Lesions Detection of Multiple Sclerosis in 3D Brian MR Images by Using Artificial Immune Systems and Support Vector Machines (original) (raw)
Related papers
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.
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.
Automatic Lesion Segmentation of Multiple Sclerosis in Mri Images Using Supervised Classifier
2020
ABSTRACT: Magnetic Resonance Imaging (MRI) can be used to detect lesions in the brains of Multiple Sclerosis (MS) patients and is imperative for diagnosing the disease and monitoring its progression. In practice, lesion load is often quantified by either manual or semi-automated segmentation of MRI, which is time-consuming, costly. We proposed model for automatic segmentation of multiple sclerosis lesions from brain MRI data. These techniques use a supervised classifier that is trained Support Vector Machine (SVM) to discriminate between the blocks in regions of MS lesions and the blocks in non-MS lesion regions mainly based on the textural features with aid of the other features. The main contribution of this set of frameworks is the use of textural features to detect MS lesions in a fully automated approach that does not rely on manually delineating the MS lesions. As a result, the sensitivity for detection of MS lesions was 81.5% with 2.9 false positives per slice based on a leav...
An Architecture For The Recognition And Classification Of Multiple Sclerosis Lesions In Images
1999
A software architecture is presented that is able to perform classification of focal lesions due to the multiple sclerosis disease in MR images of the brain. The methodology proceeds through four main steps: tissue segmentation, re-clustering and tissue classification, lesion localization and lesion classification. Images are first segmented using the FCM algorithm; then the images of each cluster are processed in order to classify and label non-pathologic tissues making use of simple decision algorithms based on suitable numerical indices related to tissue morphology. All possible candidates to be sclerosis lesions are then located by means of morphological operations applied to binary images of single tissues. Finally the classification step is performed together with an estimate of the position and the shape for each lesion. Each candidate has been characterized by means of a set of measurements related to its shape, position and brightness as a way to code the clinical knowledge about the disease under investigation. Classification is implemented using both an algorithmic classifier and a multi-layer perceptron trained using a back-propagation scheme, and the performances of the two approaches are compared. The outline of the whole architecture is presented and the experimental results are reported.
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.
A Bibliography of Multiple Sclerosis Lesions Detection Methods using Brain MRIs
arXiv (Cornell University), 2023
Introduction: Multiple Sclerosis (MS) is a chronic disease that affects millions of people across the globe. MS can critically affect different organs of the central nervous system such as the eyes, the spinal cord, and the brain. Background: To help physicians in diagnosing MS lesions, computer-aided methods are widely used. In this regard, a considerable research has been carried out in the area of automatic detection and segmentation of MS lesions in magnetic resonance images (MRIs). Methodology: In this study, we review the different approaches that have been used in computer-aided detection and segmentation of MS lesions. Our review resulted in categorizing MS lesion segmentation approaches into six broad categories: data-driven, statistical, supervised machine learning, unsupervised machine learning, fuzzy, and deep learning-based techniques. We critically analyze the different techniques under these approaches and highlight their strengths and weaknesses.
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.
Automated detection of multiple sclerosis lesions in the human brain using MR image processing
Neuroscience Letters, 1997
Introduction Multiple sclerosis (MS) is a serious disease typically occurring in the brain whose diagnosis and efficacy of treatment monitoring are vital. Magnetic resonance imaging (MRI) is frequently used in serial brain imaging due to the rich and detailed information provided. Methods Time-series analysis of images is widely used for MS diagnosis and patient follow-up. However, conventional manual methods are time-consuming, subjective, and errorprone. Thus, the development of automated techniques for the detection and quantification of MS lesions is a major challenge. Results This paper presents an up-to-date review of the approaches which deal with the time-series analysis of brain MRI for detecting active MS lesions and quantifying lesion load change. We provide a comprehensive reference source for researchers in which several approaches to change detection and quantification of MS lesions are investigated and classified. We also analyze the results provided by the approaches, discuss open problems, and point out possible future trends. Conclusion Lesion detection approaches are required for the detection of static lesions and for diagnostic purposes, while either quantification of detected lesions or change detection algorithms are needed to follow up MS patients. However, there is not yet a single approach that can emerge as a standard for the clinical practice, automatically providing an accurate MS lesion evolution quantification. Future trends will focus on combining the lesion detection in single studies with the analysis of the change detection in serial MRI.
Of Multiple Sclerosis Lesions in MR Images
A software architecture is presented that is able to perform classification of focal lesions due to the multiple sclerosis disease in MR images of the brain. The methodology proceeds through four main steps: tissue segmentation, re-clustering and tissue classification, lesion localization and lesion classification. Images are first segmented using the FCM algorithm; then the images of each cluster are processed in order to classify and label non-pathologic tissues making use of simple decision algorithms based on suitable numerical indices related to tissue morphology. All possible candidates to be sclerosis lesions are then located by means of morphological operations applied to binary images of single tissues. Finally the classification step is performed together with an estimate of the position and the shape for each lesion. Each candidate has been characterized by means of a set of measurements related to its shape, position and brightness as a way to code the clinical knowledge...
Eurasip Journal on Wireless Communications and Networking, 2008
This paper presents a local feature vector based method for automated Multiple Sclerosis (MS) lesion segmentation of multi spectral MRI data. Twenty datasets from MS patients with FLAIR, T1,T2, MD and FA data with expert annotations are available as training set from the MICCAI 2008 challenge on MS, and 24 test datasets. Our local feature vector method contains neighbourhood voxel