scholarly journals A Novel Statistical Based Methodology for the Feature Extraction of both MRI and CT images

2019 ◽  
Vol 8 (6S3) ◽  
pp. 1788-1794
Keyword(s):  
Feature Extraction ◽  
Statistical Method ◽  
Principle Component Analysis ◽  
Matrix Method ◽  
Ct Images ◽  
Gray Level ◽  
Common Features ◽  
Principle Component ◽  
The Common ◽  
Occurrence Matrix

Design of a common methodology for the diagnosis of different image types is the objective of the work presented in this paper. The software is developed and can be used to diagnose MRI and CT images by the laboratory technician The paper presents a statistical method for the feature extraction of MRI and CT images. About thirteen features are extracted using the methodology adopted for the proposed work. The thirteen features are based on texture, shape and intensity. The data dimensionality is reduced using the Principle Component Analysis (PCA). The common features are extracted using the Gray level co-occurrence matrix method. The software is developed using MATLAB and PYTHON for IoT support.

scholarly journals Klasifikasi Citra X-Ray Diagnosis Tuberkulosis Berbasis Fitur Statistis

2018 ◽  
Vol 2 (3) ◽  
Author(s):  
Yudhi Agussationo ◽  
Indah Soesanti ◽  
Warsun Najib
Keyword(s):  
Principle Component Analysis ◽  
Component Analysis ◽  
Gray Level ◽  
Multi Layer Perceptron ◽  
Data Input ◽  
X Ray ◽  
Principle Component ◽  
Occurrence Matrix

Tuberkulosis merupakan salah satu penyebab kematian manusia. Hasil pemeriksaan x-ray diagnosis tuberkulosis dapat dijadikan objek pada proses ekstraksi ciri yang merupakan suatu tahapan dalam mengekstrak ciri/ informasi dari objek yang terdapat pada suatu citra diagnosis tuberkulosis. Pada penelitian ini digunakan metode ekstraksi ciri citra berbasis tekstur statistis orde satu (histogram), orde kedua berbasis Gray-Level Co-occurrence Matrix (GLCM), serta Principle Component Analysis (PCA). Data penelitian diperoleh dari RS Dr. Sardjito Yogyakarta sebanyak 33 citra digital x-ray pasien diagnosis tuberkulosis Tahun 2012 masing-masing 6 citra PA (Postero-Anterior) normal, 19 citra abnormal, 4 citra AP (Antero-Posterior) normal, dan 4 citra AP abnormal. Penelitian ini bertujuan mencari ciri terbaik yang terkandung pada citra x-ray diagnosis tuberkulosis menggunakan analisis tekstur statistis yang diperoleh dari fitur ciri yang terdapat pada metode ekstraksi ciri berbasis tekstur. Fitur ciri yang teridentifikasi antara lain: varians, std deviasi, skewness, kurtosis, contrast dan energy. Klasifikasi menggunakan data input berupa 33 data uji yang dibangun dengan metode Multi Layer Perceptron (MLP), sedang output berupa citra normal dan citra abnormal. Hasil penelitian menunjukkan bahwa akurasi klasifikasi menggunakan metode Histogram (81,81%), metode GLCM (96,96%), metode PCA (81,82%), dan metode kombinasi Histogram GLCM (100%).

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.

ANALYSIS OF VOICE CUES IN RECOGNITION OF SARCASM

2019 ◽  
Vol 12 ◽  
Author(s):  
Basavaraj N Hiremath ◽  
Malini M Patilb
Keyword(s):  
Feature Extraction ◽  
Computer Program ◽  
Principle Component Analysis ◽  
Voice Recognition ◽  
Component Analysis ◽  
Recognition System ◽  
Principle Component ◽  
Whole Process ◽  
The Voice

The voice recognition system is about cognizing the signals, by feature extraction and identification of related parameters. The whole process is referred to as voice analytics. The paper aims at analysing and synthesizing the phonetics of voice using a computer program called “PRAAT”. The work carried out in the paper also supports the analysis of voice segmentation labelling, analyse the unique features of voice cues, understanding physics of voice, further the process is carried out to recognize sarcasm. Different unique features identified in the work are, intensity, pitch, formants related to read, speak, interactive and declarative sentences by using principle component analysis.

scholarly journals PENGENALAN TAS RANSEL PADA CITRA DIGITAL DENGAN EKSTRAKSI FITUR TEKSTUR MENGGUNAKAN METODE GRAY LEVEL CO-OCCURRENCE MATRIX

2018 ◽  
Vol 4 (4) ◽  
pp. 258
Author(s):  
Cahya Rahmad ◽  
Mungki Astiningrum ◽  
Ade Putra Lesmana
Keyword(s):  
Feature Extraction ◽  
Texture Feature ◽  
Gray Level ◽  
Significant Development ◽  
Average Percentage ◽  
The Road ◽  
The Third ◽  
Last Stage ◽  
On The Road ◽  
Occurrence Matrix

The Backpack is one type of bag that experienced significant development. Many people buy it for their needs. However, when assessing a backpack directly or on the road, he could not recognize the backpack. The generally people want to buy backpacks must look at the price, color, shape, features, and the main ingredients of manufacture. Therefore, in image processing, there is a feature extraction theory for the process of recognizing an object. The backpack itself has a different texture. So that the introduction of the object is better done texture feature extraction with the gray level Co-occurrence matrix method. After that, then get the uniqueness of the backpack image to the classification with the image of the backpack in the database. The last stage in this study the authors conducted trials in 3 conditions. The first condition is based on a backpack photo-taking background. The second condition is based on the pixel capacity of the camera to retrieve the backpack image. And the third condition is based on the brightness of the backpack image. Of these three conditions, a percentage of matching values was obtained in the first condition with an average percentage of 90%, the second condition with an average percentage of 80% and last on the third condition with an average percentage of 70%.

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 Using Color and Texture Feature Extraction Technique to Retrieve Image

2019 ◽  
Vol 8 (11S) ◽  
pp. 915-917
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Texture Feature ◽  
Color Images ◽  
Color Histogram ◽  
Gray Level ◽  
Texture Feature Extraction ◽  
Color Feature ◽  
Occurrence Matrix ◽  
Feature Extraction Technique

This paper proposes a content image retrieval using the texture and the color feature of the images. Although for extraction of texture feature, the “gray level co-occurrence matrix (GLCM) algorithm” is used and for extracting color feature the color histogram is used. The presented system is tested on the WANG database that contains a thousand color images with ten different classes by the help of three various type of distances

scholarly journals Image Texture Feature Extraction Method Based on Regional Average Binary Gray Level Difference Co-occurrence Matrix

2011 ◽  
Vol 10 (3) ◽  
pp. 73-79 ◽  
Author(s):  
Jian Yang ◽  
Jingfeng Guo
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Texture Feature ◽  
Local Area ◽  
Image Texture ◽  
Gray Level ◽  
Texture Feature Extraction ◽  
Feature Extraction Method ◽  
Level Difference ◽  
Occurrence Matrix

Texture feature is a measure method about relationship among the pixels in local area, reflecting the changes of image space gray levels. This paper presents a texture feature extraction method based on regional average binary gray level difference co-occurrence matrix, which combined the texture structural analysis method with statistical method. Firstly, we calculate the average binary gray level difference of eight-neighbors of a pixel to get the average binary gray level difference image which expresses the variation pattern of the regional gray levels. Secondly, the regional co-occurrence matrix is constructed by using these average binary gray level differences. Finally, we extract the second-order statistic parameters reflecting the image texture feature from the regional co-occurrence matrix. Theoretical analysis and experimental results show that the image texture feature extraction method has certain accuracy and validity

scholarly journals Intelligent Offer Model for Game Online Item with Principle Hybrid Filtering Together with Categorical Data Technique

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Feature Extraction ◽  
User Interface ◽  
Principle Component Analysis ◽  
Categorical Data ◽  
Raw Material ◽  
Collaborative Group ◽  
Online Game ◽  
Principle Component ◽  
Hybrid Filtering ◽  
Group Pattern

This paper focus to Intelligent offer pattern, The commonly used raw material, wearing: costumes, earrings, gloves, gloves, boots, shoes, costumes, hair, necklace, two hands, costume masks, hats, costumes, weapons, clothes, watches, rings, plaques and feature character. Robot and various tools are built to response of players. One of the expectations of most players is items selection which is selected from the User Interface. It is obtrusive noise of view screen while players select a few of the items. Object Items of games offer to each player overlap on the screen. It helps reduce event to switch the screen to maximize revenue from the purchase, sale, and exchange object items in the online game. we use Double Two dimension Principle Component Analysis for the feature extraction data behavior which is behavior a repeated in each player such as character of clothes, characteristic of choose activity , characteristic of use game items. We built Collaborative group pattern (CGP) and analysis player separate to each corporative group. Adding create offer game items in each corporative group. Each player will receive the best offers. The game operator has the opportunity to sell game items online as possible by without interrupting game play. The results of experiment showed that this method can offer a game item which is player want to sequence in the top of the list, and average 11.41 percentages is garbage items which player does not want, by game items which each player need to be in the order of 1-10 of menu

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