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Pidchayathanakorn P, Supratid S. An assessment of noise variance estimations in Bayes threshold denoising under stationary wavelet domain on brain lesions and tumor MRIs. DATA TECHNOLOGIES AND APPLICATIONS 2021. [DOI: 10.1108/dta-09-2020-0221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
PurposeA major key success factor regarding proficient Bayes threshold denoising refers to noise variance estimation. This paper focuses on assessing different noise variance estimations in three Bayes threshold models on two different characteristic brain lesions/tumor magnetic resonance imaging (MRIs).Design/methodology/approachHere, three Bayes threshold denoising models based on different noise variance estimations under the stationary wavelet transforms (SWT) domain are mainly assessed, compared to state-of-the-art non-local means (NLMs). Each of those three models, namely D1, GB and DR models, respectively, depends on the most detail wavelet subband at the first resolution level, on the entirely global detail subbands and on the detail subband in each direction/resolution. Explicit and implicit denoising performance are consecutively assessed by threshold denoising and segmentation identification results.FindingsImplicit performance assessment points the first–second best accuracy, 0.9181 and 0.9048 Dice similarity coefficient (Dice), sequentially yielded by GB and DR; reliability is indicated by 45.66% Dice dropping of DR, compared against 53.38, 61.03 and 35.48% of D1 GB and NLMs, when increasing 0.2 to 0.9 noise level on brain lesions MRI. For brain tumor MRI under 0.2 noise level, it denotes the best accuracy of 0.9592 Dice, resulted by DR; however, 8.09% Dice dropping of DR, relative to 6.72%, 8.85 and 39.36% of D1, GB and NLMs is denoted. The lowest explicit and implicit denoising performances of NLMs are obviously pointed.Research limitations/implicationsA future improvement of denoising performance possibly refers to creating a semi-supervised denoising conjunction model. Such model utilizes the denoised MRIs, resulted by DR and D1 thresholding model as uncorrupted image version along with the noisy MRIs, representing corrupted version ones during autoencoder training phase, to reconstruct the original clean image.Practical implicationsThis paper should be of interest to readers in the areas of technologies of computing and information science, including data science and applications, computational health informatics, especially applied as a decision support tool for medical image processing.Originality/valueIn most cases, DR and D1 provide the first–second best implicit performances in terms of accuracy and reliability on both simulated, low-detail small-size region-of-interest (ROI) brain lesions and realistic, high-detail large-size ROI brain tumor MRIs.
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Wang M, Li P, Liu F. Multi-atlas active contour segmentation method using template optimization algorithm. BMC Med Imaging 2019; 19:42. [PMID: 31126254 PMCID: PMC6534882 DOI: 10.1186/s12880-019-0340-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2018] [Accepted: 05/14/2019] [Indexed: 11/10/2022] Open
Abstract
Background Brain image segmentation is the basis and key to brain disease diagnosis, treatment planning and tissue 3D reconstruction. The accuracy of segmentation directly affects the therapeutic effect. Manual segmentation of these images is time-consuming and subjective. Therefore, it is important to research semi-automatic and automatic image segmentation methods. In this paper, we propose a semi-automatic image segmentation method combined with a multi-atlas registration method and an active contour model (ACM). Method We propose a multi-atlas active contour segmentation method using a template optimization algorithm. First, a multi-atlas registration method is used to obtain the prior shape information of the target tissue, and then a label fusion algorithm is used to generate the initial template. Second, a template optimization algorithm is used to reduce the multi-atlas registration errors and generate the initial active contour (IAC). Finally, a ACM is used to segment the target tissue. Results The proposed method was applied to the challenging publicly available MR datasets IBSR and MRBrainS13. In the MRBrainS13 datasets, we obtained an average thalamus Dice similarity coefficient of 0.927 ± 0.014 and an average Hausdorff distance (HD) of 2.92 ± 0.53. In the IBSR datasets, we obtained a white matter (WM) average Dice similarity coefficient of 0.827 ± 0.04 and a gray gray matter (GM) average Dice similarity coefficient of 0.853 ± 0.03. Conclusion In this paper, we propose a semi-automatic brain image segmentation method. The main contributions of this paper are as follows: 1) Our method uses a multi-atlas registration method based on affine transformation, which effectively reduces the multi-atlas registration time compared to the complex nonlinear registration method. The average registration time of each target image in the IBSR datasets is 255 s, and the average registration time of each target image in the MRBrainS13 datasets is 409 s. 2) We used a template optimization algorithm to improve registration error and generate a continuous IAC. 3) Finally, we used a ACM to segment the target tissue and obtain a smooth continuous target contour.
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Affiliation(s)
- Monan Wang
- School of Mechanical & Power Engineering, Harbin University of Science and Technology, Xue Fu Road No. 52, Nangang District, Harbin City, Heilongjiang Province, 150080, People's Republic of China.
| | - Pengcheng Li
- School of Mechanical & Power Engineering, Harbin University of Science and Technology, Xue Fu Road No. 52, Nangang District, Harbin City, Heilongjiang Province, 150080, People's Republic of China
| | - Fengjie Liu
- School of Mechanical & Power Engineering, Harbin University of Science and Technology, Xue Fu Road No. 52, Nangang District, Harbin City, Heilongjiang Province, 150080, People's Republic of China
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Khan SU, Ullah I, Ahmed I, Imran A, Ullah N. A spatial fuzzy C-means algorithm for segmentation of brain MRI images. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2019; 27:1087-1099. [PMID: 31561406 DOI: 10.3233/xst-190547] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Brain and its structure are extremely complex with deep levels of details. Applying image processing methods of brain image can be very useful in many practical domains. Magnetic Resonance Imaging (MRI) is widely used imaging technique and has particular advantage by possessing the capability of providing highly detailed images of brain soft tissues than any other imaging techniques. The real challenge at hand for researchers is to perform precise segmentation while overcoming the effects of noise and other imaging artifacts like intensity in homogeneity introduced in medical images during image acquisition process. In this research work, a directional weighted optimized Fuzzy C-Means (dwsFCM) method has been proposed for segmentation of brain MR images. This method works by incorporating the spatial information of the pixels of the images and assigning the directional weights to the neighborhood. In order to validate the proposed segmentation framework, a comprehensive set of experiments have been performed on publically available standard simulated as well as real datasets. The experimental results showed 95% of accuracy and the performance of the proposed segmentation framework is much better and the framework suppress the sufficient amount of noise especially rician noise and reproduce good segmentation by overcoming the effect of intensity in homogeneity.
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Affiliation(s)
| | | | - Imran Ahmed
- Institute of Management Sciences, Peshawar, Pakistan
| | - Ali Imran
- University of Science and Technology, Bannu, Pakistan
| | - Najeeb Ullah
- Cecos University of IT & Emerging Sciences, Peshawar, Pakistan
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Guerrout ELH, Ait-Aoudia S, Michelucci D, Mahiou R. Hidden Markov random field model and Broyden–Fletcher–Goldfarb–Shanno algorithm for brain image segmentation. J EXP THEOR ARTIF IN 2017. [DOI: 10.1080/0952813x.2017.1409280] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- EL-Hachemi Guerrout
- Laboratoire LMCS, Ecole nationale Supérieure en Informatique, Oued-Smar, Algeria
| | - Samy Ait-Aoudia
- Laboratoire LMCS, Ecole nationale Supérieure en Informatique, Oued-Smar, Algeria
| | | | - Ramdane Mahiou
- Laboratoire LMCS, Ecole nationale Supérieure en Informatique, Oued-Smar, Algeria
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Maglietta R, Amoroso N, Boccardi M, Bruno S, Chincarini A, Frisoni GB, Inglese P, Redolfi A, Tangaro S, Tateo A, Bellotti R. Automated hippocampal segmentation in 3D MRI using random undersampling with boosting algorithm. Pattern Anal Appl 2015; 19:579-591. [PMID: 27110218 PMCID: PMC4828512 DOI: 10.1007/s10044-015-0492-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2014] [Accepted: 06/07/2015] [Indexed: 11/10/2022]
Abstract
The automated identification of brain structure in Magnetic Resonance Imaging is very important both in neuroscience research and as a possible clinical diagnostic tool. In this study, a novel strategy for fully automated hippocampal segmentation in MRI is presented. It is based on a supervised algorithm, called RUSBoost, which combines data random undersampling with a boosting algorithm. RUSBoost is an algorithm specifically designed for imbalanced classification, suitable for large data sets because it uses random undersampling of the majority class. The RUSBoost performances were compared with those of ADABoost, Random Forest and the publicly available brain segmentation package, FreeSurfer. This study was conducted on a data set of 50 T1-weighted structural brain images. The RUSBoost-based segmentation tool achieved the best results with a Dice's index of [Formula: see text] ([Formula: see text]) for the left (right) brain hemisphere. An independent data set of 50 T1-weighted structural brain scans was used for an independent validation of the fully trained strategies. Again the RUSBoost segmentations compared favorably with manual segmentations with the highest performances among the four tools. Moreover, the Pearson correlation coefficient between hippocampal volumes computed by manual and RUSBoost segmentations was 0.83 (0.82) for left (right) side, statistically significant, and higher than those computed by Adaboost, Random Forest and FreeSurfer. The proposed method may be suitable for accurate, robust and statistically significant segmentations of hippocampi.
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Affiliation(s)
- Rosalia Maglietta
- />Istituto di Studi sui Sistemi Intelligenti per l’Automazione, Consiglio Nazionale delle Ricerche, Via G. Amendola 122, 70126 Bari, Italy
| | - Nicola Amoroso
- />Dipartimento Interateneo di Fisica M.Merlin, Universita’ degli Studi di Bari, Bari, Italy
- />Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
| | - Marina Boccardi
- />LENITEM Laboratory of Epidemiology, Neuroimaging and Telemedicine, IRCCS S.Giovanni di Dio, FBF, Brescia, Italy
| | | | - Andrea Chincarini
- />Istituto Nazionale di Fisica Nucleare, Sezione di Genova, Genova, Italy
| | - Giovanni B. Frisoni
- />LENITEM Laboratory of Epidemiology, Neuroimaging and Telemedicine, IRCCS S.Giovanni di Dio, FBF, Brescia, Italy
- />AFaR Associazione FateBeneFratelli per la Ricerca, Roma, Italy
- />Psychogeriatric Ward, IRCCS S.Giovanni di Dio, FBF, Brescia, Italy
| | - Paolo Inglese
- />Dipartimento Interateneo di Fisica M.Merlin, Universita’ degli Studi di Bari, Bari, Italy
- />Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
| | - Alberto Redolfi
- />LENITEM Laboratory of Epidemiology, Neuroimaging and Telemedicine, IRCCS S.Giovanni di Dio, FBF, Brescia, Italy
| | - Sabina Tangaro
- />Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
| | - Andrea Tateo
- />Dipartimento Interateneo di Fisica M.Merlin, Universita’ degli Studi di Bari, Bari, Italy
- />Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
| | - Roberto Bellotti
- />Dipartimento Interateneo di Fisica M.Merlin, Universita’ degli Studi di Bari, Bari, Italy
- />Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
| | - The Alzheimers Disease Neuroimaging Initiative
- />Istituto di Studi sui Sistemi Intelligenti per l’Automazione, Consiglio Nazionale delle Ricerche, Via G. Amendola 122, 70126 Bari, Italy
- />Dipartimento Interateneo di Fisica M.Merlin, Universita’ degli Studi di Bari, Bari, Italy
- />Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
- />LENITEM Laboratory of Epidemiology, Neuroimaging and Telemedicine, IRCCS S.Giovanni di Dio, FBF, Brescia, Italy
- />Overdale Hospital, Saint Helier, Jersey
- />Istituto Nazionale di Fisica Nucleare, Sezione di Genova, Genova, Italy
- />AFaR Associazione FateBeneFratelli per la Ricerca, Roma, Italy
- />Psychogeriatric Ward, IRCCS S.Giovanni di Dio, FBF, Brescia, Italy
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