This document reviews various feature extraction and classification methods that have been used for the automated detection of Alzheimer's disease from magnetic resonance imaging (MRI) scans. It summarizes several studies that used different feature extraction techniques like voxel-based, vertex-based, and region of interest-based methods. Popular classification algorithms discussed include support vector machines, linear discriminant analysis, Bayesian classifiers and artificial neural networks. The document concludes that selecting relevant features extracted from MRI scans can yield accurate classification of Alzheimer's disease.
This document proposes a method for early detection of Alzheimer's disease using image processing techniques on brain MRI scans. It involves preprocessing MRI images, identifying regions of interest like the hippocampus and ventricles, segmenting images using techniques like thresholding and watershed segmentation to classify pixels as healthy or damaged tissue, and analyzing features like brain atrophy and enlarged ventricles to classify subjects as healthy, mild cognitive impairment, or Alzheimer's disease. The method was tested on 12 MRI samples and achieved 90% accuracy in detection. Future work could involve applying machine learning methods like neural networks to the image analysis for more accurate detection of the disease.
Mri brain tumour detection by histogram and segmentationiaemedu
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Mri brain tumour detection by histogram and segmentationiaemedu
This document summarizes a research paper on detecting brain tumors in MRI images using a combination of histogram thresholding, modified gradient vector field (GVF), and morphological operators. The non-brain regions are removed using morphological operators. Histogram thresholding is then used to detect if the brain is normal or abnormal/contains a tumor. If abnormal, the modified GVF is used to detect the tumor contour. The proposed method aims to be computationally efficient by only performing segmentation if a tumor is detected. It was tested on many MRI brain images and performance was validated against human expert segmentation.
Mri brain tumour detection by histogram and segmentation by modified gvf model 2IAEME Publication
The document describes a new method for detecting and segmenting brain tumors in MRI images. It combines histogram thresholding, modified gradient vector flow, and morphological operators. Histogram thresholding is used to detect if the brain is normal or abnormal. If abnormal, modified GVF is used to segment the tumor contour. Otherwise, segmentation is skipped to minimize computation time. The method was tested on many MRI brain images and tumor detection and dimensions were validated against expert segmentation. It provides an efficient and computationally inexpensive approach for brain tumor detection and segmentation in MRI images.
Mri brain tumour detection by histogram and segmentation by modified gvf model 2iaemedu
The document summarizes a proposed method for detecting brain tumors in MRI images in 4 steps:
1. Skull stripping and smoothing are performed to isolate the brain region.
2. Histogram thresholding is used to detect if the brain is normal or abnormal by comparing histograms of halves of the brain image.
3. For abnormal brains, a modified gradient vector flow (GVF) model is used to create a force field and detect the tumor contour.
4. The area of the tumor is then calculated. The method aims to minimize segmentation time by skipping segmentation for normal brains detected in Step 2. Validation with expert segmentation is performed.
Multistage Classification of Alzheimer’s DiseaseIJLT EMAS
Alzheimer’s disease is a type of dementia that destroys
memory and other mental functions. During the progression of
the disease certain proteins called plaques and tangles get
deposited in hippocampus which is located in the temporal lobe
of brain. The disease is not a normal part of aging and gets
worsen over time. Medical imaging techniques like Magnetic
Resonance Imaging (MRI), Computed Tomography (CT) and
Positron Emission Tomography (PET) play significant role in the
disease diagnosis. In this paper, we propose a method for
classifying MRI into Normal Control (NC), Mild Cognitive
Impairment (MCI) and Alzheimer’s Disease(AD). An overall
outline of the methodology includes textural feature extraction,
feature reduction process and classification of the images into
various stages. Classification has been performed with three
classifiers namely Support Vector Machine (SVM), Artificial
Neural Network (ANN) and k-Nearest Neighbours (k-NN)
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The analysis of MRI images is a manual process carried by experts which need to be automated to accurately classify the normal and abnormal images. We have proposed a reduced, three staged model having pre-processing, feature extraction and classification steps. In preprocessing the noise has been removed from grayscale images using a median filter, and then grayscale images have been converted to color (RGB) images. In feature extraction, red, green and blue channels from each channel of the RGB has been extracted because they are so much informative and easier to process. The first three color moments mean, variance, and skewness are calculated for each red, green and blue channel of images. The features extracted in the feature extraction stage are classified into normal and abnormal with K-Nearest Neighbors (k-NN). This method is applied to 100 images (70 normal, 30 abnormal). The proposed method gives 98.00% training and 95.00% test accuracy with datasets of normal images and 100% training and 90.00% test accuracy with abnormal images. The average computation time for each image was .06s.
This document reviews various techniques for detecting brain tumors in MRI images. It begins with an introduction to MRI and brain tumors. It then discusses several common methods for feature extraction (such as texture-based features using gray-level co-occurrence matrix) and classification (including neural networks, fuzzy c-means, k-nearest neighbors, support vector machines) that have been used for automated brain tumor detection. The document reviews 10 previous studies that detected brain tumors using techniques like segmentation, principal component analysis, probabilistic neural networks, and self-organizing maps. It then provides more detail on feature extraction methods, focusing on texture-based features.
This document proposes a method for early detection of Alzheimer's disease using image processing techniques on brain MRI scans. It involves preprocessing MRI images, identifying regions of interest like the hippocampus and ventricles, segmenting images using techniques like thresholding and watershed segmentation to classify pixels as healthy or damaged tissue, and analyzing features like brain atrophy and enlarged ventricles to classify subjects as healthy, mild cognitive impairment, or Alzheimer's disease. The method was tested on 12 MRI samples and achieved 90% accuracy in detection. Future work could involve applying machine learning methods like neural networks to the image analysis for more accurate detection of the disease.
Mri brain tumour detection by histogram and segmentationiaemedu
This document summarizes a research paper on detecting brain tumors in MRI images using a combination of histogram thresholding, modified gradient vector field (GVF), and morphological operators. The non-brain regions are removed using morphological operators. Histogram thresholding is then used to detect if the brain is normal or abnormal/contains a tumor. If abnormal, the modified GVF is used to detect the tumor contour. The proposed method aims to be computationally efficient by only performing segmentation if a tumor is detected. It was tested on many MRI brain images and performance was validated against human expert segmentation.
Mri brain tumour detection by histogram and segmentationiaemedu
This document summarizes a research paper on detecting brain tumors in MRI images using a combination of histogram thresholding, modified gradient vector field (GVF), and morphological operators. The non-brain regions are removed using morphological operators. Histogram thresholding is then used to detect if the brain is normal or abnormal/contains a tumor. If abnormal, the modified GVF is used to detect the tumor contour. The proposed method aims to be computationally efficient by only performing segmentation if a tumor is detected. It was tested on many MRI brain images and performance was validated against human expert segmentation.
Mri brain tumour detection by histogram and segmentation by modified gvf model 2IAEME Publication
The document describes a new method for detecting and segmenting brain tumors in MRI images. It combines histogram thresholding, modified gradient vector flow, and morphological operators. Histogram thresholding is used to detect if the brain is normal or abnormal. If abnormal, modified GVF is used to segment the tumor contour. Otherwise, segmentation is skipped to minimize computation time. The method was tested on many MRI brain images and tumor detection and dimensions were validated against expert segmentation. It provides an efficient and computationally inexpensive approach for brain tumor detection and segmentation in MRI images.
Mri brain tumour detection by histogram and segmentation by modified gvf model 2iaemedu
The document summarizes a proposed method for detecting brain tumors in MRI images in 4 steps:
1. Skull stripping and smoothing are performed to isolate the brain region.
2. Histogram thresholding is used to detect if the brain is normal or abnormal by comparing histograms of halves of the brain image.
3. For abnormal brains, a modified gradient vector flow (GVF) model is used to create a force field and detect the tumor contour.
4. The area of the tumor is then calculated. The method aims to minimize segmentation time by skipping segmentation for normal brains detected in Step 2. Validation with expert segmentation is performed.
Multistage Classification of Alzheimer’s DiseaseIJLT EMAS
Alzheimer’s disease is a type of dementia that destroys
memory and other mental functions. During the progression of
the disease certain proteins called plaques and tangles get
deposited in hippocampus which is located in the temporal lobe
of brain. The disease is not a normal part of aging and gets
worsen over time. Medical imaging techniques like Magnetic
Resonance Imaging (MRI), Computed Tomography (CT) and
Positron Emission Tomography (PET) play significant role in the
disease diagnosis. In this paper, we propose a method for
classifying MRI into Normal Control (NC), Mild Cognitive
Impairment (MCI) and Alzheimer’s Disease(AD). An overall
outline of the methodology includes textural feature extraction,
feature reduction process and classification of the images into
various stages. Classification has been performed with three
classifiers namely Support Vector Machine (SVM), Artificial
Neural Network (ANN) and k-Nearest Neighbours (k-NN)
A robust technique of brain mri classification using color features and k nea...Salam Shah
The analysis of MRI images is a manual process carried by experts which need to be automated to accurately classify the normal and abnormal images. We have proposed a reduced, three staged model having pre-processing, feature extraction and classification steps. In preprocessing the noise has been removed from grayscale images using a median filter, and then grayscale images have been converted to color (RGB) images. In feature extraction, red, green and blue channels from each channel of the RGB has been extracted because they are so much informative and easier to process. The first three color moments mean, variance, and skewness are calculated for each red, green and blue channel of images. The features extracted in the feature extraction stage are classified into normal and abnormal with K-Nearest Neighbors (k-NN). This method is applied to 100 images (70 normal, 30 abnormal). The proposed method gives 98.00% training and 95.00% test accuracy with datasets of normal images and 100% training and 90.00% test accuracy with abnormal images. The average computation time for each image was .06s.
This document reviews various techniques for detecting brain tumors in MRI images. It begins with an introduction to MRI and brain tumors. It then discusses several common methods for feature extraction (such as texture-based features using gray-level co-occurrence matrix) and classification (including neural networks, fuzzy c-means, k-nearest neighbors, support vector machines) that have been used for automated brain tumor detection. The document reviews 10 previous studies that detected brain tumors using techniques like segmentation, principal component analysis, probabilistic neural networks, and self-organizing maps. It then provides more detail on feature extraction methods, focusing on texture-based features.
IRJET- A Study on Brain Tumor Detection Algorithms for MRI ImagesIRJET Journal
This document discusses algorithms for detecting brain tumors in MRI images. It begins with an abstract that outlines the key stages of brain tumor detection using image processing techniques: pre-processing, segmentation, feature extraction, and classification. It then reviews several existing techniques for brain tumor segmentation and classification, noting their advantages and limitations. Specifically, it examines algorithms using Bayesian techniques, neural networks, clustering, and deep learning. The document proposes using a Spearman algorithm for segmentation combined with a convolutional neural network classifier to overcome limitations of other methods and provide more accurate tumor detection.
A Review on Brain Disorder Segmentation in MR ImagesIJMER
This document reviews various methods for automatically detecting brain tumors from MRI scans using computer-aided systems. It summarizes segmentation and classification approaches that have been used, including thresholding, region growing, genetic algorithms, clustering, and neural networks. The most common techniques are thresholding, region-based segmentation, and support vector machines or neural networks for classification. While these methods have achieved some success, challenges remain in developing systems that can accurately classify tumor types with high performance on diverse datasets. Future work may explore combining discrete and continuous segmentation approaches to improve computational efficiency and detection accuracy.
Classification of Abnormalities in Brain MRI Images Using PCA and SVMIJERA Editor
The impact of digital image processing is increasing by the day for its use in the medical and research areas. Medical image classification scheme has been on the increase in order to help physicians and medical practitioners in their evaluation and analysis of diseases. Several classification schemes such as Artificial Neural Network (ANN), Bayes Classification, Support Vector Machine (SVM) and K-Means Nearest Neighbor have been used. In this paper, we evaluate and compared the performance of SVM and PCA by analyzing diseased image of the brain (Alzheimer) and normal (MRI) brain. The results show that Principal Components Analysis outperforms the Support Vector Machine in terms of training time and recognition time.
The document describes a new computational method for diagnosing Alzheimer's disease (AD) using 3D brain magnetic resonance imaging (MRI) scans. The method involves two phases: 1) segmentation of brain tissues (white matter, grey matter, cerebrospinal fluid) using a convolutional neural network model with Gaussian mixture model input, and 2) classification of AD vs normal controls using a model that combines extreme gradient boosting and support vector machines. The method is evaluated on two datasets, achieving Dice scores of 0.96 for segmentation and accuracies of 0.88 and 0.80 for classification.
This document reviews various automated techniques that have been developed for brain tumor detection. It summarizes research done by several researchers on methods like sequential floating forward selection, color coding schemes using brain atlases, neural networks, region growing segmentation combined with area calculation, symmetry analysis of tumor areas in MRI images, and combining clustering and classification algorithms. The paper concludes that image segmentation plays an important role in medical applications like tumor diagnosis and that more robust techniques are needed for high accuracy and reliability.
This document presents an automatic brain tumor detection and segmentation scheme using MRI images. The proposed method involves 4 main stages: 1) preprocessing to reduce noise and improve image quality, 2) feature extraction of shape, intensity, and texture features, 3) tumor segmentation, and 4) post-processing including regularization and constraints. The method aims to identify and segment tumor portions of brain images successfully and with less time than manual methods. Evaluation results suggest it outperforms other peer methods in accuracy metrics.
An effective feature selection using improved marine predators algorithm for ...IJECEIAES
Alzheimer’s disease (AD) is an irremediable neurodegenerative illness developed by the fast deterioration of brain cells. AD is mostly common in elder people and it extremely disturbs the physical and mental health of patients, therefore early detection is essential to prevent AD development. However, the precise detection of AD and mild cognitive impairment (MCI) is difficult during classification. In this paper, the Residual network i.e., ResNet-18 is used for extracting the features, and the proposed improved marine predators algorithm (IMPA) is developed for choosing the optimum features to perform an effective classification of AD. The multi-verse optimizer (MVO) used in the IMPA helps to balance exploration and exploitation, which leads to the selection of optimal relevant features. Further, the classification of AD is accomplished using the multiclass support vector machine (MSVM). Open access series of imaging studies-1 (OASIS-1) and Alzheimer disease neuroimaging initiative (ADNI) datasets are used to evaluate the IMPA-MSVM method. The performance of the IMPA-MSVM method is analyzed using accuracy, sensitivity, specificity, positive predictive value (PPV) and matthews correlation coefficient (MCC). The existing methods such as the deep learning-based segmenting method using SegNet (DLSS), mish activation function (MAF) with spatial transformer network (STN) and BrainNet2D are used to evaluate the IMPA-MSVM method. The accuracy of IMPA-MSVM for the ADNI dataset is 98.43% which is more when compared to the DLSS and MAF-STN.
MRI Image Segmentation Using Gradient Based Watershed Transform In Level Set ...IJERA Editor
This document summarizes a research paper on segmenting MRI brain images using a gradient-based watershed transform within a level set method. The paper begins with an introduction on the importance of accurate brain image segmentation for medical diagnosis. It then reviews existing segmentation methods and their limitations. The proposed method uses a two-level gradient watershed transform combined with morphological operations within a level set framework to segment brain images. Experimental results showed this approach achieved better segmentation accuracy than traditional methods.
Efficient Brain Tumor Detection Using Wavelet TransformIJERA Editor
Brain tumor detection is a challenging task and its very important to analyze the structure of the tumor correctly so a automatic method is used now a days for the detection of the tumor. This method saves time as well as it reduces the error which occurs in the method of manual detection. In this paper the tumor is detected using wavelet transform. MRI is an important tool used in many fields of medicine and is capable of generating a detailed image of any part of the human body. The tumor is segmented from the MRI images, features are extracted and then the area of the tumor is determined. PNN can successfully handle the process of brain tumor classification
1) The document presents an integrated technique for detecting brain tumors in MRI images that combines modified texture-based region growing segmentation and edge detection.
2) The technique first performs pre-processing on MRI images, then uses modified texture-based region growing to segment regions. It then applies edge detection to extract the tumor region.
3) Experimental results show the integrated technique provides more accurate tumor detection compared to individual segmentation methods and manual segmentation.
This document presents a comparative study of two segmentation methods - k-means clustering and fuzzy c-means clustering with genetic algorithm - for detecting brain tumors in MRI images. K-means clustering is used to segment MRI images into clusters and identify tumor regions. Fuzzy c-means clustering with genetic algorithm aims to improve upon k-means by eliminating over-segmentation issues and providing faster, more efficient clustering results. The experimental results indicate fuzzy c-means performs better than k-means for brain tumor segmentation. The document also reviews several other related works applying techniques like edge detection and probabilistic neural networks to segment brain tumors from MRI scans.
Alzheimer’s detection through neuro imaging and subsequent fusion for clinica...IJECEIAES
This document summarizes a study that aims to detect Alzheimer's disease through neuroimaging and subsequently fuse magnetic resonance imaging (MRI) and positron emission tomography (PET) scans. The study first detects Alzheimer's from MRI scans by segmenting gray and white matter and calculating their volume ratios. Image fusion is then performed on the MRI scan detected with Alzheimer's and a corresponding PET scan. The fusion combines the two scans into a single image containing structural and anatomical information to aid clinical diagnosis. The proposed method achieved good performance metrics for the fused image, with a peak signal-to-noise ratio of 60.6 dB, mean square error of 0.0176, and other favorable values, suggesting it yields more informative results than individual scans
Fully Automatic Method for 3D T1-Weighted Brain Magnetic Resonance Images Seg...CSCJournals
In the domain of medical imaging, accurate segmentation of brain MR images is of interest for many brain disorders. However, due to several factors such noise, imaging artefacts, intrinsic tissue variation and partial volume effects, tissue segmentation remains a challenging task. So, in this paper, a full automatic method for segmentation of brain MR images is presented. The method consists of four steps segmentation procedure. First, noise removing by median filtering is done; second segmentation of brain/non-brain tissue is performed by using a Threshold Morphologic Brain Extraction method (TMBE). Then initial centroids estimation by gray level histogram analysis based is executed. Finally, Fuzzy C-means Algorithm is used for MRI tissue segmentation. The efficiency of the proposed method is demonstrated by extensive segmentation experiments using simulated and real MR images.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
SEGMENTATION OF MULTIPLE SCLEROSIS LESION IN BRAIN MR IMAGES USING FUZZY C-MEANSijaia
Magnetic resonance images (MRI) play an important role in supporting and substituting clinical
information in the diagnosis of multiple sclerosis (MS) disease by presenting lesion in brain MR images. In
this paper, an algorithm for MS lesion segmentation from Brain MR Images has been presented. We revisit
the modification of properties of fuzzy -c means algorithms and the canny edge detection. By changing and
reformed fuzzy c-means clustering algorithms, and applying canny contraction principle, a relationship
between MS lesions and edge detection is established. For the special case of FCM, we derive a sufficient
condition and clustering parameters, allowing identification of them as (local) minima of the objective
function.
Early Detection of Alzheimer’s Disease Using Machine Learning TechniquesIRJET Journal
This document discusses early detection of Alzheimer's disease using machine learning techniques. It proposes using deep learning models to classify brain MRI images from three planes (coronal, axial, sagittal) to detect brain damage related to Alzheimer's with 99.5% accuracy. This high-performing model is compared to other state-of-the-art machine and deep learning models. The methodology involves training deep learning and machine learning models on MRI datasets and evaluating their performance on test data.
Evaluation of Default Mode Network In Mild Cognitive Impairment and Alzheimer...CSCJournals
Although progressive functional brain network disorders has been one of the indication of Alzheimer's disease, The current research on aging and dementia focus on diagnostics of the cognitive changes of normal aging and Alzheimer Disease (AD), these changes known as Mild Cognitive Impairment (MCI). The default mode network (DMN) is a network of interacting brain regions known to have activity highly correlated with each other and distinct from other networks in the brain, the default mode network is active during passive rest and consists of a set of brain areas that are tightly functionally connected and distinct from other systems within the brain. Anatomically, the DMN includes the posterior cingulated cortex (PCC), dorsal and ventral medial prefrontal cortex, the lateral parietal cortex, and the medial temporal lobes. DMN involves multiple anatomical networks that converge on cortical hubs, such as the PCC, ventral medial prefrontal, and inferior parietal cortices. The aim of this study was to evaluate the default mode network functional connectivity in MCI patients. While no treatments are recommended for MCI currently, Mild Cognitive Impairment is becoming a very important subject for researchers and deserves more recognition and further study, In order to increase the ability to recognize earlier symptoms of Alzheimer's disease.
Automatic brain tumor detection using adaptive region growing with thresholdi...IAESIJAI
Brain cancer affects many people around the world. It's not just limited to the elderly; it is also recognized in children. With the development of image processing, early detection of mental development is possible. Some designers suggest deformable models, histogram averaging, or manual division. Due to constant manual intervention, these cycles can be uncomfortable and tiring. This research introduces a high-level system for the removal of malignant tumors from attractive reverberation images, based on a programmed and rapid distribution strategy for surface extraction and recreation for clinicians. To test the proposed system, acquired tomography images from the Cancer Imaging Archive were used. The results of the study strongly demonstrate that the intended structure is viable in brain tumor detection.
Brain tumor is a malformed growth of cells within brain which may be
cancerous or non-cancerous. The term ‘malformed’ indicates the existence of tumor. The
tumor may be benign or malignant and it needs medical support for further classification.
Brain tumor must be detected, diagnosed and evaluated in earliest stage. The medical
problems become grave if tumor is detected at the later stage. Out of various technologies
available for diagnosis of brain tumor, MRI is the preferred technology which enables the
diagnosis and evaluation of brain tumor. The current work presents various clustering
techniques that are employed to detect brain tumor. The classification involves classification
of images into normal and malformed (if detected the tumor). The algorithm deals with
steps such as preprocessing, segmentation, feature extraction and classification of MR brain
images. Finally, the confirmatory step is specifying the tumor area by technique called
region of interest.
The key elements of the Christian worldview are faith, love, forgiveness, and living in Christ. These elements are fundamental to Christ's teachings and reflect the author's own worldview. Living in Christ incorporates aspects like praise, prayer, and witnessing. However, the Christian worldview should focus more on redemption than reconciling the Bible with science. The overarching themes of the Bible can be summarized as creation, humanity, sin/fall, and redemption.
The document provides instructions for requesting writing assistance from HelpWriting.net. It outlines a 5-step process: 1) Create an account with a password and email. 2) Complete a 10-minute order form providing instructions, sources, and deadline. 3) Review bids from writers and choose one based on qualifications. 4) Review the completed paper and authorize payment if satisfied. 5) Request revisions until needs are fully met, with a refund option for plagiarized content.
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Evaluation of Default Mode Network In Mild Cognitive Impairment and Alzheimer...CSCJournals
Although progressive functional brain network disorders has been one of the indication of Alzheimer's disease, The current research on aging and dementia focus on diagnostics of the cognitive changes of normal aging and Alzheimer Disease (AD), these changes known as Mild Cognitive Impairment (MCI). The default mode network (DMN) is a network of interacting brain regions known to have activity highly correlated with each other and distinct from other networks in the brain, the default mode network is active during passive rest and consists of a set of brain areas that are tightly functionally connected and distinct from other systems within the brain. Anatomically, the DMN includes the posterior cingulated cortex (PCC), dorsal and ventral medial prefrontal cortex, the lateral parietal cortex, and the medial temporal lobes. DMN involves multiple anatomical networks that converge on cortical hubs, such as the PCC, ventral medial prefrontal, and inferior parietal cortices. The aim of this study was to evaluate the default mode network functional connectivity in MCI patients. While no treatments are recommended for MCI currently, Mild Cognitive Impairment is becoming a very important subject for researchers and deserves more recognition and further study, In order to increase the ability to recognize earlier symptoms of Alzheimer's disease.
Automatic brain tumor detection using adaptive region growing with thresholdi...IAESIJAI
Brain cancer affects many people around the world. It's not just limited to the elderly; it is also recognized in children. With the development of image processing, early detection of mental development is possible. Some designers suggest deformable models, histogram averaging, or manual division. Due to constant manual intervention, these cycles can be uncomfortable and tiring. This research introduces a high-level system for the removal of malignant tumors from attractive reverberation images, based on a programmed and rapid distribution strategy for surface extraction and recreation for clinicians. To test the proposed system, acquired tomography images from the Cancer Imaging Archive were used. The results of the study strongly demonstrate that the intended structure is viable in brain tumor detection.
Brain tumor is a malformed growth of cells within brain which may be
cancerous or non-cancerous. The term ‘malformed’ indicates the existence of tumor. The
tumor may be benign or malignant and it needs medical support for further classification.
Brain tumor must be detected, diagnosed and evaluated in earliest stage. The medical
problems become grave if tumor is detected at the later stage. Out of various technologies
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diagnosis and evaluation of brain tumor. The current work presents various clustering
techniques that are employed to detect brain tumor. The classification involves classification
of images into normal and malformed (if detected the tumor). The algorithm deals with
steps such as preprocessing, segmentation, feature extraction and classification of MR brain
images. Finally, the confirmatory step is specifying the tumor area by technique called
region of interest.
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A Review On Methods For Feature Extraction And Classification For The Automated Detection Of Alzheimer S Disease
1. IOSR Journal of Engineering (IOSRJEN) www.iosrjen.org
ISSN (e): 2250-3021, ISSN (p): 2278-8719
Vol. 11, Issue 1, January 2021, ||Series -I|| PP 22-27
International organization of Scientific Research 22 | P a g e
A Review on Methods for Feature Extraction and Classification
for the Automated Detection of Alzheimer’s Disease
Nirupama P. Ansingkar1
, Rita B. Patil2
, Prapti D. Deshmukh
Department of CS and IT, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, Maharashtra.
Received 15 January 2021; Accepted 31 January 2021
Abstract: Alzheimer Disease (AD) is a type of Dementia which is a neurodegenerative progressive disorder.
No treatment can stop or prevent the growth of this disease. Magnetic Resonance Imaging (MRI) is the best tool
for the early detection of AD. This paper presents the review of feature extraction and classification techniques
used for the early detection of AD which are used earlier. Proposed algorithm for the early detection of AD is
also stated in the paper.
Keywords: Alzheimer Disease, Mild Cognitive Impairment, Magnetic Resonance Imaging, ALFF
I. INTRODUCTION
Human “Nervous System” involved control and coordination of various body functions. It consists of
highly specialized cells called Neurons. These are the cells which detect and receive information from different
sensory organs and integrate them to determine the mod of response of a living body.
Dementia is a syndrome which is a chronic and decline in cognitive function due to the damage in
brain cells. Now a days it becomes a major global health and social threat. According to the survey of World
Health Organization (WHO) done in the year 2019, 50 million people have dementia and every year 10 million
new cases add into it. It causes due to deterioration in memory, thinking behaviour and the ability to think. The
early symptoms of dementia include memory problem, difficulty in word finding, lack of initiative, changes in
personality or behaviour and day today’s function at home or at work [1-3].
Dementia is classified into various types namely Vascular Dementia, Dementia with Lewy Bodies,
front-temporal lobar degeneration, mixed Dementia, Parkinson’s disease, Alzheimer disease[4-5]. Most
common type of Dementia is an Alzheimer’s disease. It affects 60-80% people over the age of 65[6].
Alzheimer is the fastest growing disease that causes death of brain cells. This disease interrupts the
travelling of neurotransmitters. Thus, the neurons fail to pass the signals between brain and sensory organs. This
happens because of two proteins in the brain such as Beta amyloid which aids to develop amyloid plaques and
Tau which develops tangles in brain cells [7-9].
Alzheimer disease is a neurodegenerative disorder that is characterised by progressive cognitive and
functional deficits. No treatment stops or reverses its progression [10-11]. In the developed countries, with the
increase in elderly population, Alzheimer disease is going to be a major problem in socioeconomic implications.
According to a recent survey, affected people will be doubled in the next 20 years. Therefore, the diagnosis of
the AD in the early stage is very important [12].
The precise diagnosis and early detection of AD is a difficult task. Another difficulty is caused by
confusion of non-AD syndromes of Dementia. The Mild Cognitive Impairment (MCI) is a prodromal stage of
AD. It is observed that MCI patients are at high risk of AD progression. [13-14]
Neuroimaging techniques such as Magnetic Resonance Imaging (MRI) and Positron Emission
Tomography (PET) have been widely used in the assessment of AD.[15-20]. Combine use of neuroimaging
techniques with selected biomarkers can contribute to the early and specific diagnosis of AD. MR Imaging is
considered the preferred neuroimaging examination for AD as it allows for accurate measurement of 3D volume
of brain structure, specifically the size of hippocampus and related regions.
II. LITERATURE REVIEW
Pre-processing: Pre-processing is the first stage. The main aim of pre-processing in the brain MR images is
error removal and MR image enhancement. The initial stage of pre-processing generally includes the steps
normalization, noise removal , Segmentation. In the case of neuroimaging, skull stripping and enhancement is
the main task of image enhancement. The enhancement is done by modifying intensities of pixels [21-23].
2. A Review on Methods for Feature Extraction and Classification for the Automated ..
International organization of Scientific Research 23 | P a g e
Figure 1 Block Diagram of General Framework of the Steps in the Diagnosis of Alzheimer’s Disease
This paper presents the study of various methods or approaches for feature extraction and classification
which will be useful for the constructive completion of advance study on Alzheimer Disease.
Feature Extraction: The main goal of feature extractor is to characterise an object which is to be
recognised by measuring the similar object in the same category. They are supposed to be simple to extract,
invariant to irrelevant transformations, incentive to noise and useful for discriminating patterns in different
category. Different approaches are used for extraction of features from MRI data. The approaches are voxel-
based, vertex-based, and ROI-based. There are various methods to find out the relevant features for the
classification of healthy and unhealthy brain.
Amulya et al. Compare and evaluate the different methods of feature extraction. The three approaches
were compared through Linear Discriminant Algorithm (LDA), Support Vector Machine (SVM) , Bay’s
classifier and ANN classifiers on MRI data. Pre-processing is done for the skull stripping to remove non brain
tissues, noise reduction, normalization before feature extraction. Markov Random Field is used [24].
Akhila J A et al. has done the segmentation based feature extraction on OASIS database of 40
subjects. Segmentation based Fractral Texture Analysis (SFTA) method is used for feature extraction. three
features are extracted from binary image. The features are size, mean grey scale and the dimensions of the
fractals obtained from binary image. Classification is done with feed forward Neural Network with 97.5%
accuracy and precision of 0.975 is obtained [25].
Shuai Mao et al used ALFF and ReHO parameters. Each voxel value is divided in these two
parameters DDARSE software tool is used for feature extraction and selection. ADNI MRI dataset is used . in
pre-processing bandpass filters and spatial smoothening filters are used for normalization. Feature components
are sorted using Fisher Score Algorithm[26].
Jesia Mathew et al. done the pre-processing with VBM8 tool box which helps in Voxel-based
morphometry of MRI. Pre-processing steps like Reorientation, cropping, skull stripping, image normalization
are done on the MRI images. Pre-processing image is segmented into grey and white matter. Feature Extraction
is done with DWT & Principle Component Algorithm (PCA) and Classification is done with SVM [27].
Chuanchuan Zheng et al. reviewed the different methods of feature extraction methods like voxel-
based, vertex based and ROI-based methods. LDA, Baysian, SVM and Artificial Neural Network (ANN) are
used for classification in the diagnosis of normal and diseased brain [28].
Ayşe Demirhan1, Talia M. Nir et al. Used voxel-based feature extraction method based on segmented
tissues probability maps using directly the voxel of the tissue probability maps as a feature using stand score[29-
30].
3. A Review on Methods for Feature Extraction and Classification for the Automated ..
International organization of Scientific Research 24 | P a g e
Seixas FL et al was used vertex-based feature extraction method. It denotes the difference between NC,
MCI and AD. In this method cortial thickness shows a direct index of atropy caused by dementia.[31-33]
Thies W et al worked on ROI-based method by using segmentation done preferably before feature
extraction Chupin et al developed fully automated segmentation method SACHA which automatically segments
hippocampus and amygdale based on competitive region growing between these two structures [34-37].
Fukunaga K et al used LDA which is one of popular dimensionality reduction method. It achieved
83% sensitivity, 84% accuracy and 86% specificity on SPECT images[38].
Classification: Plant et al combined a feature selection with classification using bays classifier for the
discrimination between AD and NC on MRI data which achieve 92% accuracy [39].
Cuingnet R et al used SVM algorithm for classification . It constructs a hyper plane or a set of hyper
plane in an infinite – dimensional space which can be used for classification. SVM lower generalization error
than other classifier and hence commonly used to solve pattern classification problems which have limited
training samples[40-42].
Dukart J et al used meta-analysis based SVM to diagnose AD and NC on MRI and PET data which
achieved accuracy of 90.0% , sensitivity of 91.8% and specificity of 87.8%[43].
Deng X et al showed that using ANN can get higher sensitivity and accuracy in dementia classification
for MRI images[44].
Table1Comparison of Feature Extraction and Classification Methods from Literature in the Identification of
Alzheimer’s Disease
Sr.
No
.
Paper Moda
lity
AD Diagnosis Techniques Used Dataset Accura
cy
Highlights of
paper
1. Amulya
E.R. et. al.
[24]
MRI Feature
Extraction and
feature
reduction
GLCM and Gabor
filter for feature
extraction and PCA,
LDA and SVM for
feature reduction
OASIS and
ADNI
-- This paper
represents the
proposed
method with
review of
feature
extraction
methods.
2. Akhila J A
et. al. [25]
MRI Segmentation
based feature
extraction and
classification
Segmentation Based
Fractal texture
Analysis (SFTA)
and Classification
with a feed forward
artificial neural
network.
OASIS
dataset
with 40
subjects
accurac
y of
97.5%
and
precisi
on of
0.975
Features are
extracted from
binary image
by breaking
image into two
threshols using
Bnary
Decomposition
Algorithm
3. Shuai Mao
et al [26]
MRI Feature
extraction,
feature
selection and
classification
Functional
correlation between
different ROI and
SVM is used for
classification
ADNI 40-
subjects
20-Normal
20- AD
97.5 is
the
recogni
tion
rate
Feature Score
Algorithm is
used for
sorting
features.
ALFF and
ReHo features
are taken for
feature
extraction
4. Jesia
Mathew et.
al. [27]
MRI Feature
extraction and
classification
DWT coupled with
PCA for
feature extraction
and SVM for its
classification
ADNI
NC=71
AD=87
-- Pre-processing
is done with
VBM8 toolbox
5. Chuanchua MRI Feature voxel-based, vertex- --
4. A Review on Methods for Feature Extraction and Classification for the Automated ..
International organization of Scientific Research 25 | P a g e
n Zheng et.
al.
28
extraction and
classification
based, and ROI-
based feature
extraction
methods and LDA-
based, Bayesian,
SVM-based, and
ANN-based pattern
classification
methods
III. CONCLUSION
This paper provide a brief review which is based on the comparison and evaluation of related work
done to detect Alzheimer’s disease using MRI. Earlier detection of dementia is very essential in today’s world
which becomes a major global health and social threat. Early detection of Alzheimer Disease would increase
the life expectancy in the community of elderly people. Various feature extraction and classification methods
are used to extract the features and classify Alzheimer’s Disease from MRI. The selection of relevant features
yields accurate classification result. Table I shows the analysis of the related works.
REFERENCES
[1]. Chuanchuan Zheng et al., “ Automated Identification of Dementia Using Medical Imaging: A Survey
from Pattern Classification Perspective”, Brain informatics(2016), 3:17-27 DOI 10.1007/s 40708-05-
0027 Springer.
[2]. American Psychiatric Association and American Psychiatric Association (1994) Task Force on DSM-IV;
Diagnostic and statistical manual of mental disorders: DSMIV 4th edition. American Psychiatric
Association Washington DC.
[3]. Suhuai Luo et al . ,“Automatic Alzheimer Disease Recognition MRI Data Using Deep Learning Method”
, journal of applied mathematics and physics 2017,5, 1892-1898 ISSN Print: 2327-4352.
[4]. S.R.Bhagyashree et al. , “ An Initial Investigation in the Diagnosis of Alzeimer’s Disease using Various
Classification Techniques”. 978-1-4799-3975-6/14 2014 IEEE.
[5]. Viswanathan.A. et al. , “ Vascular Risk Factors And Dementia: How to Move Forward ?” Neurology 72:
Pp 368-74,2009.
[6]. Ronghui Ju et al. , “ Early Diagnosis of Alzeimr’s Disease Based on Resting-State Brain Networks And
Deep Learning”,IEEE/ACM Translations on Computational Biology And Bioinformatics DOI
10.1109/TCVPBB2017.
[7]. A.Burns et al ., “ Alzheimer’s Disease” BMJ338B158 DOI : 10.1136/BMJ B158TMID19196745,
February 2009.
[8]. Ahila Arumugam Annakutty et al., “ Review of Brain Imaging Techniques, Feature Extraction and
Classification Algorithms to Identify Alzheimer’s Disease,” International Journal of Pharma Medicine
And Biological Sciences Vol.5,Number 3, July 2016.
[9]. Alzheimer’s Disease and Dementia , Alzheimer’s Association.
[10]. Collin C. LUK et al., “ Alzheumer’s Disease: 3-Dimensional MRI Texture for Prediction of Conversion
from Mild Cognitive Impairment”, 2352-87292018, ELSEVIER.
[11]. “Dementia Fact Sheet Number 362”, World Health Organisation April-2012 Retrieved 28 November
2014.
[12]. Alzheimer’s Association, “2016 Alzheimer’s Disease Facts And Figures” Alzheimer’s And Dementia
Vol.12 Number 4, Pp 459-509,2016.
[13]. Dubois B. Et al, “ Research Criteria for the diagnosis of Alzheimer’s Disease: Revising the
NINCDSADRDA criteria. The Lancet Neurology, 2007;6(8):734-746.[PubMed: 17616482].
[14]. Siq. Liu, Sidong Liu et al. “ Multimodal Neuroimaging Feature Learnin for Multi-class Diagnosis of
Alzheimer’s Disease” , IEEE Trans. Biomed Eng. 2015 april : 62x(4) : 1132-1140, boi: ten 1109/TBME
2014. 2372011.
[15]. Liu S. et al IEEE International Symposium On Biomedical Imaging: from Nano to Micro (ISBI 2013)
IEEE 2013. Multichannel Brain Atrophy Pattern Analysis in Neuroimaging Retrieval ;T206- 209.
[16]. Liu S. et al IEEE International Symposium on Biomedical Imaging: from Nano to Micro (ISBI 2013)
IEEE 2013. Neuroimaging Biomarker Based Prediction of Alzheimer's Disease Severity With Optimised
Graph Construction P1324-1327.
[17]. Cai W. et al International Symposium on Biomedical Imaging: from Nano to Micro (ISBI 2013) IEEE
2014. 3D Difference of Gaussian Based Lesion Detector for Brain PET P 677-680.
6. A Review on Methods for Feature Extraction and Classification for the Automated ..
International organization of Scientific Research 27 | P a g e
[43]. Dukart J et al (2013) Meta-analysis based SVM classification enables accurate detection of Alzheimer’s
disease across different clinical centres using FDG-PET and MRI. Psychiatry Res 212(3):230–236
[44]. Deng X et al (1998) Application of artificial neural network in the MRI study of Alzheimer disease. Chin
J Radiol, pp 812–816
Nirupama P. Ansingkar, et. al. “A Review on Methods for Feature Extraction and Classification for
the Automated Detection of Alzheimer’s Disease.” IOSR Journal of Engineering (IOSRJEN), 11(01),
2021, pp. 22-27.