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With the rapid growth in the number of motor vehicles worldwide, the general public is beginning to attach importance to the quality inspection of wheels before they leave the factory. The current wheel defect detection systems are often cumbersome to operate and have low practical performance. Therefore, this research will use dynamic image segmentation, image texture feature extraction and Back Propagation neural network classification based on wheel image defect feature analysis algorithm to achieve automatic intelligent detection of automotive wheel defects. In this study, an intelligent detection system for automotive wheel defects is also designed, and finally the performance of the detection system is tested experimentally to illustrate its practicality. The experimental results show that the proposed intelligent detection system for automotive wheel defects based on image texture features identifies defects in wheel castings with a correct rate of 96% and a false positive rate of only 2%. This illustrates that the detection system proposed in this study has a high recognition rate and can provide a useful reference for the automotive industry inspection. [ABSTRACT FROM AUTHOR]

The objective of this study was to evaluate the usefulness of machine learning based on image texture parameters to discriminate plum stone cultivars. The plums of cultivars 'Emper', 'Kalipso', and 'Polinka' were sampled. For each cultivar, one hundred images of plum stones were acquired using a digital camera. Processing of the plum stone images included the conversion of the images to individual color channels, image segmentation, region of interest (ROI) determination, and texture parameter extraction. Then, the discriminant analysis, including the texture selection and building discriminative models for the evaluation of the diversity of the plum stone cultivars, was carried out. The obtained results of discrimination of plum stone cultivars were very accurate and confirmed the effectiveness of image processing to evaluate the cultivar diversity. The most satisfactory results, reaching 96.67% for the average accuracy for three cultivars (97% for 'Emper', 'Kalipso', and 96% for 'Polinka'), were obtained for the models built based on combined textures selected from all the color channels using the IBk classifier. The developed procedure can be of practical importance for the correct identification of plum stone cultivars and avoiding their mixing to preserve cultivar uniformity. [ABSTRACT FROM AUTHOR]

Global food production in the developing world occurs within sub-hectare fields that are difficult to identify with moderate resolution satellite imagery. Knowledge about the distribution of these fields is critical in food security programs. We developed a semi-automated image segmentation approach using wall-to-wall sub-meter imagery with high-performance computing to map crop area (CA) throughout Tigray, Ethiopia that encompasses over 41,000 km 2 . Multiple processing streams were tested to minimize mapping error while applying five unique smoothing kernels to capture differences in land surface texture associated to CA. Typically, very-small fields (mean < 2 ha) have a smooth image roughness compared to natural scrub/shrub woody vegetation at the ~1 m scale and these features can be segmented in panchromatic imagery with multi-level histogram thresholding. Multi-temporal very-high resolution (VHR) panchromatic imagery with multi-spectral VHR are sufficient in extracting critical CA information needed in food security programs. A 2011 to 2015 CA map was produced, using over 3000 WorldView-1 panchromatic images wall-to-wall in 1/2° mosaics for Tigray, Ethiopia. CA was evaluated with nearly 3000 WorldView-2 2 m multispectral 250 × 250 m image subsets by seven expert interpretations, and with in-situ global positioning system photography. CA estimates ranged from 32 to 41% in sub regions of Tigray with median maximum per bin commission and omission errors of 11% and 1% respectively, with most of the error occurring in bins <15%. This empirical, simple, and low direct cost approach via U.S. government license agreement to access commercial VHR data, could be a viable big-data high-performance computing methodology to extract wall-to-wall CA for other regions of the world that have very-small agriculture fields with similar image texture. [ABSTRACT FROM AUTHOR]

This paper presents an image watermarking technique for authentication and self-recovery called AuSR2. The AuSR2 scheme partitions the cover image into 3 × 3 non-overlapping blocks. The watermark data is embedded into two Least Significant Bit (LSB), consisting of two authentication bits and 16 recovery bits for each block. The texture of each block is preserved in the recovery data. Thus, each tampered pixel can be recovered independently instead of using the average block. The recovery process may introduce the tamper coincidence problem, which can be solved using image inpainting. The AuSR2 implements the LSB shifting algorithm to increase the imperceptibility by 2.8%. The experimental results confirm that the AuSR2 can accurately detect the tampering area up to 100%. The AuSR2 can recover the tampered image with a PSNR value of 38.11 dB under a 10% tampering rate. The comparative analysis proves the superiority of the AuSR2 compared to the existing schemes. [ABSTRACT FROM AUTHOR]

The K-means Iterative Fisher (KIF) algorithm is a robust, unsupervised clustering algorithm applied here to the problem of image texture segmentation. The KIF algorithm involves two steps. First, K-means is applied. Second, the K-means class assignments are used to estimate parameters required for a Fisher linear discriminant (FLD). The FLD is applied iteratively to improve the solution. This combined K-means and iterative FLD is referred to as the KIF algorithm. Two KIF implementations are presented: a mixture resolving approach is extended to an unsupervised binary hierarchical approach. The same binary hierarchical KIF algorithm is used to properly segment images even though the number of classes, the class spatial boundaries, and the number of samples per class vary. The binary hierarchical KIF algorithm is fully unsupervised, requires no a priori knowledge of the number of classes, is a non-parametric solution, and is computationally efficient compared to other methods used for clustering in image texture segmentation solutions. This unsupervised methodology is demonstrated to be an improvement over other published texture segmentation results using a wide variety of test imagery. Gabor filters and co-occurrence probabilities are used as texture features. [Copyright &y& Elsevier]

Abstract: MaZda, a software package for 2D and 3D image texture analysis is presented. It provides a complete path for quantitative analysis of image textures, including computation of texture features, procedures for feature selection and extraction, algorithms for data classification, various data visualization and image segmentation tools. Initially, MaZda was aimed at analysis of magnetic resonance image textures. However, it revealed its effectiveness in analysis of other types of textured images, including X-ray and camera images. The software was utilized by numerous researchers in diverse applications. It was proven to be an efficient and reliable tool for quantitative image analysis, even in more accurate and objective medical diagnosis. MaZda was also successfully used in food industry to assess food product quality. MaZda can be downloaded for public use from the Institute of Electronics, Technical University of Lodz webpage. [Copyright &y& Elsevier]

Compared to multispectral or panchromatic bands, fusion imagery contains both the spectral content of the former and the spatial resolution of the latter. Even though the Estimation of Scale Parameter (ESP), the ESP 2 tool, and some segmentation evaluation methods have been introduced to simplify the choice of scale parameter (SP), shape, and compactness, many challenges remain, including obtaining the natural border of plastic greenhouses (PGs) from a GaoFen-2 (GF-2) fusion imagery, accelerating the progress of follow-up texture analysis, and accurately evaluating over-segmentation and under-segmentation of PG segments in geographic object-based image analysis. Considering the features of high-resolution images, the heterogeneity of fusion imagery was compressed using texture analysis before calculating the optimal scale parameter in ESP 2 in this study. As a result, we quantified the effects of image texture analysis, including increasing averaging operator size (AOS) and decreasing greyscale quantization level (GQL) on PG segments via recognition of a proposed Over-Segmentation Index (OSI)-Under-Segmentation Index (USI)-Error Index of Total Area (ETA)-Composite Error Index (CEI) pattern. The proposed pattern can be used to reasonably evaluate the quality of PG segments obtained from GF-2 fusion imagery and its derivative images, showing that appropriate texture analysis can effectively change the heterogeneity of a fusion image for better segmentation. The optimum setup of GQL and AOS are determined by comparing CEI and visual analysis. [ABSTRACT FROM AUTHOR]

Faced with the problem of incompatibility between traditional information acquisition mode and spaceborne earth observation tasks, starting from the general mathematical model of compressed sensing, a theoretical model of block compressed sensing was established, and a full-process adaptive coding and decoding compressed sensing framework for remote sensing images was proposed, which includes five parts: mode selection, feature factor extraction, adaptive shape segmentation, adaptive sampling rate allocation and image reconstruction. Unlike previous semi-adaptive or local adaptive methods, the advantages of the adaptive encoding and decoding method proposed in this paper are mainly reflected in four aspects: (1) Ability to select encoding modes based on image content, and maximizing the use of the richness of the image to select appropriate sampling methods; (2) Capable of utilizing image texture details for adaptive segmentation, effectively separating complex and smooth regions; (3) Being able to detect the sparsity of encoding blocks and adaptively allocate sampling rates to fully explore the compressibility of images; (4) The reconstruction matrix can be adaptively selected based on the size of the encoding block to alleviate block artifacts caused by non-stationary characteristics of the image. Experimental results show that the method proposed in this article has good stability for remote sensing images with complex edge textures, with the peak signal-to-noise ratio and structural similarity remaining above 35 dB and 0.8. Moreover, especially for ocean images with relatively simple image content, when the sampling rate is 0.26, the peak signal-to-noise ratio reaches 50.8 dB, and the structural similarity is 0.99. In addition, the recovered images have the smallest BRISQUE value, with better clarity and less distortion. In the subjective aspect, the reconstructed image has clear edge details and good reconstruction effect, while the block effect is effectively suppressed. The framework designed in this paper is superior to similar algorithms in both subjective visual and objective evaluation indexes, which is of great significance for alleviating the incompatibility between traditional information acquisition methods and satellite-borne earth observation missions. [ABSTRACT FROM AUTHOR]

Background: The cellular morphology of plant organs is strongly related to other physical properties such as shape, size, growth, mechanical properties or chemical composition. Cell morphology often vary depending on the type of tissue, or on the distance to a specific tissue. A common challenge in quantitative plant histology is to quantify not only the cellular morphology, but also its variations within the image or the organ. Image texture analysis is a fundamental tool in many areas of image analysis, that was proven efficient for plant histology, but at the scale of the whole image. Results: This work presents a method that generates a parametric mapping of cellular morphology within images of plant tissues. It is based on gray level granulometry from mathematical morphology for extracting image texture features, and on Centroidal Voronoi Diagram for generating a partition of the image. Resulting granulometric curves can be interpreted either through multivariate data analysis or by using summary features corresponding to the local average cell size. The resulting parametric maps describe the variations of cellular morphology within the organ. Conclusions: We propose a methodology for the quantification of cellular morphology and of its variations within images of tissue sections. The results should help understanding how the cellular morphology is related to genotypic and / or environmental variations, and clarify the relationships between cellular morphology and chemical composition of cell walls. [ABSTRACT FROM AUTHOR]

Image texture features and details are extremely important in image super-resolution. Meanwhile, preserving image texture features and details with less running time is also a significant problem to be solved. This paper proposes an image super-resolution method based on polynomial exponential function and non-uniform rectangular partition to restore the image structure and the details of edges and textures. The algorithm performs least-squares fitting according to the image grayscale to divide the low-resolution image into several different rectangular regions. By substituting the coordinates into each regional polynomial exponential function, the approximated regional high-resolution sub-image can be obtained to form the final whole high-resolution image. The proposed method can better represent the image textures in complex regions and effectively preserve edge details and suppress artifacts. The experimental results demonstrate that the proposed method is significantly superior to the state-of-the-art methods in the terms of subjective vision, objective quantitative evaluation, and execution time. [ABSTRACT FROM AUTHOR]

Markov random field model (MRF) has attracted great attention in the field of image segmentation. Its basic unit can be pixels or regions. These pixel‐based or region‐based MRF models have their own advantages and disadvantages. In order to complement advantages of each other, a unified Markov random field (UMRF) model is proposed in this paper. The strength of the UMRF model lies in two aspects. First, the proposed model combines the benefits of the pixel‐based and the region‐based MRF models by decomposing the likelihood function into the product of the pixel likelihood function and the regional likelihood function. It can make the UMRF model take both pixel information and regional information into account. Second, a new regional feature is designed for the UMRF model to describe macro texture patterns. A principled probabilistic inference is developed to integrate different types of likelihood information and the spatial constraint by iteratively updating the posterior probability of the proposed model. Segmentation results can be achieved when iteration converges. Texture, remote sensing and nature images are employed to test the effectiveness of the proposed model. Experimental results illustrate that our model can achieve higher segmentation accuracy than either the pixel‐based or region‐based MRF models. [ABSTRACT FROM AUTHOR]

The purpose of this study was to investigate the correlation between image features extracted from PET images and the accuracy of manually drawn lesion contours in the lung. Such correlations are interesting in that they could potentially be used in predictive models to help guide physician contouring. In this work, 26 synthetic PET datasets were created using an anthropomorphic phantom and Monte Carlo simulation. Manual contours of simulated lesions were provided by 10 physicians. Contour accuracy was quantified using five commonly used similarity metrics which were then correlated with several features extracted from the images. Features were sub-divided into three groups using intensity, geometry, and texture as categorical descriptors. When averaged among the participants, the results showed relatively strong correlations with complexity and contrast I ( r ≥ 0.65, p < 0.001), and moderate correlations with several other image features ( r ≥ 0.5, p < 0.01). The predictive nature of these correlations was improved through stepwise regression and the creation of multi-feature models. Imaging features were also correlated with the standard deviation of contouring error in order to investigate inter-observer variability. Several features were consistently identified as influential including integral of mean curvature and complexity. These relationships further the understanding as to what causes variation in the contouring of PET positive lesions. [ABSTRACT FROM AUTHOR]

Local binary pattern (LBP) is a frequently‐used texture descriptor. Lots of LBP‐variants have been proposed to improve its performance of representing textures. However, most of them ignore the global and neighbour‐difference information of an image texture. In this study, the authors propose a structural difference histogram representation by fusing the segmented structure pattern (SSP), the refined LBP (RLBP) and the neighbour‐difference pattern (NDP) for texture classification. Particularly, the segmented structure, which contains the global contour information of an image texture, is first constructed to compute its SSP histogram feature. Simultaneously, the RLBP is defined to represent the local texture information. Furthermore, the NDP is presented to describe differences between neighbours of a centre pixel in the local patch of texture images. Experimental results on Brodatz and Columbia‐Utrecht reflectance databases indicate that the proposed method can achieve the satisfactory classification accuracy compared with several representative methods. [ABSTRACT FROM AUTHOR]

Although simple and efficient, traditional feature-based texture segmentation methods usually suffer from the intrinsical less inaccuracy, which is mainly caused by the oversimplified assumption that each textured subimage used to estimate a feature is homogeneous. To solve this problem, an adaptive segmentation algorithm based on the coupled Markov random field (CMRF) model is proposed in this paper. The CMRF model has two mutually dependent components: one models the observed image to estimate features, and the other models the labeling to achieve segmentation. When calculating the feature of each pixel, the homogeneity of the subimage is ensured by using only the pixels currently labeled as the same pattern. With the acquired features, the labeling is obtained through solving a maximum a posteriori problem. In our adaptive approach, the feature set and the labeling are mutually dependent on each other, and therefore are alternately optimized by using a simulated annealing scheme. With the gradual improvement of features' accuracy, the labeling is able to locate the exact boundary of each texture pattern adaptively. The proposed algorithm is compared with a simple MRF model based method in segmentation of Brodatz texture mosaics and real scene images. The satisfying experimental demonstrate that the proposed approach can differentiate textured images more accurately. [ABSTRACT FROM AUTHOR]

Image processing technology assists physicians in the analysis of athletes' human motion injuries, not only to improve the accuracy of athletes' injury detection but also to improve the localization and recognition of injury locations. It is important to accurately segment human motion injury ultrasound medical images. To address many problems such as poor effect of traditional ultrasonic medical image segmentation algorithm for a sports injury. Therefore, we propose a segmentation algorithm for human motion injury ultrasound medical images using deep feature fusion. First, the accurate estimated value of human posture is extracted and combined with image texture features and image gray value as the target feature value of the ultrasonic medical image of human motion injury. Second, the image features are deeply fused by an adaptive fusion algorithm to enhance the image resolution. Finally, the best segmentation value of the image is obtained by the trained support vector machine to realize the accurate segmentation of human motion injury ultrasonic medical image. The results show that the average accuracy of the posture accurate estimation of the proposed algorithm is 95.97%; the segmentation time of the human motion injury ultrasound medical image of the proposed algorithm is below 150 ms; and the convergence of the algorithm is completed when the number of iterations is 3. The maximum segmentation error rate is 2.68%. The image segmentation effect is consistent with the ideal target segmentation effect. The proposed algorithm has important application value in the field of ultrasonic medical diagnosis of sports injury. [ABSTRACT FROM AUTHOR]

In this paper, from the perspective of improving the visual communication of brand design, image texture intelligent matching processing is needed, proposing a brand design texture intelligent matching method based on visual communication, constructing a brand design texture intelligent information acquisition model under visual communication, using automatic image imaging technology for texture imaging and feature segmentation of brand design, and extracting typical brand design and ethnic design language of texture histogram, texture segmentation, and automatic matching under visual communication according to histogram distribution, brand design texture information enhancement and optimization detection by regularized feature fusion method, extraction of edge contour feature points of brand design, and texture matching with the extracted edge contour feature points of decorative patterns as input statistics. The adaptive performance of texture matching for a brand design using this method is better, and the texture discrimination ability is stronger, which improves the application research of better reflecting brand design in modern visual communication design and promotes the innovative combination of traditional cultural elements and modern design. [ABSTRACT FROM AUTHOR]

Aiming at the poor anti-jamming effect of traditional fuzzy c-means clustering image segmentation method, a fuzzy c-means clustering image segmentation algorithm based on Hidden Markov model is proposed. Hidden Markov model is used to extract the model state, observation state, initial probability, transition probability and visible symbol probability parameters of fuzzy c-means clustering image. According to these parameters, the image texture is measured, and the image mean is removed to eliminate the influence of brightness. Then the image features are extracted by combining the standard deviation. Through the color space and convert it from RGB model to his model. On the basis of removing the image noise, the image is smoothed to achieve image segmentation. Experimental results show that the algorithm has good segmentation effect, smooth segmentation boundary, little influence of image noise and strong robustness. [ABSTRACT FROM AUTHOR]

Local binary patterns (LBPs) are used for effective texture representation in various applications. This study explores the clustering consistency and stability of image segmentation when distance‐based clustering methods are used with an LBP. Because data are described by features and attributes, distances among data are also dominated by the definition of features. Moreover, four popular LBP encoding schemes for segmenting computed tomography (CT) images by using fuzzy C‐means are discussed and compared. The experimental results demonstrate several notable phenomena: When the pixels are encoded in different LBP encoding schemes, the distance between these pixels varies considerably. The experimental results indicate that each LBP encoding scheme emphasizes a specific image texture that dominates how pixels are described in the feature space and thus affects the clustering results. Based on the evaluation of CT image segmentation by using the clustering inconsistency index, linear and corner‐like LBPs are particularly suitable for clustering longitudinal sections and cross sections of CT images, respectively. [ABSTRACT FROM AUTHOR]

The extraction of river planforms from remotely sensed satellite images is a task of crucial importance to many applications such as land planning, water resource monitoring, or flood prediction. In this article, we present a novel framework for the extraction of rivers from synthetic aperture radar (SAR) images, based on superpixel segmentation and subsequent classification. Superpixel segmentation is achieved by a modeling of the image pixels’ amplitudes and spatial coordinates as a finite mixture model, where the generalized Gamma distribution is used to model accurately a variety of high-resolution SAR scenes. A number of features describing image texture and statistics are extracted on a superpixel level, facilitating the identification of river superpixels—planforms are then extracted by unsupervised, agglomerative clustering, thus eliminating the need for labeled training data. We present the results of our proposed method on the ICEYE-X2 and SENTINEL-1 SAR data, demonstrating its ability to produce pixel-accurate river masks. [ABSTRACT FROM AUTHOR]

It is meaningful to develop the automation segmentation of rice planthopper pests based on imaging technology in precision agriculture. However, rice planthopper images affected by light and complicated backgrounds in open rice fields make the segmentation difficult. This study proposed a segmentation approach of rice planthopper images based on the Markov random field to conduct effective segmentation. First, fractional order differential was introduced into the extraction process of image texture features to gain complete texture information of rice planthopper images. Observation data modeling was established by a combination of image color features and texture features to overcome the disadvantages of insufficient image texture information. Finally, the improved potential function models, the neighborhood relationship between the pixel labels, and the attributes of pixels were defined. The segmentation results were assessed by quantitative evaluation. The experiments showed that the proposed improved approach in the study was more robust, especially with the changes in the illumination condition. This approach can effectively improve segmentation accuracy and promote vision segmentation results of rice planthopper images. [ABSTRACT FROM AUTHOR]

Purpose: Whole-body positron emission tomography/computed tomography (PET/CT) has become a standard method of imaging patients with various disease conditions, especially cancer. Body-wide accurate quantification of disease burden in PET/CT images is important for characterizing lesions, staging disease, prognosticating patient outcome, planning treatment, and evaluating disease response to therapeutic interventions. However, body-wide anatomy recognition in PET/CT is a critical first step for accurately and automatically quantifying disease body-wide, body-region-wise, and organwise. This latter process, however, has remained a challenge due to the lower quality of the anatomic information portrayed in the CT component of this imaging modality and the paucity of anatomic details in the PET component. In this paper, the authors demonstrate the adaptation of a recently developed automatic anatomy recognition (AAR) methodology [Udupa et al., "Body-wide hierarchical fuzzy modeling, recognition, and delineation of anatomy in medical images," Med. Image Anal. 18, 752-771 (2014)] to PET/CT images. Their goal was to test what level of object localization accuracy can be achieved on PET/CT compared to that achieved on diagnostic CT images. Methods: The authors advance the AAR approach in this work in three fronts: (i) from body-regionwise treatment in the work of Udupa et al. to whole body; (ii) from the use of image intensity in optimal object recognition in the work of Udupa et al. to intensity plus object-specific texture properties, and (iii) from the intramodality model-building-recognition strategy to the intermodality approach. The whole-body approach allows consideration of relationships among objects in different body regions, which was previously not possible. Consideration of object texture allows generalizing the previous optimal threshold-based fuzzy model recognition method from intensity images to any derived fuzzy membership image, and in the process, to bring performance to the level achieved on diagnostic CT and MR images in body-region-wise approaches. The intermodality approach fosters the use of already existing fuzzy models, previously created from diagnostic CT images, on PET/CT and other derived images, thus truly separating the modality-independent object assembly anatomy from modality-specific tissue property portrayal in the image. Results: Key ways of combining the above three basic ideas lead them to 15 different strategies for recognizing objects in PET/CT images. Utilizing 50 diagnostic CT image data sets from the thoracic and abdominal body regions and 16 whole-body PET/CT image data sets, the authors compare the recognition performance among these 15 strategies on 18 objects from the thorax, abdomen, and pelvis in object localization error and size estimation error. Particularly on texture membership images, object localization is within three voxels on whole-body low-dose CT images and 2 voxels on body-region-wise low-dose images of known true locations. Surprisingly, even on direct body-region-wise PET images, localization error within 3 voxels seems possible. Conclusions: The previous body-region-wise approach can be extended to whole-body torso with similar object localization performance. Combined use of image texture and intensity property yields the best object localization accuracy. In both body-region-wise and whole-body approaches, recognition performance on low-dose CT images reaches levels previously achieved on diagnostic CT images. The best object recognition strategy varies among objects; the proposed framework however allows employing a strategy that is optimal for each object. [ABSTRACT FROM AUTHOR]

Image segmentation is one of the most important aspects of image processing that has many applications in image analysis, computer vision, and machine vision. So far, researchers have proposed many image segmentation algorithms that provide a relatively appropriate response for a particular application and over a specific type of input images. At the same time, they have not yet suggested a simple, fast, and efficient segmentation method that can apply to a wide range of natural and texture images. In this study, the authors present a new adaptive method for selecting Gabor filters using the image frequency content. Adaptive spectral features of the image are then extracted using only these selected filters from a Gabor filter bank. A pre‐setting parameter Gabor filter is used to extract the gradient of each feature in x and y directions. Combining these features gradient, the texture gradient image is calculated, and then by selected markers for different texture regions and applying the marker‐based watershed transform on texture gradient image, the authors present a new image segmentation method that has a less computational cost and yields better results compared to using all other filter banks. [ABSTRACT FROM AUTHOR]

Image segmentation is one of the most important aspects of image processing that has many applications in image analysis, computer vision, and machine vision. So far, researchers have proposed many image segmentation algorithms that provide a relatively appropriate response for a particular application and over a specific type of input images. At the same time, they have not yet suggested a simple, fast, and efficient segmentation method that can apply to a wide range of natural and texture images. In this study, the authors present a new adaptive method for selecting Gabor filters using the image frequency content. Adaptive spectral features of the image are then extracted using only these selected filters from a Gabor filter bank. A pre-setting parameter Gabor filter is used to extract the gradient of each feature in x and y directions. Combining these features gradient, the texture gradient image is calculated, and then by selected markers for different texture regions and applying the marker-based watershed transform on texture gradient image, the authors present a new image segmentation method that has a less computational cost and yields better results compared to using all other filter banks. [ABSTRACT FROM AUTHOR]

The conventional JSEG algorithm has a powerful detection capability on the homogeneity of regional texture features because it combines the spectral information with image texture features during the segmentation. However, the conventional JSEG method is not very accurate for the target edge localization in segmentation results. To solve this problem, this paper proposes an improved segmentation method of remotely sensed image based on JSEG algorithm and fuzzy c-means (FCM) with spatial constraints. Firstly, the FCM clustering method based on spatial neighborhood terms is used to replace the traditional HCM clustering method in the quantization step. Then the region growing method is applied to segment the class diagram after FCM clustering. Finally, the proposed method uses the improved regional merger approach to merger the over divided region after segmentation. According to the J index, the proposed algorithm is improved by 31% and 12% compared with the traditional JSEG segmentation method and improved by 17% and 8% compared with the FNEA segmentation algorithm for aerial image and the SPOT 5 image. The experimental results show that the proposed segmentation algorithm has good noise immunity because of the fuzzy clustering of spatial constraints and can extract the edge of the target more accurately. [ABSTRACT FROM AUTHOR]

Texture analysis is an important topic in Ultrasound (US) image analysis for structure segmentation and tissue classification. In this work a novel approach for US image texture feature extraction is presented. It is mainly based on parametrical modelling of a signal version of the US image in order to process it as data resulting from a dynamical process. Because of the predictive characteristics of such a model representation, good estimations of texture features can be obtained with less data than generally used methods require, allowing higher robustness to low Signal-to-Noise ratio and a more localized US image analysis. The usability of the proposed approach was demonstrated by extracting texture features for segmenting the thyroid in US images. The obtained results showed that features corresponding to energy ratios between different modelled texture frequency bands allowed to clearly distinguish between thyroid and non-thyroid texture. A simple k-means clustering algorithm has been used for separating US image patches as belonging to thyroid or not. Segmentation of thyroid was performed in two different datasets obtaining Dice coefficients over 85%. [ABSTRACT FROM AUTHOR]

This paper presents an integrated system for the automatic analysis of mammograms to assist radiologists in confirming their diagnosis in mammography screening. The proposed automated confirmatory system (ACS) can process a digitalized mammogram online, and generates a high quality filtered segmentation of an image for biological interpretation and a texture-feature based diagnosis. We use a serial of image pre-processing and segmentation techniques, including 2D median filtering, seeded region growing (SRG) algorithm, image contrast enhancement, to remove noise, delete radiopaque artifacts and eliminate the projection of the pectoral muscle from a digitalized mammogram. We also develop an entire-image texture-feature based classification method, by combining a Rough-set approach to extract five fundamental texture features from images, and then an Artificial Neural Network technique to classify a mammogram as: normal; indicating the presence of a benign lump; or representing a malignant tumor. Here, 222 random images from the Mammographic Image Analysis Society (MIAS) database are used for the offline ACS training. Once the system is tuned and trained, it is ready for the automated use for the analysis and diagnosis of new mammograms. To test the trained system, a separate set of 100 random images from the MIAS and another set of 100 random images from the independent BancoWeb database are selected. The proposed ACS is shown to be successful in confirming diagnosis of mammograms from the two independent databases. [ABSTRACT FROM AUTHOR]

This paper introduces a novel region-based active contour model (ACM) with modified fractional non-Markov Poisson stochastic process (MFNMPS) to segment images within intensity inhomogeneity interface in a short time. Utilizing the global ACM, the technique integrates the MFNMPS in its edge enhancement. The MFNMPS with total energy is employed to enhance rapid contour movement within an enhanced of edge in the image texture. The fractional Poisson stochastic process with its counting function could classify the inhomogeneous object in a region in providing a smooth homogeneous region for segmentation with low computational cost. The MFNMPS with global energy allows the contour development to transfer toward the object founded on the preserved edges in providing improved segmentation. The conforming Euler-Lagrange is applied within the level set framework to minimize the energy. This study practices the proposed process to segment images with intensity inhomogeneity in segmenting several synthetic and medical images. With improved speed, the proposed method more accurately segments medical images compared with other baseline approaches. [ABSTRACT FROM AUTHOR]

This paper proposes a new texture enhancement method which uses an image decomposition that allows different visual characteristics of textures to be represented by separate components in contrast with previous methods which either enhance texture indirectly or represent all texture information using a single image component. Our method is intended to be used as a preprocessing step prior to the use of texture-based image segmentation algorithms. Our method uses a modification of morphological component analysis (MCA) which allows texture to be separated into multiple morphological components each representing a different visual characteristic of texture. We select four such texture characteristics and propose new dictionaries to extract these components using MCA. We then propose procedures for modifying each texture component and recombining them to produce a texture-enhanced image. We applied our method as a preprocessing step prior to a number of texture-based segmentation methods and compared the accuracy of the results, finding that our method produced results superior to comparator methods for all segmentation algorithms tested. We also demonstrate by example the main mechanism by which our method produces superior results, namely that it causes the clusters of local texture features of each distinct image texture to mutually diverge within the multidimensional feature space to a vastly superior degree versus the comparator enhancement methods. [ABSTRACT FROM AUTHOR]

A noniterative fractional-order (FO) two-dimensional (2-D) adaptive filtering mechanism is proposed in this article. Quartiles-based adaptive textural segmentation is employed for the calculation of texture-dependent FO. Statistically, the quartiles/quintiles/quantiles are cut points for dividing the range of a probability distribution into continuous intervals with equal probabilities. For the purpose of texture-based isolation of the spatial regions, a 2-D textural map is framed by evaluating spatial entropy by considering a pixel-wise local circular neighborhood. A novel end-to-end framework is proposed for FO texture-dependent image sharpening in an independent manner without influencing the other classes of textural regions. A novel inclusion of texture-wise adaptive gamma correction is also proposed in this article by drafting a mechanism where different kinds of textural regions can be separately processed in an independent manner. A novel quintiles-based multiscale Retinex (MSR) inspired approach for reflectance computation is coined in this article for suppressing environmental artifacts and unbalanced/nonuniform illumination. In this context, various scales required for MSR are themselves computed through quartiles-based intensity levels. The proposed model is highly modular. So, it can also be pipelined in a parallel manner, along with any well-established state-of-the-art contrast enhancement approach. Also, this approach is noniterative and highly robust. It can be proposed as an add-on for several possible (as well as pre-existing) image processing procedures. Rigorous comparative evaluations are performed so that the excellence of the proposed approach can be underlined. [ABSTRACT FROM AUTHOR]

In order to solve the problem of muskmelon fruit image segmentation under a complex background, an algorithm of image segmentation based on fusing color feature and texture feature was proposed in this paper. First, the collected muskmelon fruit images were transformed from RGB color space to CIELAB color space and HSV space respectively. According to the color characteristics of muskmelon fruit, the collected images were binarized using the threshold of angle model that was set up in using a*b* components in CIELAB color space. To reduce the influence of the uneven illumination distribution of segmentation, the H S components segmentation threshold was selected to binarize the collected images. Converging the results of the angle model segmentation and the HS weighted threshold segmentation, the results were obtained based on color feature segmentation. Then, the image texture features were extracted and the binarization images were obtained by using texture feature threshold. The segmentation results were achieved by fusing the texture features and the color feature segmentation result. Finally, taking the fruit color feature segmentation area as the qualification, the final segmentation results were obtained by binding growth based on the segmentation area that were obtained by fusing color features and texture features. In order to evaluate the effect of proposed algorithm, the collected images were segmented using the super green threshold algorithm and the NDI algorithm and the results were gained. The average detection rate of three algorithms were 83.24%, 43.12% and 99.09%, respectively. Comparing the results of the detection rate and false detection rate, the experimental results of the proposed algorithm were superior to super green feature segmentation and normalized difference index (NDI) segmentation algorithm. [ABSTRACT FROM AUTHOR]

This article proposes a new multispectral image texture segmentation algorithm using a multi-resolution fuzzy Markov random field model for a variable scale in the wavelet domain. The algorithm considers multi-scalar information in both vertical and lateral directions. The feature field of the scalable wavelet coefficients is modelled, combining with the fuzzy label field describing the spatially constrained correlations between neighbourhood features to achieve a more accurate parameter estimation. The extended scalable label field models the label data from different scales to obtain more homogeneous areas; image segmentation results are finally obtained according to the Bayesian rule from a coarser to a finer scale. Multispectral texture images and remote-sensing images are used to test the effectiveness of the the proposed method. Segmentation results show that the new method simultaneously presents a better performance in achieving the homogeneity of the region and accuracy of detected boundaries compared with existing image segmentation algorithms. [ABSTRACT FROM PUBLISHER]

Abstract: Modern medical diagnosis utilizes techniques of visualization of human internal organs (CT, MRI) or of its metabolism (PET). However, evaluation of acquired images made by human experts is usually subjective and qualitative only. Quantitative analysis of MR data, including tissue classification and segmentation, is necessary to perform e.g. attenuation compensation, motion detection, and correction of partial volume effect in PET images, acquired with PET/MR scanners. This article presents briefly a MaZda software package, which supports 2D and 3D medical image analysis aiming at quantification of image texture. MaZda implements procedures for evaluation, selection and extraction of highly discriminative texture attributes combined with various classification, visualization and segmentation tools. Examples of MaZda application in medical studies are also provided. [Copyright &y& Elsevier]

It is crucial to evaluate human settlements to maintain human-based social development. To accurately extract human settlements land-cover information, this article focuses on the unsupervised image segmentation in high-resolution, remotely sensed imagery. J-based segmentation (JSEG) algorithm can offer good segmentation results, but it is highly dependent on image merging thresholds. In this article, a combined, unsupervised image segmentation algorithm is proposed, in which an initial segmentation result is first produced by JSEG and then the image texture is extracted for later region merging. The experiments on the high-resolution imagery show that, when compared with the results obtained by the original JSEG algorithm and eCognition, the proposed algorithm improves the segmentation accuracy. [ABSTRACT FROM AUTHOR]

BACKGROUND The precise assessment of embryo viability is an extremely important factor for the optimization of IVF treatments. In order to assess embryo viability, several embryo scoring systems have been developed. However, they rely mostly on a subjective visual analysis of embryo morphological features and thus are subject to inter- and intra-observer variation. In this paper, we propose a method for image segmentation (the dividing of an image into its meaningful constituent regions) and classification of human blastocyst images with the aim of automating embryo grading. METHODS The delineation of the boundaries (segmentation) of the zona pellucida, trophectoderm (TE) and inner cell mass (ICM) were performed using advanced image analysis techniques (level set, phase congruency and fitting of ellipse methods). The fractal dimension and mean thickness of TE and ICM image texture descriptors (texture spectrum and grey-level run lengths) were calculated to characterize the main morphological features of the blastocyst with the aim of automatic grading using Support Vector Machine classifiers. RESULTS The fractal dimension calculated from the delineated TE boundary provided a good indication of cell number (presented a 0.81 Pearson correlation coefficient with the number of cells), a feature closely associated with blastocyst quality. The classifiers showed different accuracy levels for each grade. They presented accuracy ranges from 0.67 to 0.92 for the embryo development classification, 0.67–0.82 for the ICM classification and 0.53–0.92 for the TE classification. The value 0.92 was the highest accuracy achieved in the tests with 73 blastocysts. CONCLUSIONS Semi-automatic grading of human blastocysts by a computer is feasible and may offer a more precise comparison of embryos, reducing subjectivity and allowing embryos with apparently identical morphological scores to be distinguished. [ABSTRACT FROM AUTHOR]

Abstract: Fractal theory provides a powerful mathematical tool for texture segmentation. However, in spite of their increasing popularity, traditional fractal features are intrinsically of less accuracy due to the difference between the idea fractal model and the fractal reality of digital images. In this paper, we incorporated the multifractal analysis method into the idea of fractal signature, and thus proposed a novel type of texture descriptor called multifractal signature, which characterizes the variation of multifractal dimensions over spatial scales. In our approach, the local multifractal dimension of each scale was calculated by using the measurement acquired at two successive scales so that the time-consuming and less accurate least square fit was avoided. Based on three popular multifractal measurements, the differential box-counting (DBC) based multifractal signature, relative DBC based multifractal signature, and morphological multifractal signature were presented in this paper. The performance of the proposed texture descriptors was evaluated for segmentation of texture mosaics by comparing to the corresponding multifractal dimensions. The experimental results demonstrated that multifractal signatures can differentiate textured images more effectively and provide more robust segmentations. [Copyright &y& Elsevier]

A superpixel segmentation algorithm called multi-density peaks clustering (MDPC) is proposed. By selecting a sufficient number of local density maximum pixels from the image as cluster centers to depict the image texture, the boundary of the object can be captured very accurately. The algorithm framework of MDPC is divided into three steps. First, the local density of pixel is defined, and the local density maximum pixels are calculated. Then, the local density maximum pixels are used as cluster centers, and the global optimal search, which is based on the path-to-point idea, is used to complete the clustering of the remaining non-cluster center pixels to realize the initial segmentation. Finally, superpixels are obtained by merging the initial segments according to the size of the segments and the distance between adjacent segments. In quantitative comparisons, MDPC is compared with 13 state-of-the-art superpixel segmentation algorithms in three image segmentation datasets. The experimental results show that MDPC achieves better performance in terms of boundary recall, boundary precision, achievable segmentation accuracy, undersegmentation error, and explained variation. And the qualitative comparisons show that the proposed algorithm has obvious advantages over other superpixel segmentation algorithms in image detail description and boundary adherence. Finally, the practicability and stability of MDPC are further demonstrated by the application of image segmentation. The source code of MDPC will be available at https://github.com/zhaojianaaa. [ABSTRACT FROM AUTHOR]

This letter describes an approach to perceptual segmentation of images through the means of clustering of spatial patterns. An image is modeled as a set of spatial patterns defined on a rectangular lattice. The distance between a spatial pattern and each cluster is defined as a combination of the Euclidean distance in the feature space and the spatial dissimilarity which reflects how much of the pattern’s neighbourhood is occupied by other clusters. Our approach has been compared with the Fuzzy C-Mean (FCM) algorithm, a spatial fuzzy clustering algorithm and a Markov Random Field (MRF) based algorithm by segmenting synthetic images, texture mosaics and natural images. The results of those comparative experiments demonstrate that the proposed approach can segment images more effectively and provide more robust segmentation results. [Copyright &y& Elsevier]

In this work, an improved kernel fuzzy c-means (KFCM) algorithm was developed for food image segmentation. Besides the three color components of RGB (red, green, and blue) space, three image texture features were first extracted to represent the spatial contextual information of each image pixel, including polarity, anisotropy, and local contrast. After that, the kernel trick was integrated into the conventional fuzzy c-means (FCM) algorithm to transform the feature vectors to a higher-dimensional space so that the vectors could be linearly separated within this space. Since the original KFCM method was computationally infeasible for real-time implementation, a new scheme was developed to reduce its time and memory complexity by reorganizing the calculations of kernel, membership, and cluster centroid matrices and wiping out the storage of the membership matrix. The experimental results indicate that the algorithm has attractive strengths in generality and computational efficiency. [ABSTRACT FROM AUTHOR]

One of the most challenging problems in automatic target detection is associated with the large variability of background clutter and object appearance. In this paper, we propose an anomaly detection approach which does not rely on an exhaustive statistical model of the targets, but rather on the local statistics of the data and possibly on some a priori information regarding the sizes and shapes of targets. Iterative procedures of feature extraction and anomaly detection are carried out, gradually reducing the false alarm rate while maintaining a high probability of detection. The background is characterized in a feature space of principal components, and a single hypothesis scheme is used for the detection of anomalous pixels. Morphological operators are subsequently employed for extracting the sizes and shapes of anomalous clusters in the image domain, and identifying potential targets. The robustness of the proposed approach is demonstrated with application to sea-mine detection in sonar imagery. [Copyright &y& Elsevier]

The segmentation of images into meaningful and homogenous regions is a key method for image analysis within applications such as content based retrieval. The watershed transform is a well established tool for the segmentation of images. However, watershed segmentation is often not effective for textured image regions that are perceptually homogeneous. In order to properly segment such regions the concept of the "texture gradient" is now introduced. Texture information and its gradient are extracted using a novel nondecimated form of a complex wavelet transform. A novel marker location algorithm is subsequently used to locate significant homogeneous textured or non textured regions. A marker driven watershed transform is then used to properly segment the identified regions. The combined algorithm produces effective texture and intensity based segmentation for the application to content based image retrieval. [ABSTRACT FROM AUTHOR]

To effectively detect the surface cracks of subway tunnels, an automatic tunnel crack detection system based on machine vision is presented. Aiming at the problems of environmental complexity and low contrast in subway tunnels, the image texture feature is first enhanced by the methods of frequency domain filtering and spatial differencing. Then, depending on the characteristics of the tunnel cracks in question, the crack propagation method is used to extract the complete cracks. Finally, broken cracks are connected during processing, and the method of combining projection and threshold is used to determine the crack types. At the same time, characteristics such as the length, width, and area of the cracks are obtained. The experimental results show that the presented methods can effectively extract complete cracks in complex tunnel environments. The identification error of tunnel crack parameters meets the actual engineering requirements. [ABSTRACT FROM AUTHOR]

Pattern regeneration is one of the applications of reverse engineering technology in the textile field, which realises the process of textile‐pattern regeneration‐textile, and fundamentally provides an intelligent design means of textile. At present, the method of pattern regeneration in the jacquard fabric is to use the image segmentation algorithm to segment the image digitalised by unidirectional imaging, and then the segmented pattern could be identified to regeneration for the design of new fabrics. However, due to the concave and convex pattern textures on the surface of jacquard fabric, the traditional unidirectional imaging method cannot be used for the full characterisation of its structural information, resulting in unsatisfactory pattern segmentation effect. To solve this problem, a novel segmentation algorithm for jacquard patterns based on multi‐view image fusion was proposed in this study. Based on multi‐view image acquisition and fusion, the pattern image of jacquard fabric could be cluster‐segmented by extracting the complete texture information of the fused image and the actual colour information of the calibrated image. Compared with the traditional unidirectional imaging method, the experimental results show that the enhanced texture information of the fused image is more workable for the pattern segmentation, it validates the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Computer‐aided diagnosis (CAD) is a common tool for the detection of diseases, particularly different types of cancers, based on medical images. Digital image processing thus plays a significant role in the processing and analysis of medical images for diseases identification and detection purposes. In this study, an efficient CAD system for the acute lymphoblastic leukaemia (ALL) detection is proposed. The proposed approach entails two phases. In the first phase, the white blood cells (WBCs) are segmented from the microscopic blood image. The second phase involves extracting important features, such as shape and texture features from the segmented cells. Eventually, on the extracted features, Naïve Bayes and k‐nearest neighbour classifier techniques are implemented to identify the segmented cells into normal and abnormal cells. The performance of the proposed approach has been assessed through comprehensive experiments carried out on the well‐known ALL‐IDB data set of microscopic blood images. The experimental results demonstrate the superior performance of the proposed approach over the state‐of‐the‐art in terms of accuracy rate in which achieved 98.7%. [ABSTRACT FROM AUTHOR]

This paper introduces a new HEp‐2 cell classification model under two phases. In the initial phase, the input image is segmented using the morphological operations (opening and closing), and the segmented image is given for Convolutional Neural Network (CNN) classifier that gives the classified output. In the second phase, the given input is processed under (i) segmentation process (ii) Feature Extraction, and (iii) Classification. From the segmented images; the features like Gray level co‐occurrence Matrix TGLCM) and Gray level Run Length Matrix (GLRM) are extracted. After extracting the features, they are subjected to a classification process, where Neural Network (NN) is used. Finally, the mean of both classified output (first phase and second phase) is considered to be the final classified output. As the main contribution, to enhance the classification accuracy, the hidden neurons of both classifiers (CNN and NN) are optimally chosen during the classification process. To make this possible, this paper aims to propose a new Randomized Update based Grey Wolf Optimization (RP‐GWO) algorithm. Finally, the performance of the implemented approach is compared over other conventional approaches and its superiority is proven with respect to certain measures. [ABSTRACT FROM AUTHOR]

Since histopathological images exist in various forms, performing segmentation on these images is tedious. While in cancer‐free colon tissue, epithelial cells generally have an elliptical shape; their structure alters in a malignant tissue. This study proposes a technique consisting of colon biopsy image segmentation and a hybrid set of features for classification, and is evaluated on multiple databases with various levels of magnifications. This study presents a novel image segmentation method with multi‐level thresholding based on Rényi's two‐dimensional entropy with a cultural algorithm (2DReCA). Based on the entropy, elliptical epithelial cells, being the region of interest, are identified from the segmented background. After successful segmentation, shape descriptors are extracted with morphological operations. Two sets of texture features (grey‐level co‐occurrence matrix and block‐wise elliptical local binary pattern) are calculated based on pre‐processed grey‐scale colon images. The proposed hybrid feature vector set, then concatenates the extracted features for training and testing with a random forest classifier. The proposed segmentation and classification model is evaluated by considering four data sets consisting of various colon images at different magnifications. In addition, it is evaluated by multiple performance measures and compared with existing techniques. [ABSTRACT FROM AUTHOR]

This paper introduces a new HEp-2 cell classification model under two phases. In the initial phase, the input image is segmented using the morphological operations (opening and closing), and the segmented image is given for Convolutional Neural Network (CNN) classifier that gives the classified output. In the second phase, the given input is processed under (i) segmentation process (ii) Feature Extraction, and (iii) Classification. From the segmented images; the features like Gray level co-occurrence Matrix TGLCM) and Gray level Run Length Matrix (GLRM) are extracted. After extracting the features, they are subjected to a classification process, where Neural Network (NN) is used. Finally, the mean of both classified output (first phase and second phase) is considered to be the final classified output. As the main contribution, to enhance the classification accuracy, the hidden neurons of both classifiers (CNN and NN) are optimally chosen during the classification process. To make this possible, this paper aims to propose a new Randomized Update based Grey Wolf Optimization (RP-GWO) algorithm. Finally, the performance of the implemented approach is compared over other conventional approaches and its superiority is proven with respect to certain measures. [ABSTRACT FROM AUTHOR]

Since histopathological images exist in various forms, performing segmentation on these images is tedious. While in cancer-free colon tissue, epithelial cells generally have an elliptical shape; their structure alters in a malignant tissue. This study proposes a technique consisting of colon biopsy image segmentation and a hybrid set of features for classification, and is evaluated on multiple databases with various levels of magnifications. This study presents a novel image segmentation method with multi-level thresholding based on Rényi's two-dimensional entropy with a cultural algorithm (2DR $_e$e CA). Based on the entropy, elliptical epithelial cells, being the region of interest, are identified from the segmented background. After successful segmentation, shape descriptors are extracted with morphological operations. Two sets of texture features (grey-level co-occurrence matrix and block-wise elliptical local binary pattern) are calculated based on pre-processed grey-scale colon images. The proposed hybrid feature vector set, then concatenates the extracted features for training and testing with a random forest classifier. The proposed segmentation and classification model is evaluated by considering four data sets consisting of various colon images at different magnifications. In addition, it is evaluated by multiple performance measures and compared with existing techniques. [ABSTRACT FROM AUTHOR]

A challenging aspect of instrument segmentation in robotic surgery is to distinguish different parts of the same instrument. Parts with similar textures are common in a practical instrument and are difficult to distinguish. In this work, the authors introduce an end‐to‐end recurrent model that comprises a multiscale semantic segmentation network and a refinement model. Specifically, the semantic segmentation network uniformly transforms the input images in multiple scales into a semantic mask, and the refinement model is a single‐scale net recurrently optimising the above semantic mask. Through extensive experiments, the authors validate that the models with multiscale inputs perform better than those to fuse encoded feature maps and ones with spatial attention. Furthermore, the authors verify the effectiveness of the proposed model with state‐of‐the‐art performances on several robotic instrument datasets derived from MICCAI Endoscopic Vision Challenges. [ABSTRACT FROM AUTHOR]

Boundaries play a crucial role in various image‐based tasks, but many existing non‐learning‐based boundary detection methods underperform in recognising authentic boundaries from a complex background. In this study, the authors address this problem using the sparseness‐constrained colour‐opponent response and the superpixel contrast. First, building on the biologically inspired colour‐opponency mechanism, the authors elaborate a method to compute the unbiased sparseness‐constrained colour‐opponent response. In this procedure, locations showing colour variations are enhanced, while the textural locations are preliminarily suppressed by the cue of local sparseness measure. Second, with the help of superpixel segmentation, the authors present an effective approach to obtain the superpixel contrast map. This approach helps to exploit the object shape information in suppressing textures. Consequently, the authors propose a non‐learning‐based method to detect boundaries in images, combining the unbiased sparseness‐constrained colour‐opponent response and the overall superpixel contrast map. Experiment results on widely adopted datasets manifest that the authors method outperforms most of the competing methods. In particular, compared with the state‐of‐the‐art surround‐modulation method, the proposed method obtains a comparable performance while consuming much less runtime. [ABSTRACT FROM AUTHOR]