Feature extraction using morphological analysis of multiresolution gray-scale images

1992 ◽  
Author(s):  
Louis A. Tamburino ◽  
Mateen M. Rizki ◽  
Michael A. Zmuda
Keyword(s):  
Feature Extraction ◽  
Morphological Analysis ◽  
Gray Scale

A novel murmur-based heart sound feature extraction technique using envelope-morphological analysis

2015 ◽  
Author(s):  
Hao-Dong Yao ◽  
Jia-Li Ma ◽  
Bin-Bin Fu ◽  
Hai-Yang Wang ◽  
Ming-Chui Dong
Keyword(s):  
Feature Extraction ◽  
Morphological Analysis ◽  
Heart Sound ◽  
Extraction Technique ◽  
Feature Extraction Technique

scholarly journals Degradation state identification for hydraulic pumps using modified hierarchical decomposition and image processing

2021 ◽  
pp. 002029402110648
Author(s):  
Mo-chao Pei ◽  
Hong-ru Li ◽  
He Yu
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Support Vector ◽  
Hierarchical Decomposition ◽  
Feature Points ◽  
Gray Scale ◽  
Nsga Ii ◽  
Vibration Signals ◽  
State Identification ◽  
Degradation State

Monitoring the degradation state of hydraulic pumps is of great significance to the safe and stable operation of equipment. As an important step, feature extraction has always been challenging. The non-stationary and nonlinear characteristics of vibration signals are likely to weaken the performance of traditional features. The two-dimensional image representation of vibration signals can provide more information for feature extraction, but it is challenging to obtain sufficient information based on small-size images. To solve these problems, a method for feature extraction based on modified hierarchical decomposition (MHD) and image processing is proposed in this paper. First, a set of signals decomposed by MHD are converted into gray-scale images. Second, features from accelerated segment test (FAST) algorithm are applied to detecting the feature points of the gray-scale image. Third, the real part of Gabor filter bank is used to convolve the images, and the responses of feature points are used to calculate histograms that are regarded as feature vectors. The method for feature extraction fully acquires the multi-layered texture information of small-size images and removes the redundant information. Furthermore, support vector machine (SVM) and nondominated sorting genetic algorithm II (NSGA-II) are introduced to conduct feature selection and state identification. NSGA-II and SVM can conduct the joint optimization of these two goals. The details of the proposed method are validated using experimental data, and the results show that the highest recognition rate of our proposed method can reach 100%. The results of the comparison among the proposed method, local binary pattern (LBP), and one-dimensional ternary patterns (1D-TPs) certify the superiorities of the proposed method. It obtains the highest classification accuracy (99.7%–98%) and the lowest feature set dimension (13–10).

scholarly journals Indian Counterfeit Currency Detection

2020 ◽  
Vol 9 (4) ◽  
pp. 1746-1749
Keyword(s):  
Feature Extraction ◽  
Image Segmentation ◽  
Edge Detection ◽  
Gray Scale ◽  
Printing Technology ◽  
Efficient System ◽  
Image Preprocessing ◽  
Currency Note ◽  
Scanned Image ◽  
Color Printing

The counterfeit currency printing rate has been increased with the progress of color printing Technology. Some people are printing fake currency using some laser printers. Therefore, the counterfeit currency notes production instead of the original currency notes has been rapidly increasing. This requires an efficient system that identifies the counterfeit currency note and displays the result. This paper developed a system consisting of image preprocessing, gray-scale conversion, image segmentation, edge detection, feature extraction, and comparison modules. The currency note is scanned and the scanned image is used in the modules. The outcome of the system will foretell if the note is counterfeit or genuine

scholarly journals HaraliCU: GPU-Powered Haralick Feature Extraction on Medical Images Exploiting the Full Dynamics of Gray-Scale Levels

2019 ◽  
pp. 304-318
Author(s):  
Leonardo Rundo ◽  
Andrea Tangherloni ◽  
Simone Galimberti ◽  
Paolo Cazzaniga ◽  
Ramona Woitek ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Medical Images ◽  
Gray Scale ◽  
Scale Levels

Classification of heart disease using mfo based neural network on mri images

2021 ◽  
Vol 17 ◽  
Author(s):  
Kalaivani. K ◽  
Uma Maheswari. N ◽  
Venkatesh. R
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Feature Extraction ◽  
Heart Disease ◽  
Optimization Process ◽  
Generalized Regression Neural Network ◽  
Gray Scale ◽  
Generalized Regression ◽  
Scale Properties ◽  
Optimal Feature

Background: Cardiovascular disease (CVD) is one of the primary diseases that cause death every year. An approximation of roughly about 17.5 million people dies due to CVD, signifying about 31% of global deaths. Based on the statistics, for every 34 seconds the people were died due to heart disease. Various classification algorithms have been developed and utilized as classifiers to support doctors who are ineffectually diagnosed with heart disease. Aims: The main aim of this work is to improve the performance of heart disease approach using image processing algorithm. To improve the effectiveness and efficiency of classification performance for heart disease diagnosis, an optimized neural network was proposed based on the feature extraction and selection approach for handling features. Objective: The objective of this investigation is to diagnosis heart disease using feature extraction and reduction based classification using image processing methods. The proposed model comprises of two subsets: Feature extraction using gray scale properties and Moth flame optimization (MFO) for effectual feature selection, followed by a classification technique using Generalized Regression Neural Network. The first system in co-operates three stages: (i) Pre-processing of the dataset (ii) feature extraction (iii) performing MFO for efficient selection. In second method, GRNN is proposed. The heart data set obtained from ACDC Challenge, was utilized for performing the computation. Method: The image obtained from the MRI-scanner is in the NIfTI image format. The pre-process step used in this is to convert the image type from INT16 to uint8 to improve the quality of image viewing and for feature extraction process. In this phase, the texture properties from the pre-processed image is calculated and the value is in the numeric format. These values are the feature attributes of the dataset. The feature attributes of the image is given as input for the moth flame optimization process and output is the feature selected from the optimization process. The whole process is performed on the feature attributes of the image and determining the optimal feature for the classifier by reducing its error rate. The optimal feature from the moth flame optimization is used for training and testing the network. The classifier used in this approach is a single neural network classifier with regression nature. Due to the regression property the network is well trained with the feature. The Generalized regression neural network is used for classifying the heart disease. Results: The proposed method achieves the accuracy of 96.23%, sensitivity 95.41% and specificity of 96.75%. These values are calculated based on the confusion matrix of the classifier. Conclusion: In this, the feature extraction using the gray scale properties plays an important role to determine the feature attributes from the MRI heart images. The Moth flame optimization able to produce an accuracy of 97.23% using GRNN for classification with minimum single attribute mean of the image. It also outperformed the other methods either the feature extraction based classification or the feature reduction based classification.

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