Average Analysis Method in Selecting Haralick’s Texture Features on Color Co-occurrence Matrix for Texture Based Image Retrieval

2016 ◽  
Vol 11 (2) ◽  
pp. 79-88
Author(s):  
Abd Rasid Mamat ◽  
Mohd Khalid Awang ◽  
Norkhairani Abdul Rawi ◽  
Mohd Isa Awang ◽  
Mohd Fadzil Abdul Kadir
Keyword(s):  
Image Retrieval ◽  
Texture Features ◽  
Analysis Method ◽  
Average Analysis ◽  
Occurrence Matrix

An Image Retrieval Algorithm Base on Texture Features

2014 ◽  
Vol 668-669 ◽  
pp. 1041-1044
Author(s):  
Lin Lin Song ◽  
Qing Hu Wang ◽  
Zhi Li Pei
Keyword(s):  
Image Retrieval ◽  
Statistical Methods ◽  
Texture Features ◽  
Retrieval Algorithm ◽  
Occurrence Matrix ◽  
Algorithm Base

This paper firstly studies the texture features. We construct a gray-difference primitive co-occurrence matrix to extract texture features by combining statistical methods with structural ones. The experiment results show that the features of the gray-difference primitive co-occurrence matrix are more delicate than the traditional gray co-occurrence matrix.

Relationships of Image Texture Properties with Chewing Activity and Mechanical Properties during Mastication of Bread

2016 ◽  
Vol 12 (4) ◽  
pp. 311-321
Author(s):  
Qian Mao ◽  
Yonghai Sun ◽  
Jumin Hou ◽  
Libo Yu ◽  
Yang Liu ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Texture Analysis ◽  
Texture Features ◽  
Image Texture ◽  
Emg Activity ◽  
Analysis Method ◽  
Texture Properties ◽  
Property Changes ◽  
Occurrence Matrix ◽  
Chewing Activity

Abstract The objective of this study was to investigate the relationships of image texture properties with chewing behaviors, and mechanical properties during mastication of bread. Gray-level gradient co-occurrence matrix (GGCM) was used to process the images of boluses. The chewing behaviors were recorded by electromyography (EMG), and the mechanical properties were measured by texture analyzer. The results showed that among the texture features, the inverse difference moment (IDMGGCM) was selected as the main parameter to describe the decomposition of boluses. IDMGGCM was positively related to the weight gain (r = 0.865, p < 0.01), negatively correlated with hardness (r = –0.835, p <0.01) and EMG activity per cycle (r = –0.767, p < 0.01). GGCM is an effective texture analysis method that could correctly identify 70.1–80.8 % of food bolus images to the corresponding chewing cycles. This study provided a new clue for texture analysis of bread bolus images and offered data revealing the bolus property changes during the mastication of bread.

scholarly journals AN OPTIMIZED FEATURE EXTRACTION TECHNIQUE FOR CONTENT BASED IMAGE RETRIEVAL

2013 ◽  
pp. 11-16
Author(s):  
SAVITHA SIVAN ◽  
THUSNAVIS BELLA MARY. I
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Feature Fusion ◽  
Texture Features ◽  
Research Area ◽  
Superior Performance ◽  
Content Based Image Retrieval ◽  
Multimedia Technologies ◽  
Active Research ◽  
Occurrence Matrix

Content-based image retrieval (CBIR) is an active research area with the development of multimedia technologies and has become a source of exact and fast retrieval. The aim of CBIR is to search and retrieve images from a large database and find out the best match for the given query. Accuracy and efficiency for high dimensional datasets with enormous number of samples is a challenging arena. In this paper, Content Based Image Retrieval using various features such as color, shape, texture is made and a comparison is made among them. The performance of the retrieval system is evaluated depending upon the features extracted from an image. The performance was evaluated using precision and recall rates. Haralick texture features were analyzed at 0 o, 45 o, 90 o, 180 o using gray level co-occurrence matrix. Color feature extraction was done using color moments. Structured features and multiple feature fusion are two main technologies to ensure the retrieval accuracy in the system. GIST is considered as one of the main structured features. It was experimentally observed that combination of these techniques yielded superior performance than individual features. The results for the most efficient combination of techniques have also been presented and optimized for each class of query.

Multi-Featured Content-Based Image Retrieval Using Color and Texture Features

2016 ◽  
Vol 850 ◽  
pp. 136-143 ◽  
Author(s):  
Mehmet Ayan ◽  
O. Ayhan Erdem ◽  
Hasan Şakir Bilge
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Texture Feature ◽  
Texture Features ◽  
Color Histogram ◽  
Superior Performance ◽  
Content Based Image Retrieval ◽  
Occurrence Matrix ◽  
Color Moment ◽  
Color And Texture Features

Content-based image retrieval (CBIR) system becomes a hot topic in recent years. CBIR system is the retrieval of images based on visual features. CBIR system based on a single feature has a low performance. Therefore, in this paper a new content based image retrieval method using color and texture features is proposed to improve performance. In this method color histogram and color moment are used for color feature extraction and grey level co-occurrence matrix (GLCM) is used for texture feature extraction. Then all extracted features are integrated for image retrieval. Finally, color histogram, color moment, GLCM and proposed methods are tested respectively. As a result, it is observed that proposed method which integrates color and texture features gave better results than the other methods used independently. To demonstrate the proposed system is successful, it was compared with existing CBIR systems. The proposed method showed superior performance than other comparative systems.

scholarly journals Texton Based Image Retrieval Using Indexed LBP Transitions

2014 ◽  
Vol 13 (12) ◽  
pp. 5286-5300 ◽  
Author(s):  
A. Srinivasa Rao ◽  
V.Venkata Krishna ◽  
Prof.YKSundara Krishna
Keyword(s):  
Image Retrieval ◽  
Data Base ◽  
Local Binary Pattern ◽  
Texture Features ◽  
Experimental Result ◽  
Texture Image ◽  
Huge Data ◽  
Proposed Model ◽  
Occurrence Matrix ◽  
Rotationally Invariant

The present paper derived a new model of texture image retrieval by integrating the transitions on Local Binary Pattern (LBP) with textons and Grey Level Co-occurrence Matrix (GLCM). The present paper initially derived transitions that occur from 0 to 1 or 1 to 0 in circular manner on LBP. The transitions reduce the 256 LBP codes into five texture features. This reduces the lot of complexity. The LBP codes are rotationally variant. The proposed circular transitions on LBP are rotationally invariant. Textons,which represents the local relationships,are detected on this. The GLCM features are evaluated on the texton based image for efficient image retrieval. The proposed method is experimented on a huge data base of textures collected from Google data base. The experimental result indicates the efficiency of the proposed model.

A Novel Texture Analysis Method Based on Reverse Biorthogonal Wavelet and Co-Occurrence Matrix Applied for Classification of Hepatocellular Carcinoma and Hepatic Hemangioma

2018 ◽  
Vol 8 (9) ◽  
pp. 1835-1843 ◽  
Author(s):  
Jia-Jun Qiu ◽  
Yue Wu ◽  
Bei Hui ◽  
Jia Chen ◽  
Lin Ji ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Texture Analysis ◽  
Texture Features ◽  
Ct Images ◽  
Hepatic Hemangioma ◽  
Gray Level ◽  
Biorthogonal Wavelet ◽  
Analysis Method ◽  
Enhanced Ct ◽  
Occurrence Matrix

Purpose: To explore the feasibility of classifying hepatocellular carcinoma (HCC) and hepatic hemangioma (HEM) using texture features of non-enhanced computed tomography (CT) images, especially to investigate the effectiveness of a novel texture analysis method based on the combination of wavelet and co-occurrence matrix. Methods: 269 patients were retrospectively analyzed, including 129 HCCs and 140 HEMs. We cropped tumor regions of interest (ROIs) on non-enhanced CT images, and then used four texture analysis methods to extract quantitative data of the ROIs: gray-level histogram (GLH), gray-level co-occurrence matrix (GLCM), reverse biorthogonal wavelet transform (RBWT), and reverse biorthogonal wavelet co-occurrence matrix (RBCM). The RBCM was a novel method proposed in this study that combined wavelet transform and co-occurrence matrix. It discretized wavelet coefficient matrices based on the statistical characteristics of the training set. Thus, four sets of texture features were obtained. We then conducted classification studies using support vector machine on each set of texture features. 10-fold cross training and testing were used in the classifications, and their results were evaluated and compared. In addition, we tested the significant differences in the texture features of the RBCM method and explored the possible relationships between textures and lesion types. Results: The RBCM method achieved the best classification performance: its average accuracy was 82.14%; its average AUC (area under the receiver operating characteristic curve) was 0.8423. In addition, using the methods of GLH, GLCM, and RBWT, their average accuracies were 75.81%, 78.79%, and 78.8%, respectively. Conclusions: It indicates that the developed texture analysis methods are rewarding for computer-aided diagnosis of HCC and HEM based on non-enhanced CT images. Furthermore, the distinguishing ability of the proposed RBCM method is more pronounced.

Review on Texture Feature Extraction and Description Methods in Content-Based Medical Image Retrieval

2011 ◽  
pp. 44-67 ◽  
Author(s):  
Gang Zhang ◽  
Z. M. Ma ◽  
Li Yan
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Medical Image ◽  
Texture Feature ◽  
Texture Features ◽  
Gabor Wavelet ◽  
Texture Feature Extraction ◽  
Medical Image Retrieval ◽  
Image Retrieval System ◽  
Occurrence Matrix

Texture feature extraction and description is one of the important research contents in content-based medical image retrieval. The chapter first proposes a framework of content-based medical image retrieval system. It then analyzes the important texture feature extraction and description methods further, such as the co-occurrence matrix, perceptual texture features, Gabor wavelet, and so forth. Moreover, the chapter analyzes the improved methods for these methods and demonstrates their application in content-based medical image retrieval.

scholarly journals Multi-scale characterizations of colon polyps via computed tomographic colonography

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Weiguo Cao ◽  
Marc J. Pomeroy ◽  
Yongfeng Gao ◽  
Matthew A. Barish ◽  
Almas F. Abbasi ◽  
...  
Keyword(s):  
Area Under The Curve ◽  
Texture Features ◽  
Learning Model ◽  
Colon Polyps ◽  
Texture Descriptor ◽  
Operating Characteristics ◽  
Computed Tomographic ◽  
Multi Scale ◽  
Definition Of ◽  
Occurrence Matrix

AbstractTexture features have played an essential role in the field of medical imaging for computer-aided diagnosis. The gray-level co-occurrence matrix (GLCM)-based texture descriptor has emerged to become one of the most successful feature sets for these applications. This study aims to increase the potential of these features by introducing multi-scale analysis into the construction of GLCM texture descriptor. In this study, we first introduce a new parameter - stride, to explore the definition of GLCM. Then we propose three multi-scaling GLCM models according to its three parameters, (1) learning model by multiple displacements, (2) learning model by multiple strides (LMS), and (3) learning model by multiple angles. These models increase the texture information by introducing more texture patterns and mitigate direction sparsity and dense sampling problems presented in the traditional Haralick model. To further analyze the three parameters, we test the three models by performing classification on a dataset of 63 large polyp masses obtained from computed tomography colonoscopy consisting of 32 adenocarcinomas and 31 benign adenomas. Finally, the proposed methods are compared to several typical GLCM-texture descriptors and one deep learning model. LMS obtains the highest performance and enhances the prediction power to 0.9450 with standard deviation 0.0285 by area under the curve of receiver operating characteristics score which is a significant improvement.

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