A Fast Image Retrieval Algorithm with Multi-Channel Textural Features in PACS

The paper presents a fast algorithm for image retrieval using multi-channel textural features in medical picture archiving and communication system （PACS）. By choosing different linear or nonlinear operators in prediction and update lifting step, the linear or nonlinear M-band wavelet decomposition can be achieved in M-band lifting. It provides the advantages such as fast transform, in-place calculation and integer-integer transform. The set of wavelet moment forms multi-channel textural feature vector related to the texture distribution of each wavelet images. The experimental results of CT image database show that the retrieval approach of multi-channel textural features is effective for image indexing and has lower computational complexity and less memory. It is much easier to implement in hardware and suitable for the applications of real time medical processing system.

Quantification of browning in apples using colour and textural features by image analysis

This study analyses the effect of browning through image analysis based on colour and textural features in fresh-cut apple slices.A computer vision system(CVS)was developed for image acquisition,which consisted of a digital camera and a florescent lamp source for illumination with a contrasting background.The CVS was calibrated using standard colour values and a model was developed by artificial neural network technique.Three varieties of apples such as Honey crisp,Granny Smith,and Golden Delicious were used for the analysis.The apples were freshly cut and subjected to image acquisition.Normalized colour features(L*,browning index,hue,and colour change)and textural features(entropy,contrast,and homogeneity)were analysed from the acquired images.The varieties Honey Crisp and Granny Smith did undergo browning within 120 min,whereas Golden delicious did not brown significantly.The study concluded that colour and textural features were important decision features for detecting browning in apples through image analysis.

Recognizing Breast Cancer Using Edge-Weighted Texture Features of Histopathology Images

Around one in eight women will be diagnosed with breast cancer at some time.Improved patient outcomes necessitate both early detection and an accurate diagnosis.Histological images are routinely utilized in the process of diagnosing breast cancer.Methods proposed in recent research only focus on classifying breast cancer on specific magnification levels.No study has focused on using a combined dataset with multiple magnification levels to classify breast cancer.A strategy for detecting breast cancer is provided in the context of this investigation.Histopathology image texture data is used with the wavelet transform in this technique.The proposed method comprises converting histopathological images from Red Green Blue(RGB)to Chrominance of Blue and Chrominance of Red(YCBCR),utilizing a wavelet transform to extract texture information,and classifying the images with Extreme Gradient Boosting(XGBOOST).Furthermore,SMOTE has been used for resampling as the dataset has imbalanced samples.The suggested method is evaluated using 10-fold cross-validation and achieves an accuracy of 99.27%on the BreakHis 1.040X dataset,98.95%on the BreakHis 1.0100X dataset,98.92%on the BreakHis 1.0200X dataset,98.78%on the BreakHis 1.0400X dataset,and 98.80%on the combined dataset.The findings of this study imply that improved breast cancer detection rates and patient outcomes can be achieved by combining wavelet transformation with textural signals to detect breast cancer in histopathology images.

Traffic Sign Detection with Low Complexity for Intelligent Vehicles Based on Hybrid Features

Globally traffic signs are used by all countries for healthier traffic flow and to protect drivers and pedestrians.Consequently,traffic signs have been of great importance for every civilized country,which makes researchers give more focus on the automatic detection of traffic signs.Detecting these traffic signs is challenging due to being in the dark,far away,partially occluded,and affected by the lighting or the presence of similar objects.An innovative traffic sign detection method for red and blue signs in color images is proposed to resolve these issues.This technique aimed to devise an efficient,robust and accurate approach.To attain this,initially,the approach presented a new formula,inspired by existing work,to enhance the image using red and green channels instead of blue,which segmented using a threshold calculated from the correlational property of the image.Next,a new set of features is proposed,motivated by existing features.Texture and color features are fused after getting extracted on the channel of Red,Green,and Blue(RGB),Hue,Saturation,and Value(HSV),and YCbCr color models of images.Later,the set of features is employed on different classification frameworks,from which quadratic support vector machine(SVM)outnumbered the others with an accuracy of 98.5%.The proposed method is tested on German Traffic Sign Detection Benchmark(GTSDB)images.The results are satisfactory when compared to the preceding work.

Feature-based sequential partial vision measurement method for large scale machine parts

To realize the high-precision vision measurement for large scale machine parts, a new vision measurement method based on dimension features of sequential partial images is proposed. Instead of mosaicking the partial images, extracting the dimension features of the sequential partial images and deriving the part size according to the relationships between the sequential images is a novel method to realize the high- precision and fast measurement of machine parts. To overcome the corresponding problems arising from the relative rotation between two sequential partial images, a rectifying method based on texture features is put forward to effectively improve the processing speed. Finally, a case study is provided to demonstrate the analysis procedure and the effectiveness of the proposed method. The experiments show that the relative error is less than 0. 012% using the sequential image measurement method to gauge large scale straight-edge parts. The measurement precision meets the needs of precise measurement for sheet metal parts.

Weber Law Based Approach for Multi-Class Image Forgery Detection

Today’s forensic science introduces a new research area for digital image analysis formultimedia security.So,Image authentication issues have been raised due to the wide use of image manipulation software to obtain an illegitimate benefit or createmisleading publicity by using tempered images.Exiting forgery detectionmethods can classify only one of the most widely used Copy-Move and splicing forgeries.However,an image can contain one or more types of forgeries.This study has proposed a hybridmethod for classifying Copy-Move and splicing images using texture information of images in the spatial domain.Firstly,images are divided into equal blocks to get scale-invariant features.Weber law has been used for getting texture features,and finally,XGBOOST is used to classify both Copy-Move and splicing forgery.The proposed method classified three types of forgeries,i.e.,splicing,Copy-Move,and healthy.Benchmarked(CASIA 2.0,MICCF200)and RCMFD datasets are used for training and testing.On average,the proposed method achieved 97.3% accuracy on benchmarked datasets and 98.3% on RCMFD datasets by applying 10-fold cross-validation,which is far better than existing methods.

Texture features analysis on micro-structure of paste backfill based on image analysis technology

The strength of cement-based materials,such as mortar,concrete and cement paste backfill(CPB),depends on its microstructures(e.g.pore structure and arrangement of particles and skeleton).Numerous studies on the relationship between strength and pore structure(e.g.,pore size and its distribution)were performed,but the micro-morphology characteristics have been rarely concerned.Texture describing the surface properties of the sample is a global feature,which is an effective way to quantify the micro-morphological properties.In statistical analysis,GLCM features and Tamura texture are the most representative methods for characterizing the texture features.The mechanical strength and section image of the backfill sample prepared from three different solid concentrations of paste were obtained by uniaxial compressive strength test and scanning electron microscope,respectively.The texture features of different SEM images were calculated based on image analysis technology,and then the correlation between these parameters and the strength was analyzed.It was proved that the method is effective in the quantitative analysis on the micro-morphology characteristics of CPB.There is a significant correlation between the texture features and the unconfined compressive strength,and the prediction of strength is feasible using texture parameters of the CPB microstructure.

Identification of oral squamous cell carcinoma in optical coherence tomography images based on texture features

Surgical excision is an effective treatment for oral squamous cell carcinoma(OSCC),but exact intraoperative differentiation OSCC from the normal tissue is the first premise.As a noninvasive imaging technique,optical coherence tomography(OCT)has the nearly same resolution as the histopathological examination,whose images contain rich information to assist surgeons to make clinical decisions.We extracted kinds of texture features from OCT images obtained by a home-made swept-source OCT system in this paper,and studied the identification of OSCC based on different combinations of texture features and machine learning classifiers.It was demonstrated that different combinations had different accuracies,among which the combination of texture features,gray level co-occurrence matrix(GLCM),Laws'texture measnres(LM),and center symmetric auto-correlation(CSAC),and SVM as the classifier,had the optimal comprehensive identification effect,whose accuracy was 94.1%.It was proven that it is feasible to distinguish OSCC based on texture features in OCT images,and it has great potential in helping surgeons make rapid and accurate decisions in oral clinical practice.

Mesomechanics coal experiment and an elastic-brittle damage model based on texture features

To accurately describe damage within coal, digital image processing technology was used to determine texture parameters and obtain quantitative information related to coal meso-cracks. The relationship between damage and mesoscopic information for coal under compression was then analysed. The shape and distribution of damage were comprehensively considered in a defined damage variable, which was based on the texture characteristic. An elastic-brittle damage model based on the mesostructure information of coal was established. As a result, the damage model can appropriately and reliably replicate the processes of initiation, expansion, cut-through and eventual destruction of microscopic damage to coal under compression. After comparison, it was proved that the predicted overall stress-strain response of the model was comparable to the experimental result.

A Method of Soil Salinization Information Extraction with SVM Classification Based on ICA and Texture Features

Salt-affected soils classification using remotely sensed images is one of the most common applications in remote sensing,and many algorithms have been developed and applied for this purpose in the literature.This study takes the Delta Oasis of Weigan and Kuqa Rivers as a study area and discusses the prediction of soil salinization from ETM +Landsat data.It reports the Support Vector Machine(SVM) classification method based on Independent Component Analysis(ICA) and Texture features.Meanwhile,the letter introduces the fundamental theory of SVM algorithm and ICA,and then incorporates ICA and texture features.The classification result is compared with ICA-SVM classification,single data source SVM classification,maximum likelihood classification(MLC) and neural network classification qualitatively and quantitatively.The result shows that this method can effectively solve the problem of low accuracy and fracture classification result in single data source classification.It has high spread ability toward higher array input.The overall accuracy is 98.64%,which increases by10.2% compared with maximum likelihood classification,even increases by 12.94% compared with neural net classification,and thus acquires good effectiveness.Therefore,the classification method based on SVM and incorporating the ICA and texture features can be adapted to RS image classification and monitoring of soil salinization.

Effect of MR Field Strength on the Texture Features of Cerebral T2-FLAIR Images: A Pilot Study

Objective To investigate effect of MR field strength on texture features of cerebral T2 fluid attenuated inversion recovery(T2-FLAIR)images.Methods We acquired cerebral 3 D T2-FLAIR images of thirty patients who were diagnosed with ischemic white matter lesion(WML)with MR-1.5 T and MR-3.0 T scanners.Histogram texture features which included mean signal intensity(Mean),Skewness and Kurtosis,and gray level co-occurrence matrix(GLCM)texture features which included angular second moment(ASM),Contrast,Correlation,Inverse difference moment(IDM)and Entropy,of regions of interest located in the area of WML and normal white matter(NWM)were measured by ImageJ software.The texture parameters acquired with MR-1.5 T scanning were compared with MR-3.0 T scanning.Results The Mean of both WML and NWM obtained with MR-1.5 T scanning was significantly lower than that acquired with MR-3.0 T(P<0.001),while Skewness and Kurtosis between MR-1.5 T and MR-3.0 T scanning showed no significant difference(P>0.05).ASM,Correlation and IDM of both WML and NWM acquired with MR-1.5 T revealed significantly lower values than those with MR-3.0 T(P<0.001),while Contrast and Entropy acquired with MR-1.5 T showed significantly higher values than those with MR-3.0 T(P<0.001).Conclusion MR field strength showed no significant effect on histogram textures,while had significant effect on GLCM texture features of cerebral T2-FLAIR images,which indicated that it should be cautious to explain the texture results acquired based on the different MR field strength.

Image Splicing Detection Based on Texture Features with Fractal Entropy

Over the past years,image manipulation tools have become widely accessible and easier to use,which made the issue of image tampering far more severe.As a direct result to the development of sophisticated image-editing applications,it has become near impossible to recognize tampered images with naked eyes.Thus,to overcome this issue,computer techniques and algorithms have been developed to help with the identification of tampered images.Research on detection of tampered images still carries great challenges.In the present study,we particularly focus on image splicing forgery,a type of manipulation where a region of an image is transposed onto another image.The proposed study consists of four features extraction stages used to extract the important features from suspicious images,namely,Fractal Entropy(FrEp),local binary patterns(LBP),Skewness,and Kurtosis.The main advantage of FrEp is the ability to extract the texture information contained in the input image.Finally,the“support vector machine”(SVM)classification is used to classify images into either spliced or authentic.Comparative analysis shows that the proposed algorithm performs better than recent state-of-the-art of splicing detection methods.Overall,the proposed algorithm achieves an ideal balance between performance,accuracy,and efficacy,which makes it suitable for real-world applications.

Retrieval of High Resolution Satellite Images Using Texture Features

In this research, a content-based image retrieval （CBIR） system for high resolution satellite images has been developed by using texture features. The proposed approach uses the local binary pattern （LBP） texture feature and a block based scheme. The query and database images are divided into equally sized blocks, from which LBP histograms are extracted. The block histograms are then compared by using the Chi-square distance. Experimental results show that the LBP representation provides a powerful tool for high resolution satellite images （HRSI） retrieval.

Magma Chamber Process of Post-collisional Magmatism: Insight from Textural and Elemental Characteristics of Plagioclase from the Tatun Volcanic Group, Northern Taiwan Volcanic Zone

The Taiwan mountain belt, one of the youngest orogenies in the world, is caused by the collision of the Luzon arc with the Eurasian margin, which leads to post-collisional extension and magmatism in the Northern Taiwan Volcanic Zone(NTVZ). The magma chamber process in this region has not previously been elucidated in detail. In this paper, the textural and compositional features of plagioclase phenocrysts in basalt from the Tatun Volcanic Group(TTVG) were studied to restrict the dynamics of magma system. Results show that the magma melts in TTVG are mainly sourced from the underlying MORB-like mantle wedge but influenced by incorporation of subduction components, causing the elevated Sr/Y and Ba/Y ratios in magma melts. The subduction components are mainly transported in the form of sediment melt. The plagioclase phenocrysts in the TTVG volcanic rocks are generally coarsely core-sieved with a clear rim. The An contents in the rims of plagioclase are much lower than those of cores, and elevated FeO concentrations are detected in the plagioclase rims. We propose there exists a double-layer magma chamber in this region. The core of the plagioclase was crystalized in the deeper quiescent magma chamber(~21 km), which was subsequently partially dissolved during the ascent of magma melt under H_(2)O-undersaturated condition, forming the typical coarsely sieved texture and synneusis. When this crystal-rich melt migrates into the shallower chamber, water saturation is reached and more sodic plagioclase formed as the rim of phenocryst. Due to the considerably higher fO_(2) in the shallow chamber than in the deeper one, and the distribution of Fe between plagioclase and melt positively correlates with fO_(2), the FeO content in the plagioclase rim elevates in conjunction with increasing fO_(2).

A Method of Using Information Entropy of an Image as an Effective Feature for Com-puter-Aided Diagnostic Applications

Computer-aided detection and diagnosis (CAD) systems are increasingly being used as an aid by clinicians for detection and interpretation of diseases. In general, a CAD system employs a classifier to detect or distinguish between abnormal and normal tissues on images. In the phase of classification, a set of image features and/or texture features extracted from the images are commonly used. In this article, we investigated the characteristic of the output entropy of an image and demonstrated the usefulness of the output entropy acting as a texture feature in CAD systems. In order to validate the effectiveness and superiority of the output-entropy-based texture feature, two well-known texture features, i.e., mean and standard deviation were used for comparison. The database used in this study comprised 50 CT images obtained from 10 patients with pulmonary nodules, and 50 CT images obtained from 5 normal subjects. We used a support vector machine for classification. A leave-one-out method was employed for training and classification. Three combinations of texture features, i.e., mean and entropy, standard deviation and entropy, and standard deviation and mean were used as the inputs to the classifier. Three different regions of interest (ROI) sizes, i.e., 11 × 11, 9 × 9 and 7 × 7 pixels from the database were selected for computation of the feature values. Our experimental results show that the combination of entropy and standard deviation is significantly better than both the combination of mean and entropy and that of standard deviation and mean in the case of the ROI size of 11 × 11 pixels (p < 0.05). These results suggest that information entropy of an image can be used as an effective feature for CAD applications.

Combining Spectral with Texture Features into Objectoriented Classification in Mountainous Terrain Using Advanced Land Observing Satellite Image

Most existing classification studies use spectral information and those were adequate for cities or plains. This paper explores classification method suitable for the ALOS （Advanced Land Observing Satellite） in mountainous terrain. Mountainous terrain mapping using ALOS image faces numerous challenges. These include spectral confusion with other land cover features, topographic effects on spectral signatures （such as shadow）. At first, topographic radiometric correction was carried out to remove the illumination effects of topography. In addition to spectral features, texture features were used to assist classification in this paper. And texture features extracted based on GLCM （Gray Level Co- occurrence Matrix） were not only used for segmentation, but also used for building rules. The performance of the method was evaluated and compared with Maximum Likelihood Classification （MLC）. Results showed that the object-oriented method integrating spectral and texture features has achieved overall accuracy of 85.73% with a kappa coefficient of 0.824, which is 13.48% and o.145 respectively higher than that got by MLC method. It indicated that texture features can significantly improve overall accuracy, kappa coefficient, and the classification precision of existing spectrum confusion features. Object-oriented method Integrating spectral and texture features is suitable for land use extraction of ALOS image in mountainous terrain.

Morphologic and texture features in classifying the malignant and benign breast nodules in ultrasonography

Objective To develop a computer-aided diagnosis(CAD)system with automatic contouring and morphologic and textural analysis to aid on the classification of breast nodules on ultrasound images.Methods A modified Level Set method was proposed to automatically segment the breast nodules(46malignant and 60benign nodules).Following,16morphologic features and 17texture features from the extracted contour were calculated and principal component analysis(PCA)was applied to find the optimal feature vector dimensions.Fuzzy C-means classifier was utilized to identify the breast nodule as benign or malignant with selected principal vectors.Results The performance of morphologic features was 78.30%for accuracy,67.39%for sensitivity and 86.67%for specificity,while the latter was 72.64%,58.70%and 83.33%,respectively.After the combination of the two features,the result was exactly the same with the morphologic performance.Conclusion This system performs well in classifying the malignant breast nodule from the benign breast nodule.

DeepFake Videos Detection Based on Texture Features

In recent years,with the rapid development of deep learning technologies,some neural network models have been applied to generate fake media.DeepFakes,a deep learning based forgery technology,can tamper with the face easily and generate fake videos that are difficult to be distinguished by human eyes.The spread of face manipulation videos is very easy to bring fake information.Therefore,it is important to develop effective detection methods to verify the authenticity of the videos.Due to that it is still challenging for current forgery technologies to generate all facial details and the blending operations are used in the forgery process,the texture details of the fake face are insufficient.Therefore,in this paper,a new method is proposed to detect DeepFake videos.Firstly,the texture features are constructed,which are based on the gradient domain,standard deviation,gray level co-occurrence matrix and wavelet transform of the face region.Then,the features are processed by the feature selection method to form a discriminant feature vector,which is finally employed to SVM for classification at the frame level.The experimental results on the mainstream DeepFake datasets demonstrate that the proposed method can achieve ideal performance,proving the effectiveness of the proposed method for DeepFake videos detection.

Glacier extraction based on ASAR,DEM and texture feature of ASAR using SVM in the Western Qilian Mountains,Northwest China

This paper is focused on the method for extracting glacier area based on ENVISAT ASAR Wide Swath Modes （WSM） data and digital elevation model （DEM） data, using support vector machines （SVM） classification method. The digitized result of the glaci- er coverage area in the western Qilian Mountains was extracted based on Enhanced LandSat Thematic Mapper （ETM＋） imagery, which was used to validate the precision of glacier extraction result. Because of similar backscattering of glacier, shadow and wa- ter, precision of the glacier coverage area extracted from single-polarization WSM data using SVM was only 35.4%. Then, texture features were extracted by the grey level co-occurrence matrix （GLCM）, with extracted glacier coverage area based on WSM data and texture feature information. Compared with the result extracted from WSM data, the precision improved 13.2%. However, the glacier was still seriously confused with shadow and water. Finally, DEM data was introduced to extract the glacier coverage area. Water and glacier can be differentiated because their distribution area has different elevations; shadow can be removed from the classification result based on simulated shadow imagery created by DEM data and SAR imaging parameters; finally, the glacier coverage area was extracted and the precision reached to 90.2%. Thus, it can be demonstrated that the glacier can be accurately semi-automatically extracted from SAR with this method. The method is suitable not only for ENVISAT ASAR WSM imagery, but also for other satellite SAR imagery, especially for SAR imagery covering mountainous areas.

Gender Classification Based on Multi-Scale and Run-Length Features

Human faces can convey substantial information about a person,such as his or her age,race,identity,gender,and emotions.Such facial information can be obtained through techniques like human facial tracking and detection,facial recognition,gender classification,emotion recognition,as well as age estimation.Of these,gender classification is particularly important due to its diverse applications in the fields such as video surveillance and commercial advertising.In this thesis,we propose a method of gender classification based on run-length histograms.The proposed method uses a run-length histogram to record the position information of pixels,thereby efficiently improves the recognition rate and makes the technique suitable for a big-data multimedia database.The experimental results show that the proposed method can achieve better accuracy than a multi-scale based method can.