Correlation of image textures of a polarization feature parameter and the microstructures of liver fibrosis tissues

Mueller matrix imaging is emerging for the quantitative characterization of pathological microstructures and is especially sensitive to fibrous structures.Liver fibrosis is a characteristic of many types of chronic liver diseases.The clinical diagnosis of liver fibrosis requires time-consuming multiple staining processes that specifically target on fibrous structures.The staining proficiency of technicians and the subjective visualization of pathologists may bring inconsistency to clinical diagnosis.Mueller matrix imaging can reduce the multiple staining processes and provide quantitative diagnostic indicators to characterize liver fibrosis tissues.In this study,a fibersensitive polarization feature parameter(PFP)was derived through the forward sequential feature selection(SFS)and linear discriminant analysis(LDA)to target on the identification of fibrous structures.Then,the Pearson correlation coeffcients and the statistical T-tests between the fiber-sensitive PFP image textures and the liver fibrosis tissues were calculated.The results show the gray level run length matrix(GLRLM)-based run entropy that measures the heterogeneity of the PFP image was most correlated to the changes of liver fibrosis tissues at four stages with a Pearson correlation of 0.6919.The results also indicate the highest Pearson correlation of 0.9996 was achieved through the linear regression predictions of the combination of the PFP image textures.This study demonstrates the potential of deriving a fiber-sensitive PFP to reduce the multiple staining process and provide textures-based quantitative diagnostic indicators for the staging of liver fibrosis.

Image block feature vectors based on a singular-value information metric and color-texture description

In this work, image feature vectors are formed for blocks containing sufficient information, which are selected using a singular-value criterion. When the ratio between the first two SVs axe below a given threshold, the block is considered informative. A total of 12 features including statistics of brightness, color components and texture measures are used to form intermediate vectors. Principal component analysis is then performed to reduce the dimension to 6 to give the final feature vectors. Relevance of the constructed feature vectors is demonstrated by experiments in which k-means clustering is used to group the vectors hence the blocks. Blocks falling into the same group show similar visual appearances.

Role of the texture features of images in the diagnosis of solitary pulmonary nodules in different sizes

Objective： To explore the role of the texture features of images in the diagnosis of solitary pulmonary nodules （SPNs） in different sizes. Materials and methods： A total of 379 patients with pathologically confirmed SPNs were enrolled in this study. They were divided into three groups based on the SPN sizes： ≤10, 11-20, and 〉20 mm. Their texture features were segmented and extracted. The differences in the image features between benign and malignant SPNs were compared. The SPNs in these three groups were determined and analyzed with the texture features of images. Results： These 379 SPNs were successfully segmented using the 2D Otsu threshold method and the self-adaptive threshold segmentation method. The texture features of these SPNs were obtained using the method of grey level co-occurrence matrix （GLCM）. Of these 379 patients, 120 had benign SPNs and 259 had malignant SPNs. The entropy, contrast, energy, homogeneity, and correlation were 3.5597±0.6470, 0.5384±0.2561, 0.1921±0.1256, 0.8281±0.0604, and 0.8748±0.0740 in the benign SPNs and 3.8007±0.6235, 0.6088±0.2961, 0.1673±0.1070, 0.7980±0.0555, and 0.8550±0.0869 in the malignant SPNs （all P〈0.05）. The sensitivity, specificity, and accuracy of the texture features of images were 83.3%, 90.0%, and 86.8%, respectively, for SPNs sized 〈10 mm, and were 86.6%, 88.2%, and 87.1%, respectively, for SPNs sized 11-20 mm and 94.7%, 91.8%, and 93.9%, respectively, for SPNs sized 〉20 mm. Conclusions： The entropy and contrast of malignant pulmonary nodules have been demonstrated to be higher in comparison to those of benign pulmonary nodules, while the energy, homogeneity correlation of malignant pulmonary nodules are lower than those of benign pulmonary nodules. The texture features of images can reflect the tissue features and have high sensitivity, specificity, and accuracy in differentiating SPNs. The sensitivity and accuracy increase for larger SPNs.

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.

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.

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.

STUDY ON THE TECHNIQUE TO DETECT TEXTURE FEATURES IN SAR IMAGES

This letter studies on the detection of texture features in Synthetic Aperture Radar (SAR) images. Through analyzing the feature detection method proposed by Lopes, an improved texture detection method is proposed, which can not only detect the edge and lines but also avoid stretching edge and suppressing lines of the former algorithm. Experimental results with both simulated and real SAR images verify the advantage and practicability of the improved method.

A novel high resolution image denoising algorithm based on Calman filter and texture feature extraction

Calman filtering method based on wavelet transform has been successfully applied to signal denoising. According to the different application methods and the realization forms of Calman filter, combined with the structural analysis of wavelet decomposition, we present kinds of multi-scale filtering methods into the category of the three. The simulation results show that the multi-scale Calman filtering method based on system layer has better performance. Synthetic aperture radar （SAR） images have rich texture information, which can reflect the spatial structure of objects. The texture feature is widely used in SAR image classification and SAR image segmentation. Affected by imaging factors, the direct use of texture features extracted from SAR images is not good enough. In order to avoid the traditional method of filtering followed the texture feature extraction caused by the loss of texture and edge information, this paper presents a texture feature extraction of SAR image, then using Robust PCA method, finally using texture feature clustering method K-means test after treatment with RPCA expression.

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.

An improved fast fractal image compression using spatial texture correlation

This paper utilizes a spatial texture correlation and the intelligent classification algorithm （ICA） search strategy to speed up the encoding process and improve the bit rate for fractal image compression. Texture features is one of the most important properties for the representation of an image. Entropy and maximum entry from co-occurrence matrices are used for representing texture features in an image. For a range block, concerned domain blocks of neighbouring range blocks with similar texture features can be searched. In addition, domain blocks with similar texture features are searched in the ICA search process. Experiments show that in comparison with some typical methods, the proposed algorithm significantly speeds up the encoding process and achieves a higher compression ratio, with a slight diminution in the quality of the reconstructed image; in comparison with a spatial correlation scheme, the proposed scheme spends much less encoding time while the compression ratio and the quality of the reconstructed image are almost the same.

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.

Role of Feature Selection on Leaf Image Classification

The digital images have been studied for image classification, enhancement, image compression and image segmentation purposes. In the present work, it is proposed to study the effects of feature selection algorithm on the predictive classification accuracy of algorithms used for discriminating the different plant leaf images. The process involves extracting the important texture features from the digital images and then subjecting them to feature selection and further classification process. The leaf image features have been extracted by using Gabor texture features and these Gabor features are subjected to Random Forest feature selection algorithm for extracting important texture features. The four classification algorithms like K-Nearest Neighbour, J48, Classification and Regression Trees and Random Forest have been used for classification purpose. This study shows that there is a net improvement in the predictive classification accuracy values, when classification algorithms have been applied on selected features over the complete set of features.

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.

Hybrid Segmentation Scheme for Skin Features Extraction Using Dermoscopy Images

Objective and quantitative assessment of skin conditions is essential for cosmeceutical studies and research on skin aging and skin regeneration.Various handcraft-based image processing methods have been proposed to evaluate skin conditions objectively,but they have unavoidable disadvantages when used to analyze skin features accurately.This study proposes a hybrid segmentation scheme consisting of Deeplab v3+with an Inception-ResNet-v2 backbone,LightGBM,and morphological processing(MP)to overcome the shortcomings of handcraft-based approaches.First,we apply Deeplab v3+with an Inception-ResNet-v2 backbone for pixel segmentation of skin wrinkles and cells.Then,LightGBM and MP are used to enhance the pixel segmentation quality.Finally,we determine several skin features based on the results of wrinkle and cell segmentation.Our proposed segmentation scheme achieved a mean accuracy of 0.854,mean of intersection over union of 0.749,and mean boundary F1 score of 0.852,which achieved 1.1%,6.7%,and 14.8%improvement over the panoptic-based semantic segmentation method,respectively.

Remote Sensing Image Retrieval Based on 3D-Local Ternary Pattern(LTP)Features and Non-subsampled Shearlet Transform(NSST)Domain Statistical Features

With the increasing popularity of high-resolution remote sensing images,the remote sensing image retrieval(RSIR)has always been a topic of major issue.A combined,global non-subsampled shearlet transform(NSST)-domain statistical features(NSSTds)and local three dimensional local ternary pattern(3D-LTP)features,is proposed for high-resolution remote sensing images.We model the NSST image coefficients of detail subbands using 2-state laplacian mixture(LM)distribution and its three parameters are estimated using Expectation-Maximization(EM)algorithm.We also calculate the statistical parameters such as subband kurtosis and skewness from detail subbands along with mean and standard deviation calculated from approximation subband,and concatenate all of them with the 2-state LM parameters to describe the global features of the image.The various properties of NSST such as multiscale,localization and flexible directional sensitivity make it a suitable choice to provide an effective approximation of an image.In order to extract the dense local features,a new 3D-LTP is proposed where dimension reduction is performed via selection of‘uniform’patterns.The 3D-LTP is calculated from spatial RGB planes of the input image.The proposed inter-channel 3D-LTP not only exploits the local texture information but the color information is captured too.Finally,a fused feature representation(NSSTds-3DLTP)is proposed using new global(NSSTds)and local(3D-LTP)features to enhance the discriminativeness of features.The retrieval performance of proposed NSSTds-3DLTP features are tested on three challenging remote sensing image datasets such as WHU-RS19,Aerial Image Dataset(AID)and PatternNet in terms of mean average precision(MAP),average normalized modified retrieval rank(ANMRR)and precision-recall(P-R)graph.The experimental results are encouraging and the NSSTds-3DLTP features leads to superior retrieval performance compared to many well known existing descriptors such as Gabor RGB,Granulometry,local binary pattern(LBP),Fisher vector(FV),vector of locally aggregated descriptors(VLAD)and median robust extended local binary pattern(MRELBP).For WHU-RS19 dataset,in terms of{MAP,ANMRR},the NSSTds-3DLTP improves upon Gabor RGB,Granulometry,LBP,FV,VLAD and MRELBP descriptors by{41.93%,20.87%},{92.30%,32.68%},{86.14%,31.97%},{18.18%,15.22%},{8.96%,19.60%}and{15.60%,13.26%},respectively.For AID,in terms of{MAP,ANMRR},the NSSTds-3DLTP improves upon Gabor RGB,Granulometry,LBP,FV,VLAD and MRELBP descriptors by{152.60%,22.06%},{226.65%,25.08%},{185.03%,23.33%},{80.06%,12.16%},{50.58%,10.49%}and{62.34%,3.24%},respectively.For PatternNet,the NSSTds-3DLTP respectively improves upon Gabor RGB,Granulometry,LBP,FV,VLAD and MRELBP descriptors by{32.79%,10.34%},{141.30%,24.72%},{17.47%,10.34%},{83.20%,19.07%},{21.56%,3.60%},and{19.30%,0.48%}in terms of{MAP,ANMRR}.The moderate dimensionality of simple NSSTds-3DLTP allows the system to run in real-time.

A classification method of building structures based on multi-feature fusion of UAV remote sensing images

In order to improve the accuracy of building structure identification using remote sensing images,a building structure classification method based on multi-feature fusion of UAV remote sensing image is proposed in this paper.Three identification approaches of remote sensing images are integrated in this method:object-oriented,texture feature,and digital elevation based on DSM and DEM.So RGB threshold classification method is used to classify the identification results.The accuracy of building structure classification based on each feature and the multi-feature fusion are compared and analyzed.The results show that the building structure classification method is feasible and can accurately identify the structures in large-area remote sensing images.

PRODUCT IMAGE RETRIEVAL BASED ON CO-FEATURES OF THE OBJECT

In this paper, we propose a product image retrieval method based on the object contour corners, image texture and color. The product image mainly highlights the object and its background is very simple. According to these characteristics, we represent the object using its contour, and detect the corners of contour to reduce the number of pixels. Every corner is described using its approximate curvature based on distance. In addition, the Block Difference of Inverse Probabilities (BDIP) and Block Variation of Local Correlation (BVLC) texture features and color moment are extracted from image's HIS color space. Finally, dynamic time warping method is used to match features with different length. In order to demonstrate the effect of the proposed method, we carry out experiments in Mi-crosoft product image database, and compare it with other feature descriptors. The retrieval precision and recall curves show that our method is feasible.

Automatic Feature Extraction from Ocular Images

Ocular images processing is an important task in: i) biometrics system based on retina and/or sclera images, and ii) in clinical ophthalmology diagnosis of diseases like various vascular disorders. We presents a general framework for image processing of ocular images with a particular view on feature extraction. The method uses the set of geometrical and texture features and based on the information of the complex vessel structure of the retina and sclera. The feature extraction contains the image preprocessing, locating and segmentation of the region of interest (ROI). The image processing of ROI and the feature extraction are proceeded, and then the feature vector is determined for the human recognition and ophthalmology diagnosis.

Image Splicing Detection Using Generalized Whittaker Function Descriptor

Image forgery is a crucial part of the transmission of misinformation,which may be illegal in some jurisdictions.The powerful image editing software has made it nearly impossible to detect altered images with the naked eye.Images must be protected against attempts to manipulate them.Image authentication methods have gained popularity because of their use in multimedia and multimedia networking applications.Attempts were made to address the consequences of image forgeries by creating algorithms for identifying altered images.Because image tampering detection targets processing techniques such as object removal or addition,identifying altered images remains a major challenge in research.In this study,a novel image texture feature extraction model based on the generalized k-symbolWhittaker function(GKSWF)is proposed for better image forgery detection.The proposed method is divided into two stages.The first stage involves feature extraction using the proposed GKSWF model,followed by classification using the“support vector machine”(SVM)to distinguish between authentic and manipulated images.Each extracted feature from an input image is saved in the features database for use in image splicing detection.The proposed GKSWF as a feature extraction model is intended to extract clues of tampering texture details based on the probability of image pixel.When tested on publicly available image dataset“CASIA”v2.0(ChineseAcademy of Sciences,Institute of Automation),the proposed model had a 98.60%accuracy rate on the YCbCr(luminance(Y),chroma blue(Cb)and chroma red(Cr))color spaces in image block size of 8×8 pixels.The proposed image authentication model shows great accuracy with a relatively modest dimension feature size,supporting the benefit of utilizing the k-symbol Whittaker function in image authentication algorithms.

Method of weld recognition based on textural feature matching

In this paper an automatic visual method of seam recognizing and seam tracking based on textural feature matching was proposed, in order to recognize the weld of multi-layer or multi-pass welding in which the weld is difficult to be recognized by conventional visual methods. This method focuses on the obvious difference of image textural feature between the weld region and the base metal region, as well as the similarity of the textural features along the welding direction. The method consists of the following steps ： setting image template and choosing the edge region as ROI （ region of interest ）, extracting the image textural feature of the template and the edge region, feature matching, and recognition of weld region. Experiment showed that the method proposed was effective for weld seam recognition in multi-layer welding.