Image Texture Analysis Techniques Research Articles

The Application of Image Texture Analysis Techniques on the Effects of Dry Needling versus Placebo in Low-Back Pain Patients: A Pilot-Study

Low back pain is the leading cause of disability in the world, with a significant socio-economic impact. Deep dry needling is effective in the treatment of this pain, and it is one of the techniques preferred by physiotherapists. In this field, the use of ultrasound provides information of interest such as length, thickness, diameter, cross-sectional area, or muscle volume, among others. Objective: To find out whether the tissue changes (thickness, histogram, and contraction rate) that occur in the lumbar multifidus after application of the deep dry needle are related to changes in the pain and the disability of the patient. Design: Randomized, double-blind, parallel-group clinical trial. Setting: University of Alcalá, Department of Physiotherapy. Subjects: 21 voluntary patients (women and men) with non-specific low-back pain aged 18–65 years. Intervention: Patients were randomly divided into two groups. One group received dry needling and the other group a dry needling placebo. Initial post-needling and one week post-needling assessments were performed by a therapist blinded to the intervention. Variables: Lumbar multifidus thickness measured by RUSI, contraction time measured by M-mode, histograms measured by image analysis, muscle area, pain measured by VAS, pressure pain threshold measured by pressure algometer, and disability measured by Roland–Morris questionnaire. Conclusions: The contraction speed, resting thickness, and pain demonstrated significant differences within each group, but not between groups. There were significant differences in contraction ratio and in PPT between groups. There was excellent intra-examiner reliability in image collection for histogram analysis. Histogram analysis showed no significant differences between groups and measurements, neither for the parameters nor for the parameters combined with the outcome variables. A robust method for the image texture analyses in future histogram muscle analyses has been performed.

Kernel Granulometric Texture Analysis and Light RES-ASPP-UNET Classification for Covid-19 Detection

This research article proposes an automatic frame work for detecting COVID -19 at the early stage using chest X-ray image. It is an undeniable fact that coronovirus is a serious disease but the early detection of the virus present in human bodies can save lives. In recent times, there are so many research solutions that have been presented for early detection, but there is still a lack in need of right and even rich technology for its early detection. The proposed deep learning model analysis the pixels of every image and adjudges the presence of virus. The classifier is designed in such a way so that, it automatically detects the virus present in lungs using chest image. This approach uses an image texture analysis technique called granulometric mathematical model. Selected features are heuristically processed for optimization using novel multi scaling deep learning called light weight residual–atrous spatial pyramid pooling (LightRES-ASPP-Unet) Unet model. The proposed deep LightRES-ASPP-Unet technique has a higher level of contracting solution by extracting major level of image features. Moreover, the corona virus has been detected using high resolution output. In the framework, atrous spatial pyramid pooling (ASPP) method is employed at its bottom level for incorporating the deep multi scale features in to the discriminative mode. The architectural working starts from the selecting the features from the image using granulometric mathematical model and the selected features are optimized using LightRES-ASPP-Unet. ASPP in the analysis of images has performed better than the existing Unet model. The proposed algorithm has achieved 99.6% of accuracy in detecting the virus at its early stage.

Selection of Superior Rice Seed Features Using Deep Learning Method

Indonesia is a tropical country, known as an agricultural country, where 88.57% of the population works in the agricultural sector. Indonesia is rich in agricultural products such as rice, soybeans, corn, peanuts, cassava and sweet potatoes. Rice (Oryza sativia L) is one of the most dominant food commodities for the people of Indonesia. The carbohydrate content per 100 g of rice reaches 79.34 grams. The main benefit of rice is as a source of carbohydrates and a source of energy for the body. Seed is one of the factors that act as a carrier of technology in advanced agriculture, therefore the seeds used must be of good quality. Farmers tend to equate rice seeds from their previous harvests, the rice seed classification process is carried out manually through visual observation and soaking rice seeds in a container filled with water, submerged and floating rice seeds are selected for use, and those that float are discarded. But in reality it still produces less than optimal yields, for example rice that is less dense and broken. This research was conducted using the k Nearest Neighbor method which can classify by utilizing GLCM feature extraction which is an image texture analysis technique to produce features or information from image objects by calculating the contrast, energy, homogeneity and correlation values, then the best hyperplane will be displayed for distinguish between the two classes, namely superior and non-superior classes using the SVM model. This study aims to optimize yields with better quality. The results of this study were successfully carried out using k-Nearest Neighbor (k-NN) with Euclidean distance and k=5 got the highest accuracy of 92.85%. Indonesia is a tropical country, known as an agricultural country, where 88.57% of the population works in the agricultural sector. Indonesia is rich in agricultural products such as rice, soybeans, corn, peanuts, cassava and sweet potatoes. Rice (Oryza sativia L) is one of the most dominant food commodities for the people of Indonesia. The carbohydrate content per 100 g of rice reaches 79.34 grams. The main benefit of rice is as a source of carbohydrates and a source of energy for the body. Seed is one of the factors that act as a carrier of technology in advanced agriculture, therefore the seeds used must be of good quality. Farmers tend to equate rice seeds from their previous harvests, the rice seed classification process is carried out manually through visual observation and soaking rice seeds in a container filled with water, submerged and floating rice seeds are selected for use, and those that float are discarded. But in reality it still produces less than optimal yields, for example rice that is less dense and broken. This research was conducted using the k Nearest Neighbor method which can classify by utilizing GLCM feature extraction which is an image texture analysis technique to produce features or information from image objects by calculating the contrast, energy, homogeneity and correlation values, then the best hyperplane will be displayed for distinguish between the two classes, namely superior and non-superior classes using the SVM model. This study aims to optimize yields with better quality. The results of this study were successfully carried out using k-Nearest Neighbor (k-NN) with Euclidean distance and k=5 got the highest accuracy of 92.85%.

Development of image texture analysis technique for boulder distribution measurements: Applications to asteroids Ryugu and Itokawa

Recent asteroid missions have revealed that many sub-kilometer asteroids are rubble piles. Large parts of their surfaces are covered with boulders larger than tens of centimeters. An evaluation of the abundance and size distribution of boulders provides clues to understand surface processes on boulder-covered asteroids. Here we report a new method that automatically measures the abundance of small boulders (sub-pixel to a few pixels), whose boundaries cannot be recognized with visual inspection, by quantifying the surface radiance variation that occurs during the spinning of the asteroid. After validating our approach with previous boulder counting data, we apply this method to images of the asteroids Ryugu and Itokawa, which were visited by JAXA's Hayabusa and Hayabusa2, and obtain a global distribution of the boulders larger than 0.75–3 ​m, which corresponds to 1.5–6 pixels. We find that the boulder number density of this size range is smaller (1) on the western bulge than on the eastern hemisphere and (2) on the equatorial ridge than on the higher latitudes, both of which exceed the number density of boulders ​> ​5 ​m by an order of magnitude. The boulder size distribution at 1.25–20 ​m shows that the boulders smaller than 1 ​m are more abundant at the equator than at mid-latitudes, while those larger than 1 ​m in diameter are more abundant at mid-latitudes than at the equator. This contrast suggests size-dependent migration of boulders in the latitudinal direction. We also find that the typical boulder size (the size reaching the cumulative areal coverage of 50%) is 1.9 ​m at the equatorial region (10°S-10°N) while it is 2.6 ​m at mid-latitudes (40°S-50°S, 40°N-50°N). The typical boulder size is also smaller in the western bulge (2.0–2.2 ​m). We construct global maps of the power-law index of the size frequency distribution of boulders and find minor variations over the entire surface of Ryugu (−2.53 ​± ​0.03) for boulders larger than 1.25 ​m. This small variation suggests homogeneous size sorting processes on Ryugu. Surface roughness does not show a significant correlation with the v-band albedo but shows a high anti-correlation (R ​= ​−0.73) with the current geological slope on the eastern hemisphere. Our method is useful enhancement of smooth area detection and boulder distribution characterization that will be applicable to other planetary explorations in the future, including those of Phobos and other asteroids.

CHARACTERIZATION OF HIPPOCAMPUS ON EPILEPTIC PATIENTS ON MRI USING TEXTURE ANALYSIS TECHNIQUES

The aim of this study was to characterize the hippocampus in Sudanese epileptic patients in MR images using image texture analysis techniques in order to differentiate hippocampus between the normal and epileptic patient. There were two groups of the patients were examined by using Signal-GE 1.5Tesla MR Scanner which was used with patients with known epilepsy and normal T1 weighted brain. MRI finding patients, 101 and 105 patients respectively examined in period from December 2017- March 2018, where the variables of the study were MRI images entered to the IDL program as input for further analysis, using window 3*3 the images texture was extracted from hippocampus (head, body and tail) that include, mean, STD, variance, energy, and entropy then the comparison was made to differentiate between the normal and abnormal hippocampus. The extracted feature classified using linear discriminate analysis. The classification score function is used to classify the hippocampus classes was as flows:
 Epileptic= (.271×mean) + (.026×variance) + (7.475× Part) -32.134
 Normal= (.240×mean) + (.052×variance) + (2.960× Part) -13.684
 The study confirmed that it’s possible to differentiate between normal and epileptic hippocampus body, head, and tail in sagittal section texturally. The result showed that the classification result is best in the tail where higher classification accuracy will be achieved followed by body and then head.

Image processing based automatic recognition of asphalt pavement patch using a metaheuristic optimized machine learning approach

Patch detection is an important task in pavement condition survey. This study establishes an automatic approach for asphalt pavement patch recognition based on image texture analysis and hybrid machine learning algorithms. Features based on image texture that employs statistical properties of color channels and the gray-scale co-occurrence matrix are used by the Least Squares Support Vector Machine (LSSVM) for discriminating patched areas from non-patch ones. In addition, to optimize the LSSVM training phase, the Differential Flower Pollination (DFP) metaheuristic is used. A data set constructed from a set of 1000 image samples has been utilized to train and verify the proposed integration of image texture analysis techniques, LSSVM, and DFP. Experimental results show that the new model can achieve a good prediction result with Classification Accuracy Rate = 95.30%, Positive Predictive Value = 0.96, and the Negative Predictive Value = 0.95. Additionally, a patch detection program has been developed and compiled in Visual C# .NET to ease the implementation of the hybrid model. Thus, the newly developed method can be a potential tool for traffic management agencies during the phase of pavement condition evaluation.

Prediction of Pork Fatty Acid Content Using Image Texture Features

ObjectivesThe aim of this study was to investigate the analytical potential of computer image processing and texture analysis techniques to quantify fatty acids associated with soft pork (specifically oleic, linoleic, linolenic, and IV).Materials and MethodsTexture features could be related to the softness of fat that would be related to level of unsaturation (Marshall, 1990). Plus, subtle changes in fatty acids profile could influence L*a*b* value or other color features not detectable by human eye. Fresh pork loins were obtained from 9 pork sides used in a separate research project that compared feeding obese swine a diet containing 16% corn oil (VEG) or 36% beef tallow (ANIM). The 3 mo feeding strategy resulted in significant differences in fatty acid profiles of the subcutaneous fat. The iodine value (IV) and concentration of oleic, linoleic, and linolenic acid expressed as percentage of total fat in a sample of subcutaneous fat collected adjacent the 10th thoracic vertebra were 85.8 (± 5.1), 36.2 (± 2.8), 26.9 (± 3.6), and 2.4 (± 0.4)% for VEG and were 60.2 (± 3.5), 44.5 (± 1.7), 9.8 (± 1.2), and 0.6 (± 0.2)% for ANIM, respectively. After a 24 h chill (2°C), each pork carcass was fully quartered at the 10th/11th rib interface and the blade and picnic shoulder were removed from the anterior section. The remaining rib section was placed against a black backdrop with the posterior (10th rib) facing the high-resolution camera (Fig. 1). After image acquisition, a 40-g sample of subcutaneous fat was collected from the 10thrib section, frozen, and sent to the University of Missouri Experiment Station Chemical Laboratories (Columbia, MO) for fatty acid profile (FAP) analysis. Pork loin images were acquired in the processing plant using a color digital camera (Model EOS 60D, Canon Corporation, Japan) with contrasting (black) background. Images were obtained in the plant to simulate the real-time industrial environment. Subsequent image analysis was conducted separately using image analyzing software (Matlab Version 7, The Math-works, Natick, MA). Five texture features (roughness, contrast, directionality, line-likeness, and heterogeneity) were extracted from the images based on previous work by Tamura, Mori, & Yamawaki (1978).Figure 1Segmentation of subcutaneous fat from a representative pork loin sample.ResultsThe image texture features of contrast, roughness, heterogeneity, line-likeness, and directionality were successfully extracted from images. The image texture features were utilized in linear and stepwise regression models to predict percentages of total fat for oleic, linoleic, and linolenic fatty acids and IV.For all predictions, linear regression models had a higher R2 than stepwise regression models. For oleic acid, the linear regression model had an R2 of 0.86 while the stepwise regression model had an R2 of 0.65. For linoleic and linolenic, both linear regression models resulted in an R2 value of 0.95 while the stepwise regression R2 values for linoleic and linolenic were 0.80 and 0.81, respectively. For IV, the linear regression model reached an R2 of 0.95 while the stepwise regression only had an R2 of 0.82. Due to the small sample size, no validation data was generated for the linear regression model.ConclusionThis research shows the potential for the extraction of image texture features from pork loin images obtained in a processing plant environment as a means for quantifying fatty acids known to influence processing attributes of pork fat.

Prediction of Pork Fatty Acid Content using Image Texture Features

ObjectivesThe aim of this study was to investigate the analytical potential of computer image processing and texture analysis techniques to quantify fatty acids associated with soft pork (specifically oleic, linoleic, linolenic, and IV). Materials and MethodsTexture features could be related to the softness of fat that would be related to level of unsaturation (Marshall, 1990). Plus, subtle changes in fatty acids profile could influence L*a*b* value or other color features not detectable by human eye. Fresh pork loins were obtained from 9 pork sides used in a separate research project that compared feeding obese swine a diet containing 16% corn oil (VEG) or 36% beef tallow (ANIM). The 3 mo feeding strategy resulted in significant differences in fatty acid profiles of the subcutaneous fat. The iodine value (IV) and concentration of oleic, linoleic, and linolenic acid expressed as percentage of total fat in a sample of subcutaneous fat collected adjacent the 10th thoracic vertebra were 85.8 (± 5.1), 36.2 (± 2.8), 26.9 (± 3.6), and 2.4 (± 0.4)% for VEG and were 60.2 (± 3.5), 44.5 (± 1.7), 9.8 (± 1.2), and 0.6 (± 0.2)% for ANIM, respectively. After a 24 h chill (2°C), each pork carcass was fully quartered at the 10th/11th rib interface and the blade and picnic shoulder were removed from the anterior section. The remaining rib section was placed against a black backdrop with the posterior (10th rib) facing the high-resolution camera (Fig. 1). After image acquisition, a 40-g sample of subcutaneous fat was collected from the 10thrib section, frozen, and sent to the University of Missouri Experiment Station Chemical Laboratories (Columbia, MO) for fatty acid profile (FAP) analysis. Pork loin images were acquired in the processing plant using a color digital camera (Model EOS 60D, Canon Corporation, Japan) with contrasting (black) background. Images were obtained in the plant to simulate the real-time industrial environment. Subsequent image analysis was conducted separately using image analyzing software (Matlab Version 7, The Math-works, Natick, MA). Five texture features (roughness, contrast, directionality, line-likeness, and heterogeneity) were extracted from the images based on previous work by Tamura, Mori, & Yamawaki (1978). Figure 1Segmentation of subcutaneous fat from a representative pork loin sample. ResultsThe image texture features of contrast, roughness, heterogeneity, line-likeness, and directionality were successfully extracted from images. The image texture features were utilized in linear and stepwise regression models to predict percentages of total fat for oleic, linoleic, and linolenic fatty acids and IV. For all predictions, linear regression models had a higher R² than stepwise regression models. For oleic acid, the linear regression model had an R² of 0.86 while the stepwise regression model had an R² of 0.65. For linoleic and linolenic, both linear regression models resulted in an R² value of 0.95 while the stepwise regression R² values for linoleic and linolenic were 0.80 and 0.81, respectively. For IV, the linear regression model reached an R2 of 0.95 while the stepwise regression only had an R² of 0.82. Due to the small sample size, no validation data was generated for the linear regression model. ConclusionThis research shows the potential for the extraction of image texture features from pork loin images obtained in a processing plant environment as a means for quantifying fatty acids known to influence processing attributes of pork fat.

Characterization of Brain Glioma in MRI using Image Texture Analysis Techniques

Characterization of Brain Glioma in MRI using Image Texture Analysis Techniques

Dynamic fractal signature dissimilarity analysis for therapeutic response assessment using dynamic contrast-enhanced MRI.

To develop a dynamic fractal signature dissimilarity (FSD) method as a novel image texture analysis technique for the quantification of tumor heterogeneity information for better therapeutic response assessment with dynamic contrast-enhanced (DCE)-MRI. A small animal antiangiogenesis drug treatment experiment was used to demonstrate the proposed method. Sixteen LS-174T implanted mice were randomly assigned into treatment and control groups (n = 8/group). All mice received bevacizumab (treatment) or saline (control) three times in two weeks, and one pretreatment and two post-treatment DCE-MRI scans were performed. In the proposed dynamic FSD method, a dynamic FSD curve was generated to characterize the heterogeneity evolution during the contrast agent uptake, and the area under FSD curve (AUCFSD) and the maximum enhancement (MEFSD) were selected as representative parameters. As for comparison, the pharmacokinetic parameter K(trans) map and area under MR intensity enhancement curve AUCMR map were calculated. Besides the tumor's mean value and coefficient of variation, the kurtosis, skewness, and classic Rényi dimensions d1 and d2 of K(trans) and AUCMR maps were evaluated for heterogeneity assessment for comparison. For post-treatment scans, the Mann-Whitney U-test was used to assess the differences of the investigated parameters between treatment/control groups. The support vector machine (SVM) was applied to classify treatment/control groups using the investigated parameters at each post-treatment scan day. The tumor mean K(trans) and its heterogeneity measurements d1 and d2 values showed significant differences between treatment/control groups in the second post-treatment scan. In contrast, the relative values (in reference to the pretreatment value) of AUCFSD and MEFSD in both post-treatment scans showed significant differences between treatment/control groups. When using AUCFSD and MEFSD as SVM input for treatment/control classification, the achieved accuracies were 93.8% and 93.8% at first and second post-treatment scan days, respectively. In comparison, the classification accuracies using d1 and d2 of K(trans) map were 87.5% and 100% at first and second post-treatment scan days, respectively. As quantitative metrics of tumor contrast agent uptake heterogeneity, the selected parameters from the dynamic FSD method accurately captured the therapeutic response in the experiment. The potential application of the proposed method is promising, and its addition to the existing DCE-MRI techniques could improve DCE-MRI performance in early assessment of treatment response.

Electrically Conductive Membranes Obtained by Simultaneous Electrospinning and Electrospraying Processes

Electrically conductive polyurethane nanostructured membranes have been prepared combining the electrospinning of polymer nanofibers (NFs) with the electrospraying of pristine multiwall carbon nanotubes (MWCNTs) in simultaneous processes. In order to have a better understanding of the distribution of MWCNTs on the surface of the membranes, the optimization of the electrospraying process has been carried out and the distribution of MWCNTs has been evaluated using image texture analysis techniques. Large membranes with a volume resistivity typical of electrostatic discharge materials with a MWCNTs concentration less than 0.3% wt (0.01 mg/cm2) have been obtained and characterized with morphological (SEM and TEM) and spectroscopic (UV-Vis, Raman) techniques.

Automatic Detection of Optic Disc in Retinal Image by Using Keypoint Detection, Texture Analysis, and Visual Dictionary Techniques.

With the advances in the computer field, methods and techniques in automatic image processing and analysis provide the opportunity to detect automatically the change and degeneration in retinal images. Localization of the optic disc is extremely important for determining the hard exudate lesions or neovascularization, which is the later phase of diabetic retinopathy, in computer aided eye disease diagnosis systems. Whereas optic disc detection is fairly an easy process in normal retinal images, detecting this region in the retinal image which is diabetic retinopathy disease may be difficult. Sometimes information related to optic disc and hard exudate information may be the same in terms of machine learning. We presented a novel approach for efficient and accurate localization of optic disc in retinal images having noise and other lesions. This approach is comprised of five main steps which are image processing, keypoint extraction, texture analysis, visual dictionary, and classifier techniques. We tested our proposed technique on 3 public datasets and obtained quantitative results. Experimental results show that an average optic disc detection accuracy of 94.38%, 95.00%, and 90.00% is achieved, respectively, on the following public datasets: DIARETDB1, DRIVE, and ROC.

Despeckle filtering software toolbox for ultrasound imaging of the common carotid artery

Ultrasound imaging of the common carotid artery (CCA) is a non-invasive tool used in medicine to assess the severity of atherosclerosis and monitor its progression through time. It is also used in border detection and texture characterization of the atherosclerotic carotid plaque in the CCA, the identification and measurement of the intima-media thickness (IMT) and the lumen diameter that all are very important in the assessment of cardiovascular disease (CVD). Visual perception, however, is hindered by speckle, a multiplicative noise, that degrades the quality of ultrasound B-mode imaging. Noise reduction is therefore essential for improving the visual observation quality or as a pre-processing step for further automated analysis, such as image segmentation of the IMT and the atherosclerotic carotid plaque in ultrasound images. In order to facilitate this preprocessing step, we have developed in MATLAB® a unified toolbox that integrates image despeckle filtering (IDF), texture analysis and image quality evaluation techniques to automate the pre-processing and complement the disease evaluation in ultrasound CCA images. The proposed software, is based on a graphical user interface (GUI) and incorporates image normalization, 10 different despeckle filtering techniques (DsFlsmv, DsFwiener, DsFlsminsc, DsFkuwahara, DsFgf, DsFmedian, DsFhmedian, DsFad, DsFnldif, DsFsrad), image intensity normalization, 65 texture features, 15 quantitative image quality metrics and objective image quality evaluation. The software is publicly available in an executable form, which can be downloaded from http://www.cs.ucy.ac.cy/medinfo/. It was validated on 100 ultrasound images of the CCA, by comparing its results with quantitative visual analysis performed by a medical expert. It was observed that the despeckle filters DsFlsmv, and DsFhmedian improved image quality perception (based on the expert's assessment and the image texture and quality metrics). It is anticipated that the system could help the physician in the assessment of cardiovascular image analysis.

Evaluation of primary phase morphology of cooling slope cast Al-Si-Mg alloy samples using image texture analysis

Rheopressure die casting is one of the newest casting processes of present era for manufacturing of near-net-shaped cast components with improved mechanical properties and high-dimensional accuracy. Rheopressure die casting demands especially prepared semi-solid alloy slurry having nearly globular primary Al phase. In this study, a cooling slope has been employed to produce semi-solid slurry of Al-Si-Mg (A356) alloy and successively cast in a metallic mould. Image texture analysis techniques have been implemented for accurate evaluation of the primary phase morphology of cast samples. In this research, efforts have been made to apply fractal analysis and run-length statistical analysis techniques for automatic characterization of optical micrographs of cast samples produced at different processing conditions.

Analysis of texture-based features for predicting mechanical properties of horticultural products by laser light backscattering imaging

Light backscattering imaging is an advanced technology applicable as a non-destructive technique for monitoring quality of horticultural products. Because of novelty of this technique, developed algorithms for processing this type of images are in preliminary stage. The present study investigates the feasibility of texture-based analysis and coefficients from space-domain analysis to develop better models for predicting mechanical properties (fruit flesh firmness or elastic modulus) of horticultural products. Images of apple, plum, tomato, and mushroom were acquired using a backscattering imaging setup capturing 660nm. After segmenting the backscattering regions of images by variable thresholding technique, they were subjected to texture analyses and space domain techniques in order to extract a number of features. Adaptive neuro-fuzzy inference system models were developed for firmness or elasticity prediction using individual types of feature sets and their combinations as input for prediction model applicable in real-time applications. Results showed that fusion of the selected feature sets of image texture analysis and space domain techniques provide an effective means for improving the performance of backscattering imaging systems in predicting mechanical properties of horticultural products. The maximum value of correlation coefficient in the prediction stage was achieved as 0.887, 0.790, 0.919, and 0.896 for apple, plum, tomato, and mushroom products, respectively.

Progressive cutting tool wear detection from machined surface images using Voronoi tessellation method

Tool condition monitoring by machine vision approach has been gaining popularity day by day since it is a low cost and flexible method. In this paper, a tool condition monitoring technique by analysing turned surface images has been presented. The aim of this work is to apply an image texture analysis technique on turned surface images for quantitative assessment of cutting tool flank wear, progressively. A novel method by the concept of Voronoi tessellation has been applied in this study to analyse the surface texture of machined surface after the creation of Voronoi diagram. Two texture features, namely, number of polygons with zero cross moment and total void area of Voronoi diagram of machined surface images have been extracted. A correlation study between measured flank wear and extracted texture features has been done for depicting the tool flank wear. It has been found that number of polygons with zero cross moment has better linear relationship with tool flank wear than that of total void area.

Improved multivariate image analysis for product quality monitoring

Product quality monitoring by image texture analysis underwent a tremendous growth in the last few years in several industrial sectors, due to the availability of low-cost digital imaging sensors. Multivariate image analysis (MIA) can be used within an image texture analysis technique to provide a spatial statistical characterization of an image. However, in most cases this spatial characterization is possible only for very local texture neighborhoods, due to the high computational cost of MIA. In this paper, the iMIA (improved Multivariate Image Analysis) algorithm is proposed, that improves over previous implementations of MIA by extending its range of applicability due to its reduced computational complexity and memory requirements. The proposed algorithm uses the Fourier transform and the convolution theorem to efficiently compute the MIA model, in such a way that the image texture can be characterized by taking into account also large neighborhood sizes. The proposed approach is applied to two case studies concerning the estimation of the fiber diameter distribution in nanostructured membranes, and the classification of paper surfaces. The results suggest that the optimum range of spatial statistics used for characterizing the image is related to the size of the main textural features.

Texture Analysis of Mammographic images

Breast cancer is the most common type of cancer among women in the world. Mammography is regarded as an effective tool for early detection and diagnosis of breast cancer. Microcalcification is one of the primary signs of breast cancer. There are various image texture analysis techniques for the detection of the microcalcifications. Screenfilm mammography is still the standard method used to detect early breast cancer, thus leading to early treatment. Digital mammography has recently been designated as the imaging technology with the greatest potential for improving the diagnosis of breast cancer. In this work a feature-based approach is used for analysis and classification of malignancy. Gray-level texture and Wavelet coefficient texture methods are used for feature extraction. Probabilistic Neural Network (PNN) is used for classification of images based on extracted features. The performance of classification by PNN based on features by texture method, wavelet method and combined methods are compared. The Receiver Operating Characteristics (ROC) Analysis is used for performance evaluation.

Automatic characterization of nanofiber assemblies by image texture analysis

This paper presents a machine vision system for the automatic characterization of the quality properties of nanofiber assemblies using image texture analysis techniques. The objective is to use a digital image of a nanofiber membrane in order to estimate the pore size distribution (PSD), fiber diameter distribution (FDD) and permeability of the assembly. The underlying idea is that, after textural features have been extracted from the image in the form of statistical descriptors, these descriptors can be related to the membrane properties using multivariate regression techniques. Two alternative feature extraction techniques are considered, a statistical-based approach (namely, gray-level co-occurrence matrix, GLCM) and a transform-based approach (based on the use of wavelet transforms). Polymer nanofiber membranes fabricated by electrospinning are used as test beds of the proposed system. The estimation results are very satisfactory for all properties with any of the two feature extraction techniques, with the wavelet transform-based approach slightly outperforming the GLCM one. Whereas for the estimation of the PSD and FDD a scanning electron microscope image of the product needs to be available, it is shown that as far as permeability is concerned an image at a much lower magnification scale (i.e. an optical microscope one) is sufficient to provide an accurate property estimation. Based on these results, the proposed system represents a very promising step toward the complete automation of quality assessment procedures in nanomaterials processing.

Grading the crease recovery with twisting of fabric by using image identification technique

Traditionally, the grade of fabric’s crease recovery with twisting is decided by comparing the processed sample fabric with standard sample photograph under some conditions. This method is completely reliant on subjective appraisal, so it is easy to lead to some subjective error and affect the conclusion. In this paper, we use image processing and texture analysis technique to calculate a few of the parameters which describe the fabric’s crease recovery properties. At the same time, we use the Fuzzy priority similarity comparison method to assess the fabric’s crease recovery properties synthetically.