scholarly journals Comparison Analysis of GLCM and PCA on Parkinson's Disease Using Structural MRI

2022 ◽  
Vol 12 (1) ◽  
pp. 1-15
Author(s):  
Sanjana Tomer ◽  
Ketna Khanna ◽  
Sapna Gambhir ◽  
Mohit Gambhir
Keyword(s):  
Feature Extraction ◽  
Principal Component ◽  
Structural Mri ◽  
Dopamine Neurons ◽  
Motor Symptoms ◽  
Non Invasive ◽  
Invasive Method ◽  
Occurrence Matrix ◽  
Time Motor ◽  
Non Motor Symptoms

Parkinson disease (PD) is a neurological disorder where the dopaminergic neurons experience deterioration. It is caused from the death of the dopamine neurons present in the substantia nigra i.e., the mid part of the brain. The symptoms of this disease emerge slowly, the onset of the earlier stages shows some non-motor symptoms and with time motor symptoms can also be gauged. Parkinson is incurable but can be treated to improve the condition of the sufferer. No definite method for diagnosing PD has been concluded yet. However, researchers have suggested their own framework out of which MRI gave better results and is also a non-invasive method. In this study, the MRI images are used for extracting the features. For performing the feature extraction techniques Gray Level Co-occurrence Matrix and Principal Component Analysis are performed and are analysed. Feature extraction reduces the dimensionality of data. It aims to reduce the feature of data by generating new features from the original one.

scholarly journals An Efficient Forgery Image Detection Method using Hybrid Feature Extraction and Multiclass SVM

2019 ◽  
Vol 8 (2S2) ◽  
pp. 278-284
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Wavelet Transform ◽  
Principal Component ◽  
Extraction Technique ◽  
Gray Level ◽  
Image Forgery ◽  
Hybrid Feature Extraction ◽  
Occurrence Matrix ◽  
Feature Extraction Technique

The advancement of image editing software tools in the image processing field has led to an exponential increase in the manipulation of the images. Subjective differentiation of original and manipulated images has become almost impossible. This has kindled the interest among researchers to develop algorithms for detecting the forgery in the image. ImageSplicing, Copy-Move and Image Retouching are the most common image forgery techniques. The existing methods to detect image forgery has drawbacks like false detection, high execution time and low accuracy rate. Considering these issues, this work proposes an efficient method for detection of image forgery. Initially, bilateral filter is used to remove the noise in pre-processing, Chan-Vese Segmentation algorithm is used to detect the clumps from the filtered image utilizing both intensity and edgeinformation, followed by hybrid feature extraction technique. Hybrid feature extraction technique comprises of Dual Tree Complex-Discrete Wavelet Transform (DWT), Principal Component Analysis (PCA) and Gray-Level-Co-Occurrence Matrix (GLCM). The DWT has dual-tree complex wavelet transform with important properties, it is nearly shift invariant and directionally selective in two and higher dimensions. Principal Component Analysis (PCA) finds the eigenvectors of a covariance matrix with the highest eigenvalues and uses these values to project the data into a new subspace of equal or less dimensions. Gray-Level-Co-Occurrence Matrix (GLCM) extracts the Feature values such as energy, entropy, homogeneity, standard deviation, variance, contrast, correlation and mean. Classification is done based on the texture values of training dataset and testing dataset using Multi Class-Support Vector Machine (SVM). The performance analysis is done based on the True positive, False positive and True negative values. The experimental results obtained using the proposed technique shows a better performance compared to the existing KNN classifier model.

scholarly journals Raman spectroscopy as a non-invasive diagnostic technique for endometriosis

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Ugur Parlatan ◽  
Medine Tuna Inanc ◽  
Bahar Yuksel Ozgor ◽  
Engin Oral ◽  
Ercan Bastu ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Diagnostic Technique ◽  
Principal Component ◽  
Classification Model ◽  
Support Vector ◽  
Serum Samples ◽  
K Nearest Neighbors ◽  
Non Invasive ◽  
Unseen Data ◽  
Invasive Method

AbstractEndometriosis is a condition in which the endometrium, the layer of tissue that usually covers the inside of the uterus, grows outside the uterus. One of its severe effects is sub-fertility. The exact reason for endometriosis is still unknown and under investigation. Tracking the symptoms is not sufficient for diagnosing the disease. A successful diagnosis can only be made using laparoscopy. During the disease, the amount of some molecules (i.e., proteins, antigens) changes in the blood. Raman spectroscopy provides information about biochemicals without using dyes or external labels. In this study, Raman spectroscopy is used as a non-invasive diagnostic method for endometriosis. The Raman spectra of 94 serum samples acquired from 49 patients and 45 healthy individuals were compared for this study. Principal Component Analysis (PCA), k- Nearest Neighbors (kNN), and Support Vector Machines (SVM) were used in the analysis. According to the results (using 80 measurements for training and 14 measurements for the test set), it was found that kNN-weighted gave the best classification model with sensitivity and specificity values of 80.5% and 89.7%, respectively. Testing the model with unseen data yielded a sensitivity value of 100% and a specificity value of 100%. To the best of our knowledge, this is the first study in which Raman spectroscopy was used in combination with PCA and classification algorithms as a non-invasive method applied on blood sera for the diagnosis of endometriosis.

Approaches to the Diagnosis of Parkinson’s Disease during the Pre-motor Phase

US Neurology ◽  
2011 ◽  
Vol 07 (01) ◽  
pp. 15
Author(s):  
Nuri Jacoby ◽  
Jacqueline B Stone ◽  
Claire Henchcliffe ◽  
◽  
◽  
...  
Keyword(s):  
At Risk ◽  
Motor Symptoms ◽  
Disease Modifying Drugs ◽  
Autonomic Changes ◽  
Genomic Analyses ◽  
Time Motor ◽  
Development And Validation ◽  
Non Motor Symptoms ◽  
Underlying Pathology ◽  
Neuroimaging Techniques

Parkinson’s disease (PD) remains a clinical diagnosis based primarily upon motor features of tremor, rigidity, and bradykinesia. However, by the time motor symptoms occur, the underlying pathology may be widespread. Approaches using a combination of clinical batteries and sophisticated biomarker technology now hold promise for identifying individuals earlier in the course of the disease, including those at risk of PD who may not yet have manifested any motor symptoms. Non-motor symptoms, such as specific sleep disorders, olfactory dysfunction, and autonomic changes, occur during a pre-motor phase, and their use in PD risk stratification is being actively evaluated. Neuroimaging techniques, in particular those focused upon measuring dopamine transporter density, are now in use in many specialist centers, molecular markers are in development and validation phases, and the use of genomic analyses has expanded the number of loci identified as contributing to PD risk. Identifying those at risk of developing PD will aid in the clinical management of the disease, and perhaps enable the use of disease-modifying drugs at as early a stage as possible.

scholarly journals A Review on Feature Extraction Techniques of Photoplethysmogram Signal

2020 ◽  
pp. 380-383
Author(s):  
Sree Sanjanaa Bose S ◽  
Sree Niranjanaa Bose S ◽  
Maniventhan M
Keyword(s):  
Feature Extraction ◽  
Blood Volume ◽  
Pulse Rate ◽  
Peripheral Blood ◽  
Extraction Techniques ◽  
Non Invasive ◽  
Invasive Method ◽  
Ectopic Beats ◽  
Vital Parameters

Photoplethysmography (PPG) signal has been used widely for detection of pulse rate from peripheral blood volume. PPG is a novel non-invasive method with the advantage of convenience and accuracy. The dichrotic notches, ectopic beats and apertures are the prominent features that can be extracted from PPG signal for computing different physiological vital parameters. In this paper, we have discussed various feature extraction techniques.

Malfunctional Reorganization in the Developing Limbo-Prefrontal System in Animals: Implications for Human Psychoses?

2001 ◽  
Vol 12 (1) ◽  
pp. 8-14
Author(s):  
Gertraud Teuchert-Noodt ◽  
Ralf R. Dawirs
Keyword(s):  
Prefrontal Cortex ◽  
Mental Disorders ◽  
Dentate Gyrus ◽  
Cognitive Functions ◽  
Experimental Approach ◽  
Regulatory Mechanisms ◽  
Hippocampal Dentate Gyrus ◽  
Non Invasive ◽  
Invasive Method ◽  
Activity Dependent

Abstract: Neuroplasticity research in connection with mental disorders has recently bridged the gap between basic neurobiology and applied neuropsychology. A non-invasive method in the gerbil (Meriones unguiculus) - the restricted versus enriched breading and the systemically applied single methamphetamine dose - offers an experimental approach to investigate psychoses. Acts of intervening affirm an activity dependent malfunctional reorganization in the prefrontal cortex and in the hippocampal dentate gyrus and reveal the dopamine position as being critical for the disruption of interactions between the areas concerned. From the extent of plasticity effects the probability and risk of psycho-cognitive development may be derived. Advance may be expected from insights into regulatory mechanisms of neurogenesis in the hippocampal dentate gyrus which is obviously to meet the necessary requirements to promote psycho-cognitive functions/malfunctions via the limbo-prefrontal circuit.

Gray-Level Co-occurrence Matrix and Random Forest Based Off-line Odia Handwritten Character Recognition

2019 ◽  
Vol 13 (2) ◽  
pp. 136-141 ◽  
Author(s):  
Abhisek Sethy ◽  
Prashanta Kumar Patra ◽  
Deepak Ranjan Nayak
Keyword(s):  
Feature Extraction ◽  
Random Forest ◽  
Character Recognition ◽  
Recognition Rate ◽  
Discrete Wavelet ◽  
Gray Level ◽  
Handwritten Character Recognition ◽  
Handwritten Character ◽  
Wide Range ◽  
Occurrence Matrix

Background: In the past decades, handwritten character recognition has received considerable attention from researchers across the globe because of its wide range of applications in daily life. From the literature, it has been observed that there is limited study on various handwritten Indian scripts and Odia is one of them. We revised some of the patents relating to handwritten character recognition. Methods: This paper deals with the development of an automatic recognition system for offline handwritten Odia character recognition. In this case, prior to feature extraction from images, preprocessing has been done on the character images. For feature extraction, first the gray level co-occurrence matrix (GLCM) is computed from all the sub-bands of two-dimensional discrete wavelet transform (2D DWT) and thereafter, feature descriptors such as energy, entropy, correlation, homogeneity, and contrast are calculated from GLCMs which are termed as the primary feature vector. In order to further reduce the feature space and generate more relevant features, principal component analysis (PCA) has been employed. Because of the several salient features of random forest (RF) and K- nearest neighbor (K-NN), they have become a significant choice in pattern classification tasks and therefore, both RF and K-NN are separately applied in this study for segregation of character images. Results: All the experiments were performed on a system having specification as windows 8, 64-bit operating system, and Intel (R) i7 – 4770 CPU @ 3.40 GHz. Simulations were conducted through Matlab2014a on a standard database named as NIT Rourkela Odia Database. Conclusion: The proposed system has been validated on a standard database. The simulation results based on 10-fold cross-validation scenario demonstrate that the proposed system earns better accuracy than the existing methods while requiring least number of features. The recognition rate using RF and K-NN classifier is found to be 94.6% and 96.4% respectively.

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