Automatic Lesion Segmentation of Multiple Sclerosis in Mri Images Using Supervised Classifier (original) (raw)
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Segmentation of multiple sclerosis lesions using support vector machines
2003
Background: Multiple Sclerosis (MS) syndrome is a type of Immune-Mediated disorder in the central nervous system (CNS) which destroys myelin sheaths, and results in plaque (lesion) formation in the brain. From the clinical point of view, investigating and monitoring information such as position, volume, number, and changes of these plaques are integral parts of the controlling process this disease over a period. Visualizing MS lesions in vivo with Magnetic Resonance Imaging (MRI) has a key role in observing the course of the disease. Material and Methods: In this analytical study, two different processing methods were present in this study in order to make an effort to detect and localize lesions in the patients' FLAIR (Fluid-attenuated inversion recovery) images. Segmentation was performed using Ensemble Support Vector Machine (SVM) classification. The trained data was randomly divided into five equal sections, and each section was fed into the computer as an input to one of the SVM classifiers that led to five different SVM structures. Results: To evaluate results of segmentation, some criteria have been investigated such as Dice, Jaccard, sensitivity, specificity, PPV and accuracy. Both modes of ESVM, including first and second ones have similar results. Dice criterion was satisfied much better with specialist's work and it is observed that Dice average has 0.57±.15 and 0.6±.12 values in the first and second approach, respectively. Conclusion: An acceptable overlap between those results reported by the neurologist and the ones obtained from the automatic segmentation algorithm was reached using an appropriate pre-processing in the proposed algorithm. Post-processing analysis further reduced false positives using morphological operations and also improved the evaluation criteria, including sensitivity and positive predictive value.
Journal of Medical Signals & Sensors, 2012
Multiple sclerosis (MS) is a progressive neurological disorder, which is caused by structural damages of axons and their myelin sheathes in the central nervous system. MS lesions present temporal changes in shape, location, and area among patients, and thus it is necessary for radiologists to accurately detect and evaluate MS lesions. [1] However, the accurate assessment of each lesion in magnetic resonance (MR) images would be a demanding and time-consuming task, and also a manual measurement could be subjective and have poor reproducibility. Therefore, a number of semi-automated or automated methods have been proposed for identifying and/or segmenting MS lesions in MR images. Khayati et al. [2] proposed an approach for fully-automated segmentation of MS lesions in fluid attenuated inversion recovery (FLAIR) MR images. The proposed approach, based on a Bayesian classifier, utilizes the adaptive mixtures method (AMM) and Markov random field (MRF) model to obtain and upgrade the class conditional probability density function (CCPDF) and a priori probability of each class.
PLoS ONE, 2014
Machine learning is a popular method for mining and analyzing large collections of medical data. We focus on a particular problem from medical research, supervised multiple sclerosis (MS) lesion segmentation in structural magnetic resonance imaging (MRI). We examine the extent to which the choice of machine learning or classification algorithm and feature extraction function impacts the performance of lesion segmentation methods. As quantitative measures derived from structural MRI are important clinical tools for research into the pathophysiology and natural history of MS, the development of automated lesion segmentation methods is an active research field. Yet, little is known about what drives performance of these methods. We evaluate the performance of automated MS lesion segmentation methods, which consist of a supervised classification algorithm composed with a feature extraction function. These feature extraction functions act on the observed T1-weighted (T1-w), T2-weighted (T2-w) and fluid-attenuated inversion recovery (FLAIR) MRI voxel intensities. Each MRI study has a manual lesion segmentation that we use to train and validate the supervised classification algorithms. Our main finding is that the differences in predictive performance are due more to differences in the feature vectors, rather than the machine learning or classification algorithms. Features that incorporate information from neighboring voxels in the brain were found to increase performance substantially. For lesion segmentation, we conclude that it is better to use simple, interpretable, and fast algorithms, such as logistic regression, linear discriminant analysis, and quadratic discriminant analysis, and to develop the features to improve performance.
Biomedical Signal Processing and Control, 2017
Multiple Sclerosis (MS) could be considered as one of the most serious neurological diseases that can cause damage to the central nervous system. Such pathology has increased dramatically during the past few years. Hence, MS exploration has captivated the interest of various research studies in clinical as well as technological fields such as medical imaging. In this context, this paper introduced a new MS exploration approach based on cerebral segmentation and MS lesion identification using the fusion of magnetic resonance (MRI) modalities sequences. The proposed segmentation approach is based on extracted volumetric features that could be deduced from the gray-level co-occurrence matrix (GLCM) and the gray-level run length (GLRLM) matrix. Volumetric features extraction would be performed by using new voxel wise techniques while preserving connectivity, spatial and shape information. In addition, our segmentation approach includes an optimized feature selection process combining the genetic algorithm (GA) and the support vector machine (SVM) tool in order to preserve only the essential features that could distinguish the main brain tissues and the MS lesions within both white matter and gray matter. The evaluation was carried out on four clinical databases. The results revealed an acceptable conformity with the ground truths compared to those of the usual methods Besides, our approach has proved its ability to select the most discriminative features, ensuring an acceptable cerebral segmentation (averages: Dice = 0.62 ± 0.11, true positive rate 'TPR' = 0.64 ± 0.12 and positive predictive value 'PPV' = 0.64 ± 0.14) and MS lesions identification with an acceptable accuracy rate (averages: Dice = 0.66 ± 0.07, TPR = 0.70 ± 0.12 and PPV = 0.67 ± 0.03). Based on these promising results, a computer aided diagnosis (CAD) system was henceforth conceived and could be useful for clinicians in order to carefully facilitate MS exploration. Such a helpful CAD system was really highly needed for clinical explorations and could be extended to other neurological pathologies such as Alzheimer's and Parkinson's diseases.
Automatic Segmentation of Multiple Sclerosis Lesions in Brain MR Images
Journal of Biomedical Engineering and Medical Imaging, 2015
We present, in the scope of this article, a contribution to the automatic extraction of Multiple Sclerosis (MS) lesions from MRI images (Magnetic Resonance Imaging). Our method is entirely automatic[1][2]. It is based on three steps : first, brain segmentation, then construction of Talairach atlas thanks to the determination of the CA-CP, VCA, and the interhemispheric plans, and finally the extraction of MS lesions by statistic analysis. Thus, the results we have obtained are close to 100%, even if some mistakes, linked to unexpected movements of the patient, can occur during the acquisition.
Unified Approach for Multiple Sclerosis Lesion Segmentation on Brain MRI
Annals of Biomedical Engineering, 2006
The presence of large number of false lesion classification on segmented brain MR images is a major problem in the accurate determination of lesion volumes in multiple sclerosis (MS) brains. In order to minimize the false lesion classifications, a strategy that combines parametric and nonparametric techniques is developed and implemented. This approach uses the information from the proton density (PD)-and T2-weighted and fluid attenuation inversion recovery (FLAIR) images. This strategy involves CSF and lesion classification using the Parzen window classifier. Image processing, morphological operations and ratio maps of PD and T2 weighted images are used for minimizing false positives. Contextual information is exploited for minimizing the false negative lesion classifications using hidden Markov random field -expectation maximization (HMRF-EM) algorithm. Lesions are delineated using fuzzy connectivity. The performance of this algorithm is quantitatively evaluated on 23 MS patients. Similarity index, percentages of over, under and correctestimations of lesions are computed by spatially comparing the results of present procedure with expert manual segmentation. The automated processing scheme detected 80% of the manually segmented lesions in the case of low-lesion load and 93% of the lesions in those cases with high lesion load.
International Journal of Cognitive Informatics and Natural Intelligence
This paper presents a segmentation method to detect multiple sclerosis (MS) lesions in brain MRI based on the artificial immune systems (AIS) and a support vector machines (SVM). In the first step, AIS is used to segment the three main brain tissues white matter, gray matter, and cerebrospinal fluid. Then the features were extracted and SVM is applied to detect the multiple sclerosis lesions based on SMO training algorithm. The experiments conducted on 3D brain MR images produce satisfying results.
Computer Methods and Programs in Biomedicine, 2017
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Highlights Accurate detection and segmentation of Multiple sclerosis (MS) diseases with lesions positions identification Adaptive background generation and binarization using global threshold are the key steps for MS lesions detection Evaluates performance with other recent method Proposed method produce good results visually as well as metrically Proposed method reduced the under segmentation, over segmentation, and spurious lesions generation
Semi-Automatic Segmentation of Multiple Sclerosis Lesion in 4D Modality
International Journal of Signal Processing Systems, 2017
The automatic and computerized recognition of Regions of Interest (ROI) is a crucial step in the process and analysis of medical images. The reasons are many and include the increase of available medical image data, the wide variety of devices and methods for image acquisition and the need to provide mechanisms making the analysis more accurate and the clinicians' job faster. Within the study on multiple sclerosis, the goal is the recognition of the damaged brain areas by processing images captured through magnetic resonance imaging. In this context, the proposed work is a study on the relationship between brain images obtained by magnetic resonance imaging, using different types of acquisitions. The goal is to understand whether it is somehow possible to identify the different regions of the brain, through a process of segmentation, using a method which allows the user's independence. The employed volumes are acquired in three different modalities T1-weighted, T2-weighted, and PD for synthetic database; T1-weighted, T2-weighted and FLAIR for real database. The purpose of this paper is to provide the doctor with a tool helping with diagnosis and detecting the possible areas of doubt. Two databases were taken into account, a synthetic one and a real one, and for the synthetic database the parameters of the confusion matrix have been calculated.
Automated Detection and Characterization of Multiple Sclerosis Lesions in Brain MR Images
Magnetic Resonance Imaging, 1998
In the present study an automatic algorithm for detection and contouring of multiple sclerosis (MS) lesions in brain magnetic resonance (MR) images is introduced. This algorithm automatically detects MS lesions in axial proton density, T 2 -weighted, gadolinium enhanced, and fast fluid attenuated inversion recovery (FLAIR) brain MR images. Automated detection consists of three main stages: (1) detection and contouring of all hyperintense signal regions within the image; (2) partial elimination of false positive segments (defined herein as artifacts) by size, shape index, and anatomical location; (3) the use of an artificial neural paradigm (Back-Propagation) for final removal of artifacts by differentiating them from true MS lesions. The algorithm was applied to 45 images acquired from 14 MS patients. The algorithm's sensitivity was 0.87 and the specificity 0.96. In 34 images, 100% of the lesions were detected. The algorithm potentially may serve as a useful preprocessing tool for quantitative MS monitoring via magnetic resonance imaging.