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Raghavendra U, Gudigar A, Paul A, Goutham TS, Inamdar MA, Hegde A, Devi A, Ooi CP, Deo RC, Barua PD, Molinari F, Ciaccio EJ, Acharya UR. Brain tumor detection and screening using artificial intelligence techniques: Current trends and future perspectives. Comput Biol Med 2023; 163:107063. [PMID: 37329621 DOI: 10.1016/j.compbiomed.2023.107063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 05/16/2023] [Accepted: 05/19/2023] [Indexed: 06/19/2023]
Abstract
A brain tumor is an abnormal mass of tissue located inside the skull. In addition to putting pressure on the healthy parts of the brain, it can lead to significant health problems. Depending on the region of the brain tumor, it can cause a wide range of health issues. As malignant brain tumors grow rapidly, the mortality rate of individuals with this cancer can increase substantially with each passing week. Hence it is vital to detect these tumors early so that preventive measures can be taken at the initial stages. Computer-aided diagnostic (CAD) systems, in coordination with artificial intelligence (AI) techniques, have a vital role in the early detection of this disorder. In this review, we studied 124 research articles published from 2000 to 2022. Here, the challenges faced by CAD systems based on different modalities are highlighted along with the current requirements of this domain and future prospects in this area of research.
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Affiliation(s)
- U Raghavendra
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India
| | - Anjan Gudigar
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India.
| | - Aritra Paul
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India
| | - T S Goutham
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India
| | - Mahesh Anil Inamdar
- Department of Mechatronics, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India
| | - Ajay Hegde
- Consultant Neurosurgeon Manipal Hospitals, Sarjapur Road, Bangalore, India
| | - Aruna Devi
- School of Education and Tertiary Access, University of the Sunshine Coast, Caboolture Campus, Australia
| | - Chui Ping Ooi
- School of Science and Technology, Singapore University of Social Sciences, Singapore, 599494, Singapore
| | - Ravinesh C Deo
- School of Mathematics, Physics, and Computing, University of Southern Queensland, Springfield, QLD, 4300, Australia
| | - Prabal Datta Barua
- Cogninet Brain Team, Cogninet Australia, Sydney, NSW, 2010, Australia; School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Toowoomba, QLD, 4350, Australia; Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, 2007, Australia
| | - Filippo Molinari
- Department of Electronics and Telecommunications, Politecnico di Torino, 10129, Torino, Italy
| | - Edward J Ciaccio
- Department of Medicine, Columbia University Medical Center, New York, NY, 10032, USA
| | - U Rajendra Acharya
- School of Mathematics, Physics, and Computing, University of Southern Queensland, Springfield, QLD, 4300, Australia; International Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto, 860-8555, Japan
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De A, Wang X, Zhang Q, Wu J, Cong F. An efficient memory reserving-and-fading strategy for vector quantization based 3D brain segmentation and tumor extraction using an unsupervised deep learning network. Cogn Neurodyn 2023; 18:1-22. [PMID: 37362765 PMCID: PMC10132803 DOI: 10.1007/s11571-023-09965-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 02/24/2023] [Accepted: 03/08/2023] [Indexed: 06/28/2023] Open
Abstract
Deep learning networks are state-of-the-art approaches for 3D brain image segmentation, and the radiological characteristics extracted from tumors are of great significance for clinical diagnosis, treatment planning, and treatment outcome evaluation. However, two problems have been the hindering factors in brain image segmentation techniques. One is that deep learning networks require large amounts of manually annotated data. Another issue is the computational efficiency of 3D deep learning networks. In this study, we propose a vector quantization (VQ)-based 3D segmentation method that employs a novel unsupervised 3D deep embedding clustering (3D-DEC) network and an efficiency memory reserving-and-fading strategy. The VQ-based 3D-DEC network is trained on volume data in an unsupervised manner to avoid manual data annotation. The memory reserving-and-fading strategy beefs up model efficiency greatly. The designed methodology makes deep learning-based model feasible for biomedical image segmentation. The experiment is divided into two parts. First, we extensively evaluate the effectiveness and robustness of the proposed model on two authoritative MRI brain tumor databases (i.e., IBSR and BrainWeb). Second, we validate the model using real 3D brain tumor data collected from our institute for clinical practice significance. Results show that our method (without data manual annotation) has superior accuracy (0.74 ± 0.04 Tanimoto coefficient on IBSR, 97.5% TP and 97.7% TN on BrainWeb, and 91% Dice, 88% sensitivity and 87% specificity on real brain data) and remarkable efficiency (speedup ratio is 18.72 on IBSR, 31.16 on BrainWeb, 31.00 on real brain data) compared to the state-of-the-art methods. The results show that our proposed model can address the lacks of manual annotations, and greatly increase computation speedup with competitive segmentation accuracy compared to other state-of-the-art 3D CNN models. Moreover, the proposed model can be used for tumor treatment follow-ups every 6 months, providing critical details for surgical and postoperative treatment by correctly extracting numerical radiomic features of tumors.
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Affiliation(s)
- Ailing De
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, 116000 Liaoning China
| | - Xiulin Wang
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, 116000 Liaoning China
- School of Biomedical Engineering, Faculty of Medicine, Dalian University of Technology, Dalian, 116000 Liaoning China
| | - Qing Zhang
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, 116000 Liaoning China
| | - Jianlin Wu
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, 116000 Liaoning China
| | - Fengyu Cong
- School of Biomedical Engineering, Faculty of Medicine, Dalian University of Technology, Dalian, 116000 Liaoning China
- Faculty of Information Technology, University of Jyväskylä, 40014 Jyväskylä, Finland
- School of Artificial Intelligence, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116000 Liaoning China
- Key Laboratory of Integrated Circuit and Biomedical Electronic System, Dalian University of Technology, Dalian, 116000 Liaoning China
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Alshathri S, Abd Elaziz M, Yousri D, Hassan OF, Ibrahim RA. Quantum Chaotic Honey Badger Algorithm for Feature Selection. ELECTRONICS 2022; 11:3463. [DOI: 10.3390/electronics11213463] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Abstract
Determining the most relevant features is a critical pre-processing step in various fields to enhance prediction. To address this issue, a set of feature selection (FS) techniques have been proposed; however, they still have certain limitations. For example, they may focus on nearby points, which lowers classification accuracy because the chosen features may include noisy features. To take advantage of the benefits of the quantum-based optimization technique and the 2D chaotic Hénon map, we provide a modified version of the honey badger algorithm (HBA) called QCHBA. The ability of such strategies to strike a balance between exploitation and exploration while identifying the workable subset of pertinent features is the basis for employing them to enhance HBA. The effectiveness of QCHBA was evaluated in a series of experiments conducted using eighteen datasets involving comparison with recognized FS techniques. The results indicate high efficiency of the QCHBA among the datasets using various performance criteria.
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Kataria P, Dogra A, Sharma T, Goyal B. Trends in DNN Model Based Classification and Segmentation of Brain Tumor Detection. Open Neuroimag J 2022. [DOI: 10.2174/18744400-v15-e2206290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Background:
Due to the complexities of scrutinizing and diagnosing brain tumors from MR images, brain tumor analysis has become one of the most indispensable concerns. Characterization of a brain tumor before any treatment, such as radiotherapy, requires decisive treatment planning and accurate implementation. As a result, early detection of brain tumors is imperative for better clinical outcomes and subsequent patient survival.
Introduction:
Brain tumor segmentation is a crucial task in medical image analysis. Because of tumor heterogeneity and varied intensity patterns, manual segmentation takes a long time, limiting the use of accurate quantitative interventions in clinical practice. Automated computer-based brain tumor image processing has become more valuable with technological advancement. With various imaging and statistical analysis tools, deep learning algorithms offer a viable option to enable health care practitioners to rule out the disease and estimate the growth.
Methods:
This article presents a comprehensive evaluation of conventional machine learning models as well as evolving deep learning techniques for brain tumor segmentation and classification.
Conclusion:
In this manuscript, a hierarchical review has been presented for brain tumor segmentation and detection. It is found that the segmentation methods hold a wide margin of improvement in the context of the implementation of adaptive thresholding and segmentation methods, the feature training and mapping requires redundancy correction, the input data training needs to be more exhaustive and the detection algorithms are required to be robust in terms of handling online input data analysis/tumor detection.
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A Fuzzy Consensus Clustering Algorithm for MRI Brain Tissue Segmentation. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12157385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Brain tissue segmentation is an important component of the clinical diagnosis of brain diseases using multi-modal magnetic resonance imaging (MR). Brain tissue segmentation has been developed by many unsupervised methods in the literature. The most commonly used unsupervised methods are K-Means, Expectation-Maximization, and Fuzzy Clustering. Fuzzy clustering methods offer considerable benefits compared with the aforementioned methods as they are capable of handling brain images that are complex, largely uncertain, and imprecise. However, this approach suffers from the intrinsic noise and intensity inhomogeneity (IIH) in the data resulting from the acquisition process. To resolve these issues, we propose a fuzzy consensus clustering algorithm that defines a membership function resulting from a voting schema to cluster the pixels. In particular, we first pre-process the MRI data and employ several segmentation techniques based on traditional fuzzy sets and intuitionistic sets. Then, we adopted a voting schema to fuse the results of the applied clustering methods. Finally, to evaluate the proposed method, we used the well-known performance measures (boundary measure, overlap measure, and volume measure) on two publicly available datasets (OASIS and IBSR18). The experimental results show the superior performance of the proposed method in comparison with the recent state of the art. The performance of the proposed method is also presented using a real-world Autism Spectrum Disorder Detection problem with better accuracy compared to other existing methods.
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Pseudo-Label-Assisted Self-Organizing Maps for Brain Tissue Segmentation in Magnetic Resonance Imaging. J Digit Imaging 2022; 35:180-192. [PMID: 35018537 PMCID: PMC8921351 DOI: 10.1007/s10278-021-00557-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 11/26/2021] [Accepted: 11/27/2021] [Indexed: 10/19/2022] Open
Abstract
Brain tissue segmentation in magnetic resonance imaging volumes is an important image processing step for analyzing the human brain. This paper presents a novel approach named Pseudo-Label Assisted Self-Organizing Map (PLA-SOM) that enhances the result produced by a base segmentation method. Using the output of a base method, PLA-SOM calculates pseudo-labels in order to keep inter-class separation and intra-class compactness in the training phase. For the mapping phase, PLA-SOM uses a novel fuzzy function that combines feature space learned by the SOM's prototypes, topological ordering from the map, and spatial information from a brain atlas. We assessed PLA-SOM performance on synthetic and real MRIs of the brain, obtained from the BrainWeb and the Internet Brain Image Repository datasets. The experimental results showed evidence of segmentation improvement achieved by the proposed method over six different base methods. The best segmentation improvements reported by PLA-SOM on synthetic brain scans are 11%, 6%, and 4% for the tissue classes cerebrospinal fluid, gray matter, and white matter, respectively. On real brain scans, PLA-SOM achieved segmentation enhancements of 15%, 5%, and 12% for cerebrospinal fluid, gray matter, and white matter, respectively.
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Fayaz M, Torokeldiev N, Turdumamatov S, Qureshi MS, Qureshi MB, Gwak J. An Efficient Methodology for Brain MRI Classification Based on DWT and Convolutional Neural Network. SENSORS 2021; 21:s21227480. [PMID: 34833556 PMCID: PMC8619601 DOI: 10.3390/s21227480] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Revised: 11/01/2021] [Accepted: 11/08/2021] [Indexed: 12/21/2022]
Abstract
In this paper, a model based on discrete wavelet transform and convolutional neural network for brain MR image classification has been proposed. The proposed model is comprised of three main stages, namely preprocessing, feature extraction, and classification. In the preprocessing, the median filter has been applied to remove salt-and-pepper noise from the brain MRI images. In the discrete wavelet transform, discrete Harr wavelet transform has been used. In the proposed model, 3-level Harr wavelet decomposition has been applied on the images to remove low-level detail and reduce the size of the images. Next, the convolutional neural network has been used for classifying the brain MR images into normal and abnormal. The convolutional neural network is also a prevalent classification method and has been widely used in different areas. In this study, the convolutional neural network has been used for brain MRI classification. The proposed methodology has been applied to the standard dataset, and for performance evaluation, we have used different performance evaluation measures. The results indicate that the proposed method provides good results with 99% accuracy. The proposed method results are then presented for comparison with some state-of-the-art algorithms where simply the proposed method outperforms the counterpart algorithms. The proposed model has been developed to be used for practical applications.
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Affiliation(s)
- Muhammad Fayaz
- Department of Computer Science, University of Central Asia, 310 Lenin Street, Naryn 722918, Kyrgyzstan; (M.F.); (M.S.Q.)
| | - Nurlan Torokeldiev
- Department of Mathematics and Natural Sciences, University of Central Asia, Khorog 736, Tajikistan;
| | - Samat Turdumamatov
- Department of Mathematics and Natural Sciences, University of Central Asia, 310 Lenin Street, Naryn 722918, Kyrgyzstan;
| | - Muhammad Shuaib Qureshi
- Department of Computer Science, University of Central Asia, 310 Lenin Street, Naryn 722918, Kyrgyzstan; (M.F.); (M.S.Q.)
| | - Muhammad Bilal Qureshi
- Department of Computer Science and IT, University of Lakki Marwat, Lakki Marwat 28420, KPK, Pakistan;
| | - Jeonghwan Gwak
- Department of Software, Korea National University of Transportation, Chungju 27469, Korea
- Department of Biomedical Engineering, Korea National University of Transportation, Chungju 27469, Korea
- Department of AI Robotics Engineering, Korea National University of Transportation, Chungju 27469, Korea
- Department of IT & Energy Convergence (BK21 FOUR), Korea National University of Transportation, Chungju 27469, Korea
- Correspondence: ; Tel.: +82-43-841-5852
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Ibrahim RA, Abualigah L, Ewees AA, Al-qaness MAA, Yousri D, Alshathri S, Abd Elaziz M. An Electric Fish-Based Arithmetic Optimization Algorithm for Feature Selection. ENTROPY 2021; 23:e23091189. [PMID: 34573818 PMCID: PMC8472813 DOI: 10.3390/e23091189] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Revised: 09/05/2021] [Accepted: 09/06/2021] [Indexed: 11/16/2022]
Abstract
With the widespread use of intelligent information systems, a massive amount of data with lots of irrelevant, noisy, and redundant features are collected; moreover, many features should be handled. Therefore, introducing an efficient feature selection (FS) approach becomes a challenging aim. In the recent decade, various artificial methods and swarm models inspired by biological and social systems have been proposed to solve different problems, including FS. Thus, in this paper, an innovative approach is proposed based on a hybrid integration between two intelligent algorithms, Electric fish optimization (EFO) and the arithmetic optimization algorithm (AOA), to boost the exploration stage of EFO to process the high dimensional FS problems with a remarkable convergence speed. The proposed EFOAOA is examined with eighteen datasets for different real-life applications. The EFOAOA results are compared with a set of recent state-of-the-art optimizers using a set of statistical metrics and the Friedman test. The comparisons show the positive impact of integrating the AOA operator in the EFO, as the proposed EFOAOA can identify the most important features with high accuracy and efficiency. Compared to the other FS methods whereas, it got the lowest features number and the highest accuracy in 50% and 67% of the datasets, respectively.
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Affiliation(s)
- Rehab Ali Ibrahim
- Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt; (R.A.I.); (M.A.E.)
| | - Laith Abualigah
- Faculty of Computer Sciences and Informatics, Amman Arab University, Amman 11953, Jordan;
| | - Ahmed A. Ewees
- Department of Computer, Damietta University, Damietta 34517, Egypt;
| | - Mohammed A. A. Al-qaness
- State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China;
| | - Dalia Yousri
- Electrical Engineering Department, Faculty of Engineering, Fayoum University, Fayoum 63514, Egypt;
| | - Samah Alshathri
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 84428, Saudi Arabia
- Correspondence:
| | - Mohamed Abd Elaziz
- Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt; (R.A.I.); (M.A.E.)
- Artificial Intelligence Research Center (AIRC), Ajman University, Ajman 346, United Arab Emirates
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Aranguren I, Valdivia A, Morales-Castañeda B, Oliva D, Abd Elaziz M, Perez-Cisneros M. Improving the segmentation of magnetic resonance brain images using the LSHADE optimization algorithm. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2020.102259] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Segmentation of MRI brain scans using spatial constraints and 3D features. Med Biol Eng Comput 2020; 58:3101-3112. [PMID: 33155095 DOI: 10.1007/s11517-020-02270-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2019] [Accepted: 09/08/2020] [Indexed: 10/23/2022]
Abstract
This paper presents a novel unsupervised algorithm for brain tissue segmentation in magnetic resonance imaging (MRI). The proposed algorithm, named Gardens2, adopts a clustering approach to segment voxels of a given MRI into three classes: cerebrospinal fluid (CSF), gray matter (GM), and white matter (WM). Using an overlapping criterion, 3D feature descriptors and prior atlas information, Gardens2 generates a segmentation mask per class in order to parcellate the brain tissues. We assessed our method using three neuroimaging datasets: BrainWeb, IBSR18, and IBSR20, the last two provided by the Internet Brain Segmentation Repository. Its performance was compared with eleven well established as well as newly proposed unsupervised segmentation methods. Overall, Gardens2 obtained better segmentation performance than the rest of the methods in two of the three databases and competitive results when its performance was measured by class. Graphical Abstract Brain tissue segmentation using 3D features and an adjusted atlas template.
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Zhou Z, He Z, Jia Y. AFPNet: A 3D fully convolutional neural network with atrous-convolution feature pyramid for brain tumor segmentation via MRI images. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2020.03.097] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Vijh S, Sharma S, Gaurav P. Brain Tumor Segmentation Using OTSU Embedded Adaptive Particle Swarm Optimization Method and Convolutional Neural Network. DATA VISUALIZATION AND KNOWLEDGE ENGINEERING 2020. [DOI: 10.1007/978-3-030-25797-2_8] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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Isselmou AEK, Xu G, Zhang S. Improved Methods for Brain Tumor Detection and Analysis Using MR Brain Images. BIOMEDICAL AND PHARMACOLOGY JOURNAL 2019; 12:1621-1631. [DOI: 10.13005/bpj/1793] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Abstract
Medical image processing techniques play an important role in helping doctors and facilities for patient diagnosis, the aim of this paper is comparison between three improved methods to identify the brain tumor using magnetic resonance brain images and analysis of the performance of each method according to different values, accuracy, nJaccard coeff, ndice, sensitivity, specificity, recall and precision values,We used three improved methods the first method improved fuzzy c-means algorithm (IFCM), the second method is improved feed-forward neural network (IFFNN), and the third method is a hybrid self-organizing map with a fuzzy k-means algorithm,the significance of these methods is complementary among them where each one has an advantage in a certain value as shown in the paper results, the three methods gave a very good performance, generally they can identify the tumor area clearly in MR brain image with different performance of the values, each method gave better values than others according to a comparison between the performance value of three methods,Finally, the improved methods allow the development of algorithms to diagnose a tumor more accurately and for a short period of time and each method is distinguished from each other in the performance and value, this gives integrity and strength to this work, these methods can be used in pre and post radio surgical applications
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Affiliation(s)
- Abd El Kader Isselmou
- Department of Biomedical Engineering, Hebei University of Technology, Tianjin City, China, 30030
| | - Guizhi Xu
- Dean of School of Electrical Engineering, Hebei University of Technology, Tianjin City, China, 30030
| | - Shuai Zhang
- Vice Dean of School of Electrical Engineering, Hebei University of Technology, Tianjin City, China,30030
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Rundo L, Tangherloni A, Cazzaniga P, Nobile MS, Russo G, Gilardi MC, Vitabile S, Mauri G, Besozzi D, Militello C. A novel framework for MR image segmentation and quantification by using MedGA. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2019; 176:159-172. [PMID: 31200903 DOI: 10.1016/j.cmpb.2019.04.016] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2018] [Revised: 04/14/2019] [Accepted: 04/16/2019] [Indexed: 06/09/2023]
Abstract
BACKGROUND AND OBJECTIVES Image segmentation represents one of the most challenging issues in medical image analysis to distinguish among different adjacent tissues in a body part. In this context, appropriate image pre-processing tools can improve the result accuracy achieved by computer-assisted segmentation methods. Taking into consideration images with a bimodal intensity distribution, image binarization can be used to classify the input pictorial data into two classes, given a threshold intensity value. Unfortunately, adaptive thresholding techniques for two-class segmentation work properly only for images characterized by bimodal histograms. We aim at overcoming these limitations and automatically determining a suitable optimal threshold for bimodal Magnetic Resonance (MR) images, by designing an intelligent image analysis framework tailored to effectively assist the physicians during their decision-making tasks. METHODS In this work, we present a novel evolutionary framework for image enhancement, automatic global thresholding, and segmentation, which is here applied to different clinical scenarios involving bimodal MR image analysis: (i) uterine fibroid segmentation in MR guided Focused Ultrasound Surgery, and (ii) brain metastatic cancer segmentation in neuro-radiosurgery therapy. Our framework exploits MedGA as a pre-processing stage. MedGA is an image enhancement method based on Genetic Algorithms that improves the threshold selection, obtained by the efficient Iterative Optimal Threshold Selection algorithm, between the underlying sub-distributions in a nearly bimodal histogram. RESULTS The results achieved by the proposed evolutionary framework were quantitatively evaluated, showing that the use of MedGA as a pre-processing stage outperforms the conventional image enhancement methods (i.e., histogram equalization, bi-histogram equalization, Gamma transformation, and sigmoid transformation), in terms of both MR image enhancement and segmentation evaluation metrics. CONCLUSIONS Thanks to this framework, MR image segmentation accuracy is considerably increased, allowing for measurement repeatability in clinical workflows. The proposed computational solution could be well-suited for other clinical contexts requiring MR image analysis and segmentation, aiming at providing useful insights for differential diagnosis and prognosis.
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Affiliation(s)
- Leonardo Rundo
- Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy; Institute of Molecular Bioimaging and Physiology, Italian National Research Council, Cefalù, PA, Italy; Department of Radiology, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Centre, Cambridge, UK.
| | - Andrea Tangherloni
- Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy; Department of Haematology, University of Cambridge, Cambridge, UK; Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK.
| | - Paolo Cazzaniga
- Department of Human and Social Sciences, University of Bergamo, Bergamo, Italy; SYSBIO.IT Centre of Systems Biology, Milan, Italy.
| | - Marco S Nobile
- Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy; SYSBIO.IT Centre of Systems Biology, Milan, Italy.
| | - Giorgio Russo
- Institute of Molecular Bioimaging and Physiology, Italian National Research Council, Cefalù, PA, Italy.
| | - Maria Carla Gilardi
- Institute of Molecular Bioimaging and Physiology, Italian National Research Council, Cefalù, PA, Italy.
| | - Salvatore Vitabile
- Department of Biomedicine, Neuroscience and Advanced Diagnostics, University of Palermo, Palermo, Italy.
| | - Giancarlo Mauri
- Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy; SYSBIO.IT Centre of Systems Biology, Milan, Italy.
| | - Daniela Besozzi
- Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy.
| | - Carmelo Militello
- Institute of Molecular Bioimaging and Physiology, Italian National Research Council, Cefalù, PA, Italy.
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Narayanan A, Rajasekaran MP, Zhang Y, Govindaraj V, Thiyagarajan A. Multi-channeled MR brain image segmentation: A novel double optimization approach combined with clustering technique for tumor identification and tissue segmentation. Biocybern Biomed Eng 2019. [DOI: 10.1016/j.bbe.2018.12.003] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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16
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Ortiz A, Munilla J, Martínez-Murcia FJ, Górriz JM, Ramírez J. Empirical Functional PCA for 3D Image Feature Extraction Through Fractal Sampling. Int J Neural Syst 2019; 29:1850040. [DOI: 10.1142/s0129065718500405] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Medical image classification is currently a challenging task that can be used to aid the diagnosis of different brain diseases. Thus, exploratory and discriminative analysis techniques aiming to obtain representative features from the images play a decisive role in the design of effective Computer Aided Diagnosis (CAD) systems, which is especially important in the early diagnosis of dementia. In this work, we present a technique that allows using specific time series analysis techniques with 3D images. This is achieved by sampling the image using a fractal-based method which preserves the spatial relationship among voxels. In addition, a method called Empirical functional PCA (EfPCA) is presented, which combines Empirical Mode Decomposition (EMD) with functional PCA to express an image in the space spanned by a basis of empirical functions, instead of using components computed by a predefined basis as in Fourier or Wavelet analysis. The devised technique has been used to classify images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the Parkinson Progression Markers Initiative (PPMI), achieving accuracies up to 93% and 92% differential diagnosis tasks (AD versus controls and PD versus Controls, respectively). The results obtained validate the method, proving that the information retrieved by our methodology is significantly linked to the diseases.
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Affiliation(s)
- Andrés Ortiz
- Communications Engineering Department, University of Málaga, Málaga 29071, Spain
| | - Jorge Munilla
- Communications Engineering Department, University of Málaga, Málaga 29071, Spain
| | | | - Juan M. Górriz
- Department of Signal Theory, Communications and Networking, University of Granada, Granada 18060, Spain
| | - Javier Ramírez
- Department of Signal Theory, Communications and Networking, University of Granada, Granada 18060, Spain
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Ahmadvand A, Daliri MR, Hajiali M. DCS-SVM: a novel semi-automated method for human brain MR image segmentation. ACTA ACUST UNITED AC 2018; 62:581-590. [PMID: 27930360 DOI: 10.1515/bmt-2015-0226] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2015] [Accepted: 10/17/2016] [Indexed: 11/15/2022]
Abstract
In this paper, a novel method is proposed which appropriately segments magnetic resonance (MR) brain images into three main tissues. This paper proposes an extension of our previous work in which we suggested a combination of multiple classifiers (CMC)-based methods named dynamic classifier selection-dynamic local training local Tanimoto index (DCS-DLTLTI) for MR brain image segmentation into three main cerebral tissues. This idea is used here and a novel method is developed that tries to use more complex and accurate classifiers like support vector machine (SVM) in the ensemble. This work is challenging because the CMC-based methods are time consuming, especially on huge datasets like three-dimensional (3D) brain MR images. Moreover, SVM is a powerful method that is used for modeling datasets with complex feature space, but it also has huge computational cost for big datasets, especially those with strong interclass variability problems and with more than two classes such as 3D brain images; therefore, we cannot use SVM in DCS-DLTLTI. Therefore, we propose a novel approach named "DCS-SVM" to use SVM in DCS-DLTLTI to improve the accuracy of segmentation results. The proposed method is applied on well-known datasets of the Internet Brain Segmentation Repository (IBSR) and promising results are obtained.
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Saliency Detection and Deep Learning-Based Wildfire Identification in UAV Imagery. SENSORS 2018; 18:s18030712. [PMID: 29495504 PMCID: PMC5876738 DOI: 10.3390/s18030712] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/08/2017] [Revised: 02/16/2018] [Accepted: 02/19/2018] [Indexed: 11/16/2022]
Abstract
An unmanned aerial vehicle (UAV) equipped with global positioning systems (GPS) can provide direct georeferenced imagery, mapping an area with high resolution. So far, the major difficulty in wildfire image classification is the lack of unified identification marks, the fire features of color, shape, texture (smoke, flame, or both) and background can vary significantly from one scene to another. Deep learning (e.g., DCNN for Deep Convolutional Neural Network) is very effective in high-level feature learning, however, a substantial amount of training images dataset is obligatory in optimizing its weights value and coefficients. In this work, we proposed a new saliency detection algorithm for fast location and segmentation of core fire area in aerial images. As the proposed method can effectively avoid feature loss caused by direct resizing; it is used in data augmentation and formation of a standard fire image dataset 'UAV_Fire'. A 15-layered self-learning DCNN architecture named 'Fire_Net' is then presented as a self-learning fire feature exactor and classifier. We evaluated different architectures and several key parameters (drop out ratio, batch size, etc.) of the DCNN model regarding its validation accuracy. The proposed architecture outperformed previous methods by achieving an overall accuracy of 98%. Furthermore, 'Fire_Net' guarantied an average processing speed of 41.5 ms per image for real-time wildfire inspection. To demonstrate its practical utility, Fire_Net is tested on 40 sampled images in wildfire news reports and all of them have been accurately identified.
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Nilashi M, Ibrahim O, Ahmadi H, Shahmoradi L, Farahmand M. A hybrid intelligent system for the prediction of Parkinson's Disease progression using machine learning techniques. Biocybern Biomed Eng 2018. [DOI: 10.1016/j.bbe.2017.09.002] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Mohan G, Subashini MM. MRI based medical image analysis: Survey on brain tumor grade classification. Biomed Signal Process Control 2018. [DOI: 10.1016/j.bspc.2017.07.007] [Citation(s) in RCA: 196] [Impact Index Per Article: 32.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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22
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Aparajeeta J, Nanda PK, Das N. Modified possibilistic fuzzy C-means algorithms for segmentation of magnetic resonance image. Appl Soft Comput 2016. [DOI: 10.1016/j.asoc.2015.12.003] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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23
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Vishnuvarthanan G, Rajasekaran MP, Subbaraj P, Vishnuvarthanan A. An unsupervised learning method with a clustering approach for tumor identification and tissue segmentation in magnetic resonance brain images. Appl Soft Comput 2016. [DOI: 10.1016/j.asoc.2015.09.016] [Citation(s) in RCA: 101] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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24
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Analyses of a cirrhotic patient's evolution using self organizing mapping and Child-Pugh scoring. J Med Syst 2015; 39:17. [PMID: 25644668 DOI: 10.1007/s10916-014-0188-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2014] [Accepted: 12/29/2014] [Indexed: 10/24/2022]
Abstract
Due to the importance of cirrhosis evolution, this study examined cirrhotic patients using Self Organizing Mapping (SOM) based on the Child-Pugh scoring method. Because Colored Doppler Ultrasound (CDU) has too many parameters, scoring can be a very difficult task. Classifying cirrhotic patients via SOM and investigating weights of the cirrhotic CDU parameters are aimed in this study. SOM was used to map high dimensional cirrhotic data onto two dimensional clustered data. These clusters provided a feature map of cirrhotic patients. In this study, 103 cirrhotic patients and a control group of 44 healthy individuals were examined in the hospital, and parameters were analyzed using SOM. These data were obtained using CDU, and age and sex parameters were analyzed in this study. Cirrhotic patients were histopathologically separated into subgroups using the Child-Pugh scoring method, and the presence of ascites was determined using SOM. In this study, differences between the control group and cirrhotic patients with their subgroups were investigated using SOM, and the results were discussed. Renal artery indices, hepatic artery indices, portal vein parameters, age and the degree of ascites were analyzed using SOM for a total of 147 individuals. The combination of SOM and Child-Pugh scoring method can be useful for the interpretation of cirrhotic patient's evolution. Computer-based SOM algorithm and negative effectiveness of a large scale dataset could be minimized by adjusting the weight of the parameters. This study will faciliate doctors to make better decisions for their patients.
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25
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De A, Guo C. An adaptive vector quantization approach for image segmentation based on SOM network. Neurocomputing 2015. [DOI: 10.1016/j.neucom.2014.02.069] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Valverde S, Oliver A, Cabezas M, Roura E, Lladó X. Comparison of 10 brain tissue segmentation methods using revisited IBSR annotations. J Magn Reson Imaging 2014; 41:93-101. [PMID: 24459099 DOI: 10.1002/jmri.24517] [Citation(s) in RCA: 65] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2013] [Accepted: 10/22/2013] [Indexed: 11/10/2022] Open
Abstract
PURPOSE Ground-truth annotations from the well-known Internet Brain Segmentation Repository (IBSR) datasets consider Sulcal cerebrospinal fluid (SCSF) voxels as gray matter. This can lead to bias when evaluating the performance of tissue segmentation methods. In this work we compare the accuracy of 10 brain tissue segmentation methods analyzing the effects of SCSF ground-truth voxels on accuracy estimations. MATERIALS AND METHODS The set of methods is composed by FAST, SPM5, SPM8, GAMIXTURE, ANN, FCM, KNN, SVPASEG, FANTASM, and PVC. Methods are evaluated using original IBSR ground-truth and ranked by means of their performance on pairwise comparisons using permutation tests. Afterward, the evaluation is repeated using IBSR ground-truth without considering SCSF. RESULTS The Dice coefficient of all methods is affected by changes in SCSF annotations, especially on SPM5, SPM8 and FAST. When not considering SCSF voxels, SVPASEG (0.90 ± 0.01) and SPM8 (0.91 ± 0.01) are the methods from our study that appear more suitable for gray matter tissue segmentation, while FAST (0.89 ± 0.02) is the best tool for segmenting white matter tissue. CONCLUSION The performance and the accuracy of methods on IBSR images vary notably when not considering SCSF voxels. The fact that three of the most common methods (FAST, SPM5, and SPM8) report an important change in their accuracy suggest to consider these differences in labeling for new comparative studies.
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Affiliation(s)
- Sergi Valverde
- Department of Computer Architecture and Technology, University of Girona, Girona, (Spain)
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Ortiz A, Palacio AA, Górriz JM, Ramírez J, Salas-González D. Segmentation of brain MRI using SOM-FCM-based method and 3D statistical descriptors. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2013; 2013:638563. [PMID: 23762192 PMCID: PMC3666364 DOI: 10.1155/2013/638563] [Citation(s) in RCA: 43] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/12/2013] [Accepted: 04/15/2013] [Indexed: 12/17/2022]
Abstract
Current medical imaging systems provide excellent spatial resolution, high tissue contrast, and up to 65535 intensity levels. Thus, image processing techniques which aim to exploit the information contained in the images are necessary for using these images in computer-aided diagnosis (CAD) systems. Image segmentation may be defined as the process of parcelling the image to delimit different neuroanatomical tissues present on the brain. In this paper we propose a segmentation technique using 3D statistical features extracted from the volume image. In addition, the presented method is based on unsupervised vector quantization and fuzzy clustering techniques and does not use any a priori information. The resulting fuzzy segmentation method addresses the problem of partial volume effect (PVE) and has been assessed using real brain images from the Internet Brain Image Repository (IBSR).
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Affiliation(s)
- Andrés Ortiz
- Communications Engineering Department, University of Malaga, 29004 Malaga, Spain
| | - Antonio A. Palacio
- Communications Engineering Department, University of Malaga, 29004 Malaga, Spain
| | - Juan M. Górriz
- Department of Signal Theory, Communications and Networking, University of Granada, 18060 Granada, Spain
| | - Javier Ramírez
- Department of Signal Theory, Communications and Networking, University of Granada, 18060 Granada, Spain
| | - Diego Salas-González
- Department of Signal Theory, Communications and Networking, University of Granada, 18060 Granada, Spain
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