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This paper uses partial differential equation image processing techniques to establish image texture analysis models based on nonlinear anisotropic diffusion equations for image denoising, image segmentation, and image decomposition. This paper proposes a class of denoising models based on the hybrid anisotropic diffusion equation from the characteristics of different noise types. The model exhibits anisotropic diffusion near the image boundary, which can protect the boundary well, and isotropic diffusion inside the image; so, it can remove the noise effectively. We use the immovable point theory to prove the uniqueness of the model solution and further discuss other properties such as asymptotics of the solution. We propose a class of image texture analysis algorithms based on anisotropic diffusion equations and discrete gray level sets. First, a class of nonconvex generalized functions is proposed to remove the noise from the original image to obtain a smooth image while sharpening the edges. Then, an energy generalization function based on the gray level set is proposed, and the existence of the global minimum of this energy generalization function is discussed. Finally, an equivalent form of this energy generalization is given in the discrete case, and an image texture analysis algorithm is designed based on the equivalent form. The algorithm is improved by initial position optimization, dynamic adjustment of parameters, and adaptive selection of thresholds so that the ants can search along the real edges. Experiments show that the improved algorithm for image edge detection can obtain more complete edges and better detection results. The energy generalization function is calculated directly on the discrete gray level set instead of solving the corresponding partial differential equation, which can avoid the selection of the initial level set and the reinitialization of the level set, thus greatly improving the segmentation efficiency. The new algorithm has a high improvement in segmentation efficiency and can efficiently handle large size complex images.

Color constancy is the capability to observe the true color of a scene from its image regardless of the scene's illuminant. It is a significant part of the digital image processing pipeline and is utilized when the true color of an object is required. Most existing color constancy methods assume a uniform illuminant across the whole scene of the image, which is not always the case. Hence, their performances are influenced by the presence of multiple light sources. This paper presents a color constancy adjustment technique that uses the texture of the image pixels to select pixels with sufficient color variation to be used for image color correction. The proposed technique applies a histogram-based algorithm to determine the appropriate number of segments to efficiently split the image into its key color variation areas. The K-means++ algorithm is then used to divide the input image into the pre-determined number of segments. The proposed algorithm identifies pixels with sufficient color variation in each segment using the entropies of the pixels, which represent the segment's texture. Then, the algorithm calculates the initial color constancy adjustment factors for each segment by applying an existing statistics-based color constancy algorithm on the selected pixels. Finally, the proposed method computes color adjustment factors per pixel within the image by fusing the initial color adjustment factors of all segments, which are regulated by the Euclidian distances of each pixel from the centers of gravity of the segments. The experimental results on benchmark single- and multiple-illuminant image datasets show that the images that are obtained using the proposed algorithm have significantly higher subjective and very competitive objective qualities compared to those that are obtained with the state-of-the-art techniques.

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

Contexto: Las carreteras públicas son esenciales para el progreso económico de cualquier país, ya que son fundamentales para el incremento en la eficiencia del transporte de bienes, y son una excelente fuente de empleo. Sin embargo, Colombia tiene pocas estadísticas sobre la condición de sus carreteras. e acuerdo con Invías, el estado de las vías pavimentadas colombianas puede resumirse así: 21,1 % son clasificadas como “Muy buenas”; 34,7 %, como “Buenas”, y 43,46 %, como “Regulares” o “Malas”. Entonces, desde el punto de vista de rehabilitación de pavimentos, vale la pena asegurar la calidad de aquellas carreteras clasificadas como “Regulares” o “Malas”.Objetivo: En este trabajo se propone un sistema para estimar el espesor de la capa de pavimento usando métodos de segmentación de texturas en imágenes. Actualmente, el espesor del pavimento es estimado usando radares de penetración terrestre, extracción de núcleos o realizando apiques; además, éste forma parte de los parámetros estructurales de los sistemas de evaluación de pavimentos.Método: El sistema propuesto está compuesto por una unidad de control de movimiento vertical, la cual introduce un videoscopio en un pequeño agujero en el pavimento, entonces las imágenes son obtenidas y unificadas en un computador portátil. Finalmente este mosaico es procesado a través de operadores de textura para estimar el espesor del pavimento. Los usuarios pueden seleccionar entre el método de Otsu y los filtros de Gabor para procesar los datos de imagen.Resultado: Los resultados reportados en este trabajo incluyen pruebas en laboratorio y en campo; estas pruebas muestran errores de 5,03 % y 11,3 %, respectivamente, en la medida del espesor del pavimento.Conclusión: El sistema propuesto es una opción atractiva para la estimación local del espesor del pavimento, con mínimos daños estructurales y menor impacto en la movilidad y la cantidad de operarios.

A new method of image texture analysis is presented, based on the mean and standard deviation of gray levels within domains in an image. The calculations are performed recursively on domains of various sizes within the images. These gray level calculations are used as the input matrix for principal component analysis. The technique analyzes the entire image as a whole and is not for image segmentation. The analysis routine operates across all distances, frequencies and directions in the image, and is not computationally burdensome. The method was applied to scanning electron microscope images of reverse osmosis membranes on domains from 23 nm to 3 µm. The texture analysis technique performed well in identifying the surface morphology and, once calibrated, in predicting the surface roughness as measured by atomic force microscopy.

Optimal texture feature extraction in multi-class texture classification is a challenging task. The choice of traditional texture features for texture classification and segmentation is subjective and highly application dependent with lower generalization to other textures. The Deep Neural Network (DNN) approach can overcome the problem of selecting a suitable and optimal subset of texture feature extraction method. The hidden layers inside the DNN architecture automatically extract suitable features without user intervention to give the best texture discriminating performances in multi-class texture classification environment. Our results on publicly available texture datasets show that deep learning techniques could be successfully used in image texture classification and segmentation.

The widespread penetration of counterfeit integrated circuits (ICs) is not only a major threat to the electronic goods supply chain, but also constitute a great threat to national security. Image processing based counterfeit IC design techniques are promising, but currently often suffer from high computational complexity and requirement of expensive image acquisition infrastructure. We describe two techniques based on image texture analysis to automate the process of counterfeit IC detection. The first method employs local textural feature identification to detect counterfeit ICs. The second method includes identification of counterfeit ICs by segmenting the image into regions of different textural features using texture filters. The first method is of lower computational complexity compared to the segmentation method, but the second method is capable of blind identification in the sense that it does not require knowledge of the textural features of a golden IC sample. Our experimental results show that these methods have high detection accuracy, even for images acquired using ordinary digital cameras and low-end digital microscopes.

We present an algorithm for risk prediction of road surface grip where skidding and sliding occur as main road surface problems. Prediction is done by defining a fine texture classification of the properties of road aggregate. In an experimental setup, data acquisition is performed with a supervised mobile vehicle scanning system, using a vehicle equipped with a camera and temperature sensor during movement along an arterial road. Image processing is performed by testing four texture feature extraction methods: Gabor filters, wavelet transform, gray level co-occurence matrix, and edge histogram descriptor, among which the Gabor transform shows the best results. The extraction of texture feature vectors follows by statistical algorithms for measuring feature vector similarity and reference vector selection, leading to image texture classification. The algorithm itself is upgraded by incorporating simultaneous surface temperature measurements in order to create and validate the final fine surface texture classification. The roads are classified and segmented into high-, medium-, and low-risk roads according to skid danger, enabling the creation of a map of high-risk zones. We validate our risk prediction algorithm by referring to crash rate data from the Road Traffic Safety Agency of Serbia database. This algorithm enables the location and mapping of high-risk zones and can be used as a support for autonomous driving and navigation.

International audience; The present study involves an investigation of a pointwise approach on the feature covariance matrix to extract textu-ral features for very high resolution (VHR) satellite images. Indeed, our proposition is to construct the covariance matrix of oriented gradients using a non-dense approach based on characteristic points extracted from the image. This novel non-dense covariance descriptor is capable of not only capturing both radiometric and local geometric information from the image, but also encoding their joint distribution and correlation, which are effectively relevant for texture characterization and discrimination. In order to demonstrate the efficiency of the proposed descriptor, a texture-based image segmentation stage is carried out. First efforts on VHR panchromatic images using the proposed algorithm provide very promising and competitive results compared to classical methods.

Multifractals have proven to be a valuable tool in image analysis applications and remote sensing (RS). They can be used for image segmentation, texture analysis, and classification. This study explores their applicability to change detection. We present a new approach to image texture description based on the calculation of a spatialized Holder exponent on multispectral satellite data. Holder exponents are evaluated in the context of an unsupervised change detection algorithm, and the classification of change in various land cover classes. Obtained results show that the sensitivity of proposed methods varies from 69% up to 93% with a considerably low level of false alarms.

The brain tumor is a cancerous disease due to the growth of abnormal cells in the human brain. It causes the death of many precious lives because of inaccurate calculation and identification of brain tumor. The average annual mortality rate of brain tumors in the United States between 2010 to 2014 was 4.33%, and almost 10,190 men and 7,830 women died this year from a brain tumor and the average survival rate in 5 years brain tumor is 36%. Much research has been done in the biomedical image processing field using computing concepts to segment and classifies brain tumors accurately. However, the diverse image content, occlusion, noisy image, chaotic object, nonuniform image texture, and other factors badly affect the performance of image clustering and segmentation algorithms. Therefore it is required to model an automatic image segmentation and classification algorithm. This research aims to segment brain tumors from MRI images using threshold segmentation and watershed algorithm and then classify brain tumors on features extracted (MSER, FAST, Harlick, etc.) through different classifiers. The proposed methodology includes image acquisition, image pre-processing, image segmentation, and feature extraction. Different classifiers are used to classify brain tumors from the datasets used accurately. The results indicate that the proposed mechanism enhances the detection of brain tumor images than the existing techniques by achieving more than 90% accuracy.

Previous approaches to texture analysis and segmentation use multichannel filtering by applying a set of filters in the frequency domain or a set of masks in the spatial domain. This paper presents two new texture segmentation algorithms based on multichannel filtering in conjunction with neural networks for feature extraction and segmentation. The features extracted by Gabor filters have been applied for image segmentation and analysis. Suitable choices of filter parameters and filter bank coverage in the frequency domain to optimize the filters are discussed. Here we introduce two methods to optimize Gabor filter bank. First, a Gabor filter bank with a flat response is implemented and the optimal feature dimension is extracted by competitive networks. Second, a subset of Gabor filter bank is selected to compose the best discriminative filters, so that each filter in this small set can discriminate a pair of textures in a given image. In both approaches, multilayer perceptrons are employed to segment the extracted features. The comparisons of segmentation results generated using the proposed methods and previous research using Gabor, discrete cosine transform (DCT), and Laws filters are presented. Finally, the segmentation results generated by applying the optimized filter banks to textured images are presented and discussed.

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.

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.

The traditional binocular vision stereo matching replaces and responds to the maximum point as the feature point, which is prone to uneven distribution of feature points, especially when the distribution of image texture strength is large. The Euclidean distance needs to be calculated when the feature points are matched, and the constraints considered are basically position relationships, ignoring the color block constraints. Based on the image texture and color distribution characteristics, the texture strength area and RGB color block are respectively calculated, and the image is segmented. The multi threshold feature points detection is used, and the matching points are calculated according to the color segmentation template. The experimental results show that compared with the traditional ORB algorithm, the proposed algorithm improves the uniformity of feature points and the matching accuracy.

Aiming at the problem that the image texture is complex, the grayscale feature is very close to the wharf, and the accurate segmentation is very difficult in the process of port ship detection in high-resolution optical remote sensing image, a port ship detection method combining visual significance model and center dark channel priori is proposed. The watershed segmentation algorithm based on spectral intensity and image texture information achieves land and sea segmentation. The land area is masked to suppress the interference of complex land background. Extracting fitting line and smoothing port contour line. According to the characteristics of the straight shape of the port and the straight shape of both sides with the ship, the center line point of the line is defined to locate the port, and the set of candidate areas of interest to the port is obtained. Combined with the visual model and the dark channel prior feature, the ship initial significance map and the central dark channel map were fused to complete the ship target detection. Finally, through the bow characteristics of the ship, the adjacent ship is separated to eliminate false alarm and improve the detection accuracy. Experiments show that the algorithm is robust, has a good detection effect, and its accuracy and recall rate are greatly improved.

We would like to thank EPSRC impact acceleration fund (EP/K503940/1) for helping support this project. RR was supported as part of the James-Watt Scholarship during her PhD research at the Heriot-Watt University. Co-locating the gross tumour volume (GTV) on cone-beam computed tomography (CBCT) of non small cell lung cancer (NSCLC) patients receiving radiotherapy (RT) is difficult because of the lack of image contrast between the tumour and surrounding tissue. This paper presents a new image analysis approach, based on second-order statistics obtained from gray level co-occurrence matrices (GLCM) combined with level sets, for assisting clinicians in identifying the GTV on CBCT images. To demonstrate the potential of the approach planning CT images from 50 NSCLC patients were rigidly registered with CBCT images from fractions 1 and 10. Image texture analysis was combined with two level set methodologies and used to automatically identify the GTV on the registered CBCT images. The Dice correlation coefficients (μ± σ) calculated between the clinician-defined and image analysis defined GTV on the planning CT and the CBCT for three different parameterisations of the model were: 0.69 ± 0.19, 0.63 ± 0.17, 0.86 ± 0.13 on fraction 1 CBCT images and 0.70 ± 0.17, 0.62 ± 0.15, 0.86 ± 0.12 on fraction 10 CBCT images. This preliminary data suggests that the image analysis approach presented may have potential for clinicians in identifying the GTV in low contrast CBCT images of NSCLC patients. Additional validation and further work, particularly in overcoming the lack of gold standard reference images, are required to progress this approach. Postprint

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 Renyi's two-dimensional entropy with a cultural algorithm (2DR 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.

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.

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.

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, Naive 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%.

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.

In the past few years, multifractal detrended fluctuation analysis (MF-DFA) method has been widely applied in the field of agricultural image processing. However, the agricultural image feature MF-DFA analyses involves a great deal of iterative processes and complex matrix operations, which require massive computation and processing time. In order to reduce processing time and improve analysis efficiency, we first develop a MF-DFA program that involves image preprocessing, image segmentation, local area accumulation matrix calculation, local area trend fitting, local area trend elimination, a global qth-order fluctuation function, and the Hurst index. Then, we analyze and compare MF-DFA each modules’ performance characteristics and explore its parallelism according to various segmentation scales s. Lastly, we propose a parallel optimization scheme based on OpenMP for the MF-DFA. The results of our rigorous performance evaluation clearly demonstrate that our proposed parallel optimization scheme can efficiently use multicore capability to extract rape leaf image texture characteristics.

This paper presents a novel set of image texture features generalizing standard grey-level co-occurrence matrices (GLCM) to multimodal image data through joint intensity matrices (JIMs). These are used to predict the survival of glioblastoma multiforme (GBM) patients from multimodal MRI data. The scans of 73 GBM patients from the Cancer Imaging Archive are used in our study. Necrosis, active tumor, and edema/invasion subregions of GBM phenotypes are segmented using the coregistration of contrast-enhanced T1-weighted (CE-T1) images and its corresponding fluid-attenuated inversion recovery (FLAIR) images. Texture features are then computed from the JIM of these GBM subregions and a random forest model is employed to classify patients into short or long survival groups. Our survival analysis identified JIM features in necrotic (e.g., entropy and inverse-variance) and edema (e.g., entropy and contrast) subregions that are moderately correlated with survival time (i.e., Spearman rank correlation of 0.35). Moreover, nine features were found to be associated with GBM survival with a Hazard-ratio range of 0.38-2.1 and a significance level of p0.05 following Holm-Bonferroni correction. These features also led to the highest accuracy in a univariate analysis for predicting the survival group of patients, with AUC values in the range of 68-70%. Considering multiple features for this task, JIM features led to significantly higher AUC values than those based on standard GLCMs and gene expression. Furthermore, an AUC of 77.56% with p = 0.003 was achieved when combining JIM, GLCM, and gene expression features into a single radiogenomic signature. In summary, our study demonstrated the usefulness of modeling the joint intensity characteristics of CE-T1 and FLAIR images for predicting the prognosis of patients with GBM.

With the development of image processing technology and the improvement of unmanned aerial vehicle (UAV) manufacture, it has been becoming a hot research topic to inspect the transmission line's failure. Here, a method of locating the area of insulator strings in the aerial image captured by UAV system is been introduced. At first, the aerial image is converted to HSV colour space model, and the components of H and S are filtered to segment the insulator strings and background areas. Considering some background areas have the similar colour with the insulator string, the result of segmentation will contain some background areas. Then, the image of result is divided into small pieces with non-overlapping windows. The information of texture on each window is used to remove the remaining background area. The outline of the insulator strings in the image can be obtained. It is easily to detect the edge of the insulator string and locate the area of the insulator string using the outline image. At last, some insulator string images have been used to illustrate the processing of the method. Compared with the method using RGB colour space model, the results of method here is effective and reliable.

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.

The use of computer vision technology to analyse the characteristics of smoke such as Ringelmann blackness coefficient and colour information can directly and efficiently reflect the situation of smoke emissions in industrial production, which has great significance in improving air quality. As many factors stand in the way, including the amount and speed of industrial smoke emissions, natural wind speed, illumination etc., an accurate and complete detection of the targeted smoke in images becomes a difficult issue in this field. In this study, a local binary pattern Silhouettes coefficient variant (LBPSCV) is proposed to segment industrial smoke images. The variant of Silhouettes coefficient was used as the weight when calculating the local binary pattern (LBP) feature vector in the LBPSCV. The algorithm overcame the shortcoming that the texture information described by LBP lacks local contrast information, making the extracted texture features more easily to be distinguished between smoke and non-smoke images. Smoke emission monitoring videos with different characteristics have been used in experiments, such as smoke emission videos with low light, multiple chimney exhaust, multi-colour smoke etc. The results show that the proposed method has higher detection accuracy and a lower false-positive rate.

Mammography is a very useful tool to diagnose breast cancer in early stages when it is easier to treat. There are two types of evidence that radiologists look for in a mammogram, calcifications and the existence of masses. In this study, an intelligent computer-aided diagnosis system is proposed for the detection of masses in mammographic images regardless of their nature. The proposed method uses a combination of extended maxima transformations, having different threshold values, in order to find suitable internal and external markers for a marker-based watershed segmentation. After segmentation, a two-stage classifier is used to distinguish the masses better from the healthy breast tissue. A feature vector based mainly on contrast and texture features is calculated and two alternative approaches, a Bayesian classifier and a support vector machine (SVM) with Gaussian kernel function, are implemented for further reduction of the false positive areas. The system was evaluated using the data from two online databases. Specifically, 73 mammographic images from the new curated breast imaging subset of digital database for screening mammography (CBIS-DDSM) database and all the mammographic images that contain masses from the mini-mammographic image analysis society (MIAS) database were used. The overall sensitivity, in both datasets, was near 80% when the Bayesian classifier was used and above 85% when the SVM was applied.

Cloud extraction and classification from satellite imagery is important for many applications in remote sensing. Satellite images are segmented based on distance, intensity and texture of the images. The popular segmentation algorithms, k-means (KM) and fuzzy c-means (FCM) clustering algorithms, face some problems such as unknown number of groups, unknown initialization and dead centers. In this paper, an unsupervised pixel classification by the KM and FCM algorithms is improved and the selection of centroids is made automatic. The proposed improved k-means (IKM) and improved fuzzy c-means (IFCM) clustering algorithms segment the INSAT-3D satellite's thermal infrared image into low-level, middle-level, high-level clouds and non-cloudy region. As human beings can easily find the clouds in the satellite images, visible image is used to differentiate the clouds from the background. A threshold is found from the histogram of the visible image to separate the cloudy and non-cloudy pixels. The other three thresholds to divide the clouds into three types are found from the thermal infrared image's histogram. The segmentation results of IKM and IFCM algorithms are compared with the existing segmentation algorithms. The comparison shows that IFCM algorithm matches well with original image followed by IKM algorithm as compared with existing algorithms.