Tongue Image Texture Segmentation Based on Gabor Filter Plus Normalized Cut

2010 ◽  
pp. 115-125 ◽  
Author(s):  
Jianfeng Li ◽  
Jinhuan Shi ◽  
Hongzhi Zhang ◽  
Yanlai Li ◽  
Naimin Li ◽  
...  
Keyword(s):  
Gabor Filter ◽  
Texture Segmentation ◽  
Image Texture ◽  
Normalized Cut

An Evaluation of the Effectiveness of Image-based Texture Features Extracted from Static B-mode Ultrasound Images in Distinguishing between Benign and Malignant Ovarian Masses

2021 ◽  
pp. 016173462199809
Author(s):  
Dhurgham Al-karawi ◽  
Hisham Al-Assam ◽  
Hongbo Du ◽  
Ahmad Sayasneh ◽  
Chiara Landolfo ◽  
...  
Keyword(s):  
Gabor Filter ◽  
Empirical Evaluation ◽  
Texture Features ◽  
Image Texture ◽  
Support Vector ◽  
Simple Majority ◽  
Ultrasound Scan ◽  
Histograms Of Oriented Gradients ◽  
The Individual ◽  
Ovarian Masses

Significant successes in machine learning approaches to image analysis for various applications have energized strong interest in automated diagnostic support systems for medical images. The evolving in-depth understanding of the way carcinogenesis changes the texture of cellular networks of a mass/tumor has been informing such diagnostics systems with use of more suitable image texture features and their extraction methods. Several texture features have been recently applied in discriminating malignant and benign ovarian masses by analysing B-mode images from ultrasound scan of the ovary with different levels of performance. However, comparative performance evaluation of these reported features using common sets of clinically approved images is lacking. This paper presents an empirical evaluation of seven commonly used texture features (histograms, moments of histogram, local binary patterns [256-bin and 59-bin], histograms of oriented gradients, fractal dimensions, and Gabor filter), using a collection of 242 ultrasound scan images of ovarian masses of various pathological characteristics. The evaluation examines not only the effectiveness of classification schemes based on the individual texture features but also the effectiveness of various combinations of these schemes using the simple majority-rule decision level fusion. Trained support vector machine classifiers on the individual texture features without any specific pre-processing, achieve levels of accuracy between 75% and 85% where the seven moments and the 256-bin LBP are at the lower end while the Gabor filter is at the upper end. Combining the classification results of the top k ( k = 3, 5, 7) best performing features further improve the overall accuracy to a level between 86% and 90%. These evaluation results demonstrate that each of the investigated image-based texture features provides informative support in distinguishing benign or malignant ovarian masses.

scholarly journals Variable Selection from Image Texture Feature for Automatic Classification of Concrete Surface Voids

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Ziting Zhao ◽  
Tong Liu ◽  
Xudong Zhao
Keyword(s):  
Variable Selection ◽  
Gabor Filter ◽  
Texture Feature ◽  
Concrete Surface ◽  
Automatic Classification ◽  
Ensemble Classification ◽  
Image Texture ◽  
Inspection Time ◽  
Selection Strategy

Machine learning plays an important role in computational intelligence and has been widely used in many engineering fields. Surface voids or bugholes frequently appearing on concrete surface after the casting process make the corresponding manual inspection time consuming, costly, labor intensive, and inconsistent. In order to make a better inspection of the concrete surface, automatic classification of concrete bugholes is needed. In this paper, a variable selection strategy is proposed for pursuing feature interpretability, together with an automatic ensemble classification designed for getting a better accuracy of the bughole classification. A texture feature deriving from the Gabor filter and gray-level run lengths is extracted in concrete surface images. Interpretable variables, which are also the components of the feature, are selected according to a presented cumulative voting strategy. An ensemble classifier with its base classifier automatically assigned is provided to detect whether a surface void exists in an image or not. Experimental results on 1000 image samples indicate the effectiveness of our method with a comparable prediction accuracy and model explicable.

scholarly journals A cluster-based feature selection method for image texture classification

2019 ◽  
Vol 14 (3) ◽  
pp. 1433 ◽  
Author(s):  
Abbas F. H. Alharan ◽  
Hayder K. Fatlawi ◽  
Nabeel Salih Ali
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Clustering Algorithm ◽  
Gabor Filter ◽  
Texture Feature ◽  
Image Feature ◽  
Image Texture ◽  
Support Vector ◽  
Texture Image ◽  
Texture Feature Extraction

<p>Computer vision and pattern recognition applications have been counted serious research trends in engineering technology and scientific research content. These applications such as texture image analysis and its texture feature extraction. Several studies have been done to obtain accurate results in image feature extraction and classifications, but most of the extraction and classification studies have some shortcomings. Thus, it is substantial to amend the accuracy of the classification via minify the dimension of feature sets. In this paper, presents a cluster-based feature selection approach to adopt more discriminative subset texture features based on three different texture image datasets. Multi-step are conducted to implement the proposed approach. These steps involve texture feature extraction via Gray Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP) and Gabor filter. The second step is feature selection by using K-means clustering algorithm based on five feature evaluation metrics which are infogain, Gain ratio, oneR, ReliefF, and symmetric. Finally, K-Nearest Neighbor (KNN), Naive Bayes (NB) and Support Vector Machine (SVM) classifiers are used to evaluate the proposed classification performance and accuracy. Research achieved better classification accuracy and performance using KNN and NB classifiers that were 99.9554% for Kelberg dataset and 99.0625% for SVM in Brodatz-1 and Brodatz-2 datasets consecutively. Conduct a comparison to other studies to give a unified view of the quality of the results and identify the future research directions.</p>

Texture Segmentation of Jacquard Fabric Using Wavelet-Domain Markov Model

2012 ◽  
Vol 468-471 ◽  
pp. 2720-2723
Author(s):  
Yang Zhang ◽  
You Cheng Tong ◽  
Jun Zhou Yao
Keyword(s):  
Markov Model ◽  
Low Frequency ◽  
Gaussian Mixture ◽  
Texture Segmentation ◽  
Image Texture ◽  
Distribution Model ◽  
Wavelet Domain ◽  
Markov Random ◽  
Multi Level ◽  
Fabric Design

To improve the accuracy and efficiency of fabric design CAD, a wavelet-domain Markov model to image texture segmentation from a natural framework for intergrating both local and global information of jacquard fabric image behavior, together with contextual information.Firstly the Daubechies wavelet and tree-structure is selected, then the approach decomposes the low frequency part of the jacquard fabric image. Secondly within the theoretical framework of Markov random field, we construct the grey field distribution model and label field prior model with finite Gaussian mixture algorithm and multi-level logistic algorithm respectively. The experiments for almost 30 warp knitting jacquard fabric images show that this approach is a feasible way for jacquard fabric, and it supplies a theoretical platform for subsequent research.

WAVELET-BASED SALIENT ENERGY POINTS FOR UNSUPERVISED TEXTURE SEGMENTATION

2005 ◽  
Vol 19 (03) ◽  
pp. 429-458 ◽  
Author(s):  
M. K. BASHAR ◽  
N. OHNISHI
Keyword(s):  
Texture Features ◽  
Texture Segmentation ◽  
Image Texture ◽  
Discrete Wavelet ◽  
Salient Point ◽  
Unsupervised Segmentation ◽  
Image Texture Analysis ◽  
Point Distribution ◽  
Binary Images ◽  
Time Systems

Despite extensive research on image texture analysis, it is still problematic to characterize and segment texture images especially in the presence of complex patterns. Upon tremendous advancement of the internet and the digital technology, there is also a need for the development of simple but efficient algorithms, which can be adaptable to real-time systems. In this study, we propose such an approach based on multiresolution discrete wavelet transform (DWT). After the transform, we compute salient energy points from each directional sub-band (LH, HL, and HH) in the form of binary image by thresholding intermittency indices of wavelet coefficients. We then propose and extract two new texture features namely Salient Point Density (SPD) and Salient Point Distribution Nonuniformity (SPDN) based on the number and the distribution of salient pixels in the local neighborhood of every pixel of the multiscale binary images. We thus obtain a set of feature images, which are subsequently applied to the popular K-means algorithm for the unsupervised segmentation of texture images. Though the above representation appear simple and infrequent in the literature, it proves useful in the context of texture segmentation. Experimental results with the standard texture (Brodatz) and natural images demonstrate the robustness and potentiality of the proposed approach.

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