A segmentation technique to detect the Alzheimer's disease using image processing

2016 ◽  
Author(s):  
R. Anitha ◽  
Prakash ◽  
S. Jyothi
Keyword(s):  
Alzheimer’S Disease ◽  
Image Processing ◽  
Alzheimer's Disease ◽  
Segmentation Technique

scholarly journals Deep Learning Image Processing Technique for Early Detection of Alzheimer's Disease

2017 ◽  
Vol 107 ◽  
pp. 85-104
Author(s):  
Raju Anitha ◽  
S. Jyothi ◽  
Venkata Naresh Mandhala ◽  
Debnath Bhattacharyya ◽  
Tai-hoon Kim
Keyword(s):  
Alzheimer’S Disease ◽  
Image Processing ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Early Detection ◽  
Processing Technique ◽  
Image Processing Technique

scholarly journals Image processing for early detection of Alzheimer’s disease using Iridology

2020 ◽  
Vol 11 (4) ◽  
pp. 5555-5559
Author(s):  
Asuntha A ◽  
Sai Kalyan Reddy R ◽  
Vamshikrishna K ◽  
Premsagar N
Keyword(s):  
Alzheimer’S Disease ◽  
Image Processing ◽  
Alzheimer's Disease ◽  
Memory Loss ◽  
Region Of Interest ◽  
Colour Pattern ◽  
Training Methods ◽  
Iris Image ◽  
Alternative Research ◽  
The Brain

Alzheimer's disease is caused by genetics, personal lifestyle and other environmental factors. It is an irreversible disease that slowly destroys the brain memory cells. There are no specific methods for the detection of Alzheimer's disease. The primary symptoms of Alzheimer's disease are memory loss, difficulty in thinking, a problem in writing and speaking and others. Iridology is alternative research that has gained more popularity in recent years, which studies the alterations of the iris in correspondence with the organs of the human body. The combination of digital image processing with Iridology gives an excellent opportunity to explore and learn about different neuronal diseases, specifically Alzheimer's disease. In this work, MATLAB software is applied to determine the colour, pattern and other factors that show the existence of Alzheimer's disease. The noise in the iris image is removed by the Gaussian filter, followed by histogram analyses and cropping. The Hough circle transform is used to identify the region of interest and to convert the circular iris image into rectangle form. In the training methods, the SVM and CNN classifiers are used to classify whether the person has Alzheimer's disease. Finally, the results are compared with the real-time images.

Intelligent System for Accurate Prediction and Detection of Alzheimer’s Disease

2019 ◽  
pp. 51-56
Author(s):  
Anusha Nambirajam K ◽  
Siva Subramanian T ◽  
Priyadharshini R
Keyword(s):  
Alzheimer’S Disease ◽  
Image Processing ◽  
Alzheimer's Disease ◽  
Digital Image Processing ◽  
Digital Image ◽  
Intelligent System ◽  
Essential Feature ◽  
Memory Loss ◽  
Video Frame ◽  
K Nearest Neighbor

Image processing is a process of converting an image into digital form and achieve some maneuvers on it, in order to get an enhanced image or to mine some useful information from it. It is a type of signal dispensation in which the input is an image, like video frame or photograph and output, may be image along with its characteristics and features associated with that image. An image is defined as a two-dimensional function F(x,y), where x and y are spatial coordinates, and the amplitude of F at any pair of coordinates (x,y) is called the intensity of that image at that point. When x, y, and amplitude values of F are finite, we call it a digital image. Image processing mainly consists of three basic steps. They are as follows: Initially, the image will be imported by using an optical scanner or by high-digital photography. Then the captured image will be subjected to the analyzation and manipulation process. These process also includes compression of data, enhancement of the image and spotting the patterns that are not visible to human eyes like satellite photography. Finally, the output will be obtained as an alternative image or any other essential feature extraction of the pre-processed image. Image Processing consists of two major types. They are  Analog Image Processing and Digital Image Processing. Digital Image Processing is a process in which a digital system is developed for processing a digital image and extracting feature form of results. Digital Image Processing works on the basis of an algorithm. An Intelligent System for Accurate Detection and Prediction of Alzheimer’s Disease mainly uses the k-nearest neighbor algorithm. Alzheimer’s  Disease is a type of disease in which the brain cells tend to die away and cause memory loss. In our proposed model we predict the accuracy of the amount of memory loss occurred in an affected brain. This system is mainly developed for helping the doctors and psychologists to obtain a maximum level of accuracy of the patient’s affected brain.

Benefits of Early Detection of Alzheimer’s Disease—A Machine Learning with Image Processing Approach

2020 ◽  
Vol 17 (1) ◽  
pp. 378-383
Author(s):  
Abhijit U. Kurtakoti ◽  
Namrata D. Hiremath ◽  
Nirmala S. Patil ◽  
Aishwarya Rane
Keyword(s):  
Alzheimer’S Disease ◽  
Image Processing ◽  
Alzheimer's Disease ◽  
Early Detection ◽  
Unsupervised Learning ◽  
Early Stage ◽  
Neurological Diseases ◽  
World Health ◽  
Primary Concern ◽  
Human Beings

Nervous system, being the most critical part of the human body has attracted many neuro-surgeons to diagnose the neurological diseases which are of primary concern. It’s been a challenge since many years. The recent report of the World Health Organization’s declares that neurological syndrome, such as, Alzheimer’s disease, affects around one billion human beings. As a consequence of neurological disorder there have been around 6.8 million deaths globally. Along with being an irremediable Disease it is at the same time a progressive brain disease which gradually diminishes the cognitive ability and affects memory which in turn affects routine life. It is prevalent cause of dementia among the elderly. This paper presents the work which assesses the efficacy of classification using unsupervised learning along with the image processing employed on the images of Magnetic Resonance Imaging scans to calculate the probability of early detection of Alzheimer’s disease. The whole brain atrophy is considered as strong diagnostic test for Alzheimer’s disease. The paper expresses the image processing methods such as pixel thresholding and unsupervised learning methods like k-means clustering, and a tailored algorithm incorporated for this specific case. The algorithm has been implemented using platforms, OpenCV and R libraries (for k means clustering), which expedites the effectiveness of the developed prototype which can be used in the hospitals/clinics, reducing the need for any proprietary software. The final output of the prototype can assist the doctors to diagnose Alzheimer’s disease at an early stage. These results can be co-related with psychiatric results for better understanding and treatment required for Alzheimer’s disease.

scholarly journals Classification of Initial Stages of Alzheimer’s Disease through Pet Neuroimaging Modality and Deep Learning: Quantifying the Impact of Image Filtering Approaches

Mathematics ◽  
2021 ◽  
Vol 9 (23) ◽  
pp. 3101
Author(s):  
Ahsan Bin Tufail ◽  
Yong-Kui Ma ◽  
Mohammed K. A. Kaabar ◽  
Ateeq Ur Rehman ◽  
Rahim Khan ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Image Processing ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Binary Classification ◽  
Image Filtering ◽  
Clinical Settings ◽  
Gaussian Filtering ◽  
3D Cnn ◽  
The Impact

Alzheimer’s disease (AD) is a leading health concern affecting the elderly population worldwide. It is defined by amyloid plaques, neurofibrillary tangles, and neuronal loss. Neuroimaging modalities such as positron emission tomography (PET) and magnetic resonance imaging are routinely used in clinical settings to monitor the alterations in the brain during the course of progression of AD. Deep learning techniques such as convolutional neural networks (CNNs) have found numerous applications in healthcare and other technologies. Together with neuroimaging modalities, they can be deployed in clinical settings to learn effective representations of data for different tasks such as classification, segmentation, detection, etc. Image filtering methods are instrumental in making images viable for image processing operations and have found numerous applications in image-processing-related tasks. In this work, we deployed 3D-CNNs to learn effective representations of PET modality data to quantify the impact of different image filtering approaches. We used box filtering, median filtering, Gaussian filtering, and modified Gaussian filtering approaches to preprocess the images and use them for classification using 3D-CNN architecture. Our findings suggest that these approaches are nearly equivalent and have no distinct advantage over one another. For the multiclass classification task between normal control (NC), mild cognitive impairment (MCI), and AD classes, the 3D-CNN architecture trained using Gaussian-filtered data performed the best. For binary classification between NC and MCI classes, the 3D-CNN architecture trained using median-filtered data performed the best, while, for binary classification between AD and MCI classes, the 3D-CNN architecture trained using modified Gaussian-filtered data performed the best. Finally, for binary classification between AD and NC classes, the 3D-CNN architecture trained using box-filtered data performed the best.

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