Texture-based Image Segmentation Research Articles

Design and Implementation of Shape and Texture Based Image Segmentation on Morphological Gradient Approach

Identifying and separating objects within an image is a significant challenge due to high object and background variability. This can be obtained through feature extraction approach. There are different ways of extracting the image features. It is based on texture, shape and colour. The present paper aims to study and analyse the various approaches for feature extraction and object recognition. This study proposed a hybrid approach, which is a combination of enhanced Fractal Texture Analysis with Layout Descriptor to overcome the obstacles in image segmentation. It is used to lessen the boundary complexity of the segmented image. First, the image is preprocessed to discard the noise and to retain the adequate details of the image in a perfect way through Adaptive Switching Median Filter. Secondly, it improves the power of the edges detected through a noise-protected edge detector. Finally, it is applied with morphological gradient technique that is a twin function of both shape and texture gradient removal for extorting the qualities of the image. In this way, the proposed methodology directly performs on the colour image which supports to enhance prediction accuracy of the object in terms of colour characteristics that offers better results than the grayscale conversion approach. Moreover, the shape feature is extracted from the preprocessed image depending on the details like compactness, rectangularity, eccentricity and moment invariants.

Machine learning-based multidomain processing for texture-based image segmentation and analysis

Atomic and molecular resolved atomic force microscopy (AFM) images offer unique insights into materials' properties such as local ordering, molecular orientation, and topological defects, which can be used to pinpoint physical and chemical interactions occurring at the surface. Utilizing machine learning for extracting underlying physical parameters increases the throughput of AFM data processing and eliminates inconsistencies intrinsic to manual image analysis, thus enabling the creation of reliable frameworks for qualitative and quantitative evaluation of experimental data. Here, we present a robust and scalable approach to the segmentation of AFM images based on flexible pre-selected classification criteria. The usage of supervised learning and feature extraction allows us to retain the consideration of specific problem-dependent features (such as types of periodical structures observed in the images and the associated numerical parameters: spacing, orientation, etc.). We highlight the applicability of this approach for the segmentation of molecular resolved AFM images based on the crystal orientation of the observed domains, automated selection of boundaries, and collection of relevant statistics. Overall, we outline a general strategy for machine learning-enabled analysis of nanoscale systems exhibiting periodic order that could be applied to any analytical imaging technique.

Automated AJCC (7th edition) staging of non-small cell lung cancer (NSCLC) using deep convolutional neural network (CNN) and recurrent neural network (RNN).

A largechunk of lung cancers are of the type non-small cell lung cancer (NSCLC). Both the treatment planning and patients' prognosis depend greatly on factors like AJCC staging which is an abstraction over TNM staging. Many significant efforts have so far been made towards automated staging of NSCLC, but the groundbreaking application of a deep neural networks (DNNs) is yet to be observed in this domain of study. DNN is capable of achieving higher level of accuracy than the traditional artificial neural networks (ANNs) as it uses deeper layers of convolutional neural network (CNN). The objective of the present study is to propose a simple yet fast CNN model combined with recurrent neural network (RNN) for automated AJCC staging of NSCLC and to compare the outcome with a few standard machine learning algorithms along with a few similar studies. The NSCLC radiogenomics collection from the cancer imaging archive (TCIA) dataset was considered for the study. The tumor images were refined and filtered by resizing, enhancing, de-noising, etc. The initial image processing phase was followed by texture based image segmentation. The segmented images were fed into a hybrid feature detection and extraction model which was comprised of two sequential phases: maximally stable extremal regions (MSER) and the speeded up robust features (SURF). After a prolonged experiment, the desired CNN-RNN model was derived and the extracted features were fed into the model. The proposed CNN-RNN model almost outperformed the other machine learning algorithms under consideration. The accuracy remained steadily higher than the other contemporary studies. The proposed CNN-RNN model performed commendably during the study. Further studies may be carried out to refine the model and develop an improved auxiliary decision support system for oncologists and radiologists.

SiMA: A simplified migration assay for analyzing neutrophil migration.

In lung inflammation, neutrophils are the first leukocytes migrating to an inflammatory site, eliminating pathogens by multiple mechanisms. The term "migration" describes several stages of neutrophil movement to reach the site of inflammation, of which the passage of the interstitium and basal membrane of the airway are necessary to reach the site of bronchial inflammation. Currently, several methods exist (e.g., Boyden Chamber, under-agarose assay, or microfluidic systems) to assess neutrophil mobility. However, these methods do not allow for parameterization on single cell level, that is, the individual neutrophil pathway analysis is still considered challenging. This study sought to develop a simplified yet flexible method to monitor and quantify neutrophil chemotaxis by utilizing commercially available tissue culture hardware, simple video microscopic equipment and highly standardized tracking. A chemotaxis 3D µ-slide (IBIDI) was used with different chemoattractants [interleukin-8 (IL-8), fMLP, and Leukotriene B4 (LTB4 )] to attract neutrophils in different matrices like Fibronectin (FN) or human placental matrix. Migration was recorded for 60 min using phase contrast microscopy with an EVOS® FL Cell Imaging System. The images were normalized and texture based image segmentation was used to generate neutrophil trajectories. Based on these spatio-temporal information a comprehensive parameter set is extracted from each time series describing the neutrophils motility, including velocity and directness and neutrophil chemotaxis. To characterize the latter one, a sector analysis was employed enabling the quantification of the neutrophils response to the chemoattractant. Using this hard- and software framework we were able to identify typical migration profiles of the chemoattractants IL-8, fMLP, and LTB4 , the effect of the matrices FN versus HEM as well as the response to different medications (Prednisolone). Additionally, a comparison of four asthmatic and three non-asthmatic patients gives a first hint to the capability of SiMA assay in the context of migration based diagnostics. Using SiMA we were able to identify typical migration profiles of the chemoattractants IL-8, fMLP, and LTB4 , the effect of the matrices FN versus HEM as well as the response to different medications, that is, Prednisolone induced a change of direction of migrating neutrophils in FN but no such effect was observed in human placental matrix. In addition, neutrophils of asthmatic individuals showed an increased proportion of cells migrating toward the vehicle. With the SiMA platform we presented a simplified but yet flexible platform for cost-effective tracking and quantification of neutrophil migration. The introduced method is based on a simple microscopic video stage, standardized, commercially available, µ-fluidic migration chambers and automated image analysis, and track validation software. © 2017 International Society for Advancement of Cytometry.

Enhancing textural differences using wavelet-based texture characteristics morphological component analysis: A preprocessing method for improving image segmentation

In a recent paper, a method called “texture characteristic based morphological component analysis” (TC-MCA) has been proposed to enhance the performances of texture-based image segmentation algorithms: (1) TC-MCA separates texture into multiple pairs of components each representing a different visual characteristic of the texture; (2) then the TC-MCA algorithm manipulates each component and recombines them to produce the enhanced image where textures are more different, so that the texture-based segmentation is improved. This paper proposes a novel method outperforming TC-MCA by: (1) applying dictionaries based on dual-tree complex wavelet transform (DTCWT) to decompose the image into components representing different visual characteristics of texture, and (2) manipulating the dual-tree complex wavelet coefficients of the texture components to enhance each texture component’s own property and producing an texture-difference-enhanced image by recombining the enhanced components. The wavelet-based texture characteristic morphological component analysis (WT-TC-MCA) is applied as the preprocessing step to some state-of-the-art texture-based segmentation algorithms and compared with other texture enhancing methods, including the TC-MCA, with respect to the accuracy, precision-recall curves of the segmentation results. Experiments demonstrate that our method produces better textural difference enhancement effects and improves texture-based segmentation more than the comparators.

Enhancement of textural differences based on morphological component analysis.

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.

Empirical Evaluation of Generic Weighted Cubicle Pattern and LBP Derivatives for Abnormality Detection in Mammogram Images

Digital image processing techniques are very useful in abnormality detection in digital mammogram images. Nowadays, texture-based image segmentation of digital mammogram images is very popular due to its better accuracy and precision. Local binary pattern (LBP) descriptor has attracted many researchers working in the field of texture analysis of digital images. Because of its success, many texture descriptors have been introduced as variants of LBP. In this work, we propose a novel texture descriptor called generic weighted cubicle pattern (GWCP) and we analyzed the proposed operator for texture image classification. We also performed abnormality detection through mammogram image segmentation using k-Nearest Neighbors (KNN) algorithm and compared the performance of the proposed texture descriptor with LBP and other variants of LBP namely local ternary pattern (LTPT), extended local texture pattern (ELTP) and local texture pattern (LTPS). For evaluation, we used the performance metrics such as accuracy, error rate, sensitivity, specificity, under estimation fraction and over estimation fraction. The results prove that the proposed method outperforms other descriptors in terms of abnormality detection in mammogram images.

A Novel Edge Detection Algorithm Based on Texture Feature Coding

AbstractA new edge detection technique based on the texture feature coding method (TFCM) is proposed. The TFCM is a texture analysis scheme that is generally used in texture-based image segmentation and classification applications. The TFCM transforms an input image into a texture feature image whose pixel values represent the texture information of the pixel in the original image. Then, on the basis of the transformed image, several features are calculated as texture descriptors. In this article, the TFCM is employed differently to construct an edge detector. In particular, the texture feature number (TFN) of the TFCM is considered. In other words, the TFN image is used for subsequent processes. After obtaining the TFN image, a simple thresholding scheme is employed for obtaining the coarse edge image. Finally, an edge-thinning procedure is used to obtain the tuned edges. We conducted several experiments on a variety of images and compared the results with the popular existing methods such as the Sobel, Prewitt, Canny, and Canny–Deriche edge detectors. The obtained results were evaluated quantitatively with the Figure of Merit criterion. The experimental results demonstrated that our proposed method improved the edge detection performance greatly. We further implemented the proposed edge detector with a hardware system. To this end, a field programmable gate array chip was used. The related simulations were carried out with the MATLAB Simulink tool. Both software and hardware implementations demonstrated the efficiency of the proposed edge detector.

Automated Colorization of Grayscale Images Using Texture Descriptors and a Modified Fuzzy C-Means Clustering

A novel example-based process for Automated Colorization of grayscale images using Texture Descriptors (ACTD) without any human intervention is proposed. By analyzing a set of sample color images, coherent regions of homogeneous textures are extracted. A multi-channel filtering technique is used for texture-based image segmentation, combined with a modified Fuzzy C-means (FCM) clustering algorithm. This modified FCM clustering algorithm includes both the local spatial information from neighboring pixels, and the spatial Euclidian distance to the cluster’s center of gravity. For each area of interest, state-of-the-art texture descriptors are then computed and stored, along with corresponding color information. These texture descriptors and the color information are used for colorization of a grayscale image with similar textures. Given a grayscale image to be colorized, the segmentation and feature extraction processes are repeated. The texture descriptors are used to perform Content-Based Image Retrieval (CBIR). The colorization process is performed by Chroma replacement. This research finds numerous applications, ranging from classic film restoration and enhancement, to adding valuable information into medical and satellite imaging. Also, this can be used to enhance the detection of objects from x-ray images at the airports.

Unsupervised texture-based image segmentation through pattern discovery

This paper presents a new efficient technique for unsupervised segmentation of textured images that aims at incorporating the advantages of supervision for discriminating texture patterns. First, a pattern discovery stage that relies on a clustering algorithm is utilized for determining the texture patterns of a given image based on the outcome of a multichannel Gabor filter bank. Then, a supervised pixel-based classifier trained with the feature vectors associated with those patterns is used to classify every image pixel into one of the sought texture classes, thus yielding the final segmentation. Multi-sized evaluation windows following a top-down approach are utilized during pixel classification in order to improve accuracy both inside and near boundaries of regions of homogeneous texture. Results with synthetic compositions and with complex real images are presented and discussed. The proposed technique is also compared with alternative texture segmentation approaches.

Dynamic Hybrid Algorithms for MAP Inference in Discrete MRFs

In this paper, we present novel techniques that improve the computational and memory efficiency of algorithms for solving multilabel energy functions arising from discrete mrfs or crfs. These methods are motivated by the observations that the performance of minimization algorithms depends on: 1) the initialization used for the primal and dual variables and 2) the number of primal variables involved in the energy function. Our first method (dynamic alpha-expansion) works by "recycling" results from previous problem instances. The second method simplifies the energy function by "reducing" the number of unknown variables present in the problem. Further, we show that it can also be used to generate a good initialization for the dynamic alpha-expansion algorithm by "reusing" dual variables. We test the performance of our methods on energy functions encountered in the problems of stereo matching and color and object-based segmentation. Experimental results show that our methods achieve a substantial improvement in the performance of alpha-expansion, as well as other popular algorithms such as sequential tree-reweighted message passing and max-product belief propagation. We also demonstrate the applicability of our schemes for certain higher order energy functions, such as the one described in [1], for interactive texture-based image and video segmentation. In most cases, we achieve a 10-15 times speed-up in the computation time. Our modified alpha-expansion algorithm provides similar performance to Fast-PD, but is conceptually much simpler. Both alpha-expansion and Fast-PD can be made orders of magnitude faster when used in conjunction with the "reduce" scheme proposed in this paper.

Variational Region-Based Segmentation Using Multiple Texture Statistics

This paper investigates variational region-level criterion for supervised and unsupervised texture-based image segmentation. The focus is given to the demonstration of the effectiveness and robustness of this region-based formulation compared to most common variational approaches. The main contributions of this global criterion are twofold. First, the proposed methods circumvent a major problem related to classical texture based segmentation approaches. Existing methods, even if they use different and various texture features, are mainly stated as the optimization of a criterion evaluating punctual pixel likelihoods or similarity measure computed within a local neighborhood. These approaches require sufficient dissimilarity between the considered texture features. An additional limitation is the choice of the neighborhood size and shape. These two parameters and especially the neighborhood size significantly influence the classification performances: the neighborhood must be large enough to capture texture structures and small enough to guarantee segmentation accuracy. These parameters are often set experimentally. These limitations are mitigated with the proposed variational methods stated at the region-level. It resorts to an energy criterion defined on image where regions are characterized by nonparametric distributions of their responses to a set of filters. In the supervised case, the segmentation algorithm consists in the minimization of a similarity measure between region-level statistics and texture prototypes and a boundary based functional that imposes smoothness and regularity on region boundaries. In the unsupervised case, the data-driven term involves the maximization of the dissimilarity between regions. The proposed similarity measure is generic and permits optimally fusing various types of texture features. It is defined as a weighted sum of Kullback-Leibler divergences between feature distributions. The optimization of the proposed variational criteria is carried out using a level-set formulation. The effectiveness and the robustness of this formulation at region-level, compared to classical active contour methods, are evaluated for various Brodatz and natural images.

Texture Based Segmentation using Statistical Properties for Mammographic Images

Segmentation is very basic and important step in computer vision and image processing. For medical images specifically accuracy is much more important than the computational complexity and thus time required by process. But as volume of data of patients goes on increasing then it becomes necessary to think about the processing time along with accuracy. Here in this paper, new algorithm is proposed for texture based segmentation using statistical properties. For that probability of each intensity value of image is calculated directly and image is formed by replacing intensity by its probability . Variance is calculated in three different ways to extract the texture features of the mammographic images. These results of proposed algorithm are compared with well known GLCM and Watershed algorithm. Segmenting mammographic images into homogeneous texture regions representing disparate tissue types is often a useful preprocessing step in the computer-assisted detection of breast cancer. With the increasing size and number of medical images, the use of computers in facilitating their processing and analysis has become necessary. Estimation of the volume of the whole organ, parts of the organ and/or objects within an organ i.e. tumors is clinically important in the analysis of medical image. The relative change in size, shape and the spatial relationships between anatomical structures obtained from intensity distributions provide important information in clinical diagnosis for monitoring disease progression. Therefore, radiologists are particularly interested to observe the size, shape and texture of the organs and/or parts of the organ. For this, organ and tissue morphometry performed in every radiological imaging centre. Texture based image segmentation is area of intense research activity in the past few years and many algorithms were published in consequence of all this effort, starting from simple thresholding method up to the most sophisticated random field type method. The repeating occurrence of homogeneous regions of images is texture. Texture image segmentation identifies image regions that have homogeneous with respect to a selected texture measure. Recent approaches to texture based segmentation are based on linear transforms and multiresolution feature extraction (1), Markov random filed models (2,3), Wavelets (4 - 6) and fractal dimension (7). Although unsupervised texture-based image segmentation is

Color- and Texture-Based Image Segmentation for Improved Forest Delineation

This paper concentrates on the delineation of forest boundaries from aerial images with focus on spatially contiguous and reproducible results for the Swiss National Forest Inventory. Because of the poor performance of common edge models to extract natural vegetation boundaries, this paper presents a combined method of image segmentation and wavelet-based texture features for the delineation of forest. The selected -measure-based segmentation method has been found to be useful to produce initial segmentation results, but lacks a semantic concept for forest vegetation. To overcome this conceptual limitation, the combination with wavelet transformation gives access to additional texture features and leads to a robust approach to obtain proper forest boundaries. Preliminary results are encouraging regarding the better agreement compared with maximum-likelihood classification results.

Texture-based image segmentation in medical volume images

Texture-based image segmentation in medical volume images

Spatial/spatial-frequency representations for image segmentation and grouping

In this paper, we consider the application of spatial/ spatial-frequency representations to texture-based image segmentation and to perceptual grouping, a related phenomenon. We discuss a system for segmentation and grouping, based on the pseudo-Wigner distribution (PWD) a discrete approximation to the WD, and show experimental results.

Development of features for texture based image segmentation using the reduced coulomb energy pattern recognition model: [formula omitted]. M.I.T. Lincoln Laboratory, P.O. Box 73, Lexington, MA 02173-0073 USA

Development of features for texture based image segmentation using the reduced coulomb energy pattern recognition model: [formula omitted]. M.I.T. Lincoln Laboratory, P.O. Box 73, Lexington, MA 02173-0073 USA