomas Flohr, and Joachim Hornegger, "Wavelet based noise reduction in CT-images using correlation analysis." IEEE transactions on medical imaging, Vol. 27 (No. 12), pp. 1685-703, (2008).
16- Maha Alafeef and Mohammad Fraiwan, "On the diagnosis of idiopathic Parkinson’s disease using continuous wavelet transform complex plot." Journal of Ambient Intelligence and Humanized Computing, Vol. 10 (No. 7), pp. 2805-15, (2019).
17- Michael Ljungberg, “The SIMIND Monte Carlo program. In: M Ljungberg S E Strand, M A King, editors. Monte Carlo calculations in nuclear medicine: Applications in diagnostic imaging.” 2nd ed. Boca Raton: CRC Press; p.111–128, (2012).
18- William Paul Segars, et al., “4D XCAT phantom for multimodality imaging research.” Medical Physics, Vol. 37(9):4902-15, (2010).
19- Mohammad Taghi Bahreyni Toossi, et al., “SIMIND Monte Carlo simulation of a single photon emission CT.” Journal of Medical Physics, Vol.35(1):42-7, (2010).
20- PMK Prasad and G Umamadhuri, "Biorthogonal wavelet-based image compression." in Artificial Intelligence and Evolutionary Computations in Engineering Systems: Springer, (2018), pp. 391-404.
21- Rahul Sahu and MP Parsai, "Comparison of digital image denoising method using various Transforms on Satellite Imagery." in 2019 2nd International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT), (2019), Vol. 1: IEEE, pp. 1391-99.
22- Nihaal Mehta et al., "Impact of binarization thresholding and brightness/contrast adjustment methodology on optical coherence tomography angiography image quantification." American journal of ophthalmology, Vol. 205pp. 54-65, (2019).
23- N Senthilkumaran and S Vaithegi, "Image segmentation by using thresholding techniques for medical images." Computer Science & Engineering: An International Journal, Vol. 6 (No. 1), pp. 1-13, (2016).
24- Diego Marcos et al., "Learning deep structured active contours end-to-end." in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (2018), pp. 8877-85.
25- Yunyi Tang and Yuanpeng Zhu, "Image zooming based on two classes of C1-continuous coons patches construction with shape parameters over triangular domain." Symmetry, Vol. 12 (No. 4), p. 661, (2020).
26- Boliang Yu et al., "HybraPD atlas: Towards precise subcortical nuclei segmentation using multimodality medical images in patients with Parkinson disease." Human brain mapping, Vol. 42 (No. 13), pp. 4399-421, (2021).
27- Nooshin Nabizadeh and Miroslav Kubat, "Brain tumors detection and segmentation in MR images: Gabor wavelet vs. statistical features." Computers & Electrical Engineering, Vol. 45pp. 286-301, (2015).
28- Yousif Abdallah, "Delineation of brain tumours for radiotherapy patients using image segmentation techniques." Onkologia i Radioterapia, Vol. 14 (No. 5), pp. 1-5, (2020).
29- Nilesh Bhaskarrao Bahadure, Arun Kumar Ray, and Har Pal Thethi, "Image analysis for MRI based brain tumor detection and feature extraction using biologically inspired BWT and SVM." International Journal of Biomedical Imaging, Vol. 2017(2017).
30- Andrius Sakalauskas, Vita Špečkauskienė, Kristina Laučkaitė, Rytis Jurkonis, Daiva Rastenytė, and Arūnas Lukoševičius, "Transcranial ultrasonographic image analysis system for decision support in parkinson disease." Journal of Ultrasound in Medicine, Vol. 37 (No. 7), pp. 1753-61, (2018).
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| Issue | Vol 11 No 2 (2024) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/fbt.v11i2.15335 | |
| Keywords | ||
| Parkinson’s Disease Single Photon Emission Computed Tomography Simulating Medical Imaging Nuclear Detectors Monte Carlo Wavelet Transformation Segmentation | ||
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How to Cite
1.
Saeidikia M, Seyedarabi H, Mahmoudian B, Pirayesh Islamian J. Improvement of Basal Ganglia Detectability in Brain Single Photon Emission Computerized Tomography by Wavelet Transformation in Image Processing Domain: A XCAT Phantom Study. Frontiers Biomed Technol. 2024;11(2):191-198.
