Segmentation of Visual Images by Sequential Extracting Homogeneous Texture Areas

Introduction. An algorithm for calculating the similarity degree between multidimensional histograms is presented. The proposed algorithm was intended for texture segmentation of images using histograms as texture features. The need to develop such a special algorithm is justified by the fact that the methods for estimating the similarity/difference measure between multidimensional vectors described in the literature provide such measures that are not very suitable for solving the texture segmentation task. The main peculiarity of the proposed algorithm is that when calculating the similarity value, it considers not only the corresponding histogram components, but also takes into account their nearest neighboring components. Due to this, the algorithm more adequately evaluates the similarity of histograms. The proposed algorithm was implemented as a computer program as an integral part of the image segmentation model. The effectiveness of the histogram comparison algorithm was indirectly confirmed by the results of texture segmentation of the image segmentation model in experiments on processing various images, including natural landscapes. Methods. The task of calculating the similarity between histograms is considered. A special algorithm is proposed because the analogical methods described in the literature are not very suitable for solving the texture segmentation task. The main peculiarity of the algorithm is that it takes into account as the corresponding histogram components as their nearest neighboring components. Due to this, the algorithm more adequately evaluates the similarity of histograms. The algorithm was implemented as a computer program. The effectiveness of the algorithm is indirectly confirmed by the results of texture segmentation of the image segmentation model in experiments on processing various images, including natural landscapes. Purpose. The goal of this work is to develop an efficient algorithm for assessing the similarity of histograms, such as brightness histograms and orientation histograms of the texture windows. The algorithm is based on the idea of taking into account not only the corresponding components of both histograms, but also the components of their immediate environment. Results. The main advantage of the proposed algorithm, compared to popular methods of calculating similarity/difference between objects (vectors), is that the range of similarity between the compared histograms (from complete similarity to complete difference) is 100%, while popular methods can offer several times smaller ranges of similarity percentage. Conclusion. The proposed algorithm provides a wide range of similarity between the compared histograms which is 100% (from complete similarity to complete difference), while popular methods can offer several times smaller ranges of similarity percentage. The algorithm was implemented as a computer program as a component of a model that solves the problem of segmenting a visual image into homogeneous texture areas. It is worth noting that the proposed histogram comparison algorithm calculates the similarity measure between histograms very quickly, since it uses only simple operations. The effectiveness of the algorithm for texture segmentation of images into homogeneous texture areas is confirmed by the results in the experiments on natural image processing. The results obtained in the experiments demonstrate the effectiveness of the algorithm and show that the algorithm performs correct (from a human point of view) texture segmentation of a wide range of images. Thus, the effectiveness of the key operation of the segmentation algorithm, the histogram comparison algorithm, is indirectly confirmed.

In the study of object recognition, image texture segmentation has being a hot and difficult aspect in computer vision. Feature extraction and texture segmentation algorithm are two key steps in texture segmentation. An effective texture description is the important factor of texture segmentation. In this paper, the neuronal activation degree (NAD) of visual model is exploited as the texture description of image patches. By processing the length and direction of NAD, we develop an effective segmentation strategy. First, the length of the NAD are used to partition blank area and non-blank area, then the mark index of neuron is used, which is maximally activated to identify the label of each segment unit to get an initial segmentation. Finally, region merging steps is exerted to get a desired result.

In order to improve the edge accuracy and the areas consistency, to reduce the partition error rate in textural image segmentation, we propose a new method which using multi-scale wavelet analysis based on feature learning in this paper. It improves the textural image segmentation by reducing the effect of redundant features on segmentation results. The method includes three stages as feature extraction, optimizing the feature vectors and feature space clustering. In the stage of filtrating valid features, we optimize the feature vectors by feature learning. The experimental results demonstrate that the improved algorithm is effective for textural image segmentation.

One of the more successful engineering applications of fractal geometry has been the utilization of fractal image models in medical image processing. These applications have included tissue characterization studies, textural image segmentation, and image restoration using fractal constraints. The class of fractal models used in medical image processing and the techniques used to estimate the fractal dimension associated with these models will be reviewed. An image segmentation algorithm that utilized a fractal textural feature and formulated the segmentation process as a configurational optimization problem is presented. The configurational optimization method allows information about both, the degree of correspondence between a candidate segment and an assumed textural model, and morphological information about the candidate segment to be used in the segmentation process. To apply this configurational optimization technique with a fractal textural model however, requires the estimation of the fractal dimension of an irregularly shaped candidate segment. The potential utility of a discrete Gerchberg-Papoulis bandlimited extrapolation algorithm to the estimation of the fractal dimension of an irregularly shaped candidate segment is also discussed.

In order to increase the edge accuracy and the areas consistency, and to reduce the partition error rate in textural image segmentation, we propose a new multi-scale wavelet analysis based on feature learning in this paper. It improves the textural image segmentation by reducing the effect of redundant features on segmentation results. This method includes three stages as follows: feature extraction, optimizing the feature vectors and feature space clustering. In the stage of filtrating valid features, we optimize the feature vectors by feature learning. The experimental results demonstrate that the improved algorithm is effective for textural image segmentation.

A new approach to colour-texture segmentation is presented which uses Local Binary Pattern data and a new colour quantisation scheme based on hue and saturation to provide evidence from which pixels can be classified into texture classes. The proposed algorithm, which we contend to be the first use of evidence gathering in the field of texture classification, uses Generalised Hough Transform style R-tables as unique descriptors for each texture class. Tests on remotely sensed images demonstrate the superiority of the colour-texture algorithm compared to the established JSEG algorithm; a notable advantage of the new approach is the absence of over-segmentation. The VisTex database is used to compare the colour-texture algorithm with alternative methods, including its grey-scale equivalent, for the segmentation of colour texture images; providing good results with smooth texture boundaries and low noise within texture segments. (6 pages)

The authors present a linear feature-reduction technique for multicomponent or textured image segmentation. The transformation matrix is computed by simultaneously diagonalizing scatter matrices evaluated at two different spatial resolutions. Under reasonable conditions, this transform closely approximates the generalized Fisher linear discriminants which are optimal for region separability. Experimental examples suggest that this technique is superior to the Karhunen-Loeve transform for texture segmentation. >

Image segmentation is an important step in several computer vision applications. The segmentation of images into homogeneous and meaningful regions is a fundamental technique for image analysis. Textures occupy a vital role in a wide range of computer vision research fields; from microscopic images to images sent down to earth by satellites, from the analysis of multi- spectral scan images to outdoor scenes, all consist of texture. Although several methods have been proposed, less work has been done in developing suitable techniques for segmentation of texture images. After a careful and in-depth survey on wavelet transforms, the present study found that efficient numerical solutions in the signal processing applications can be found using Stationary Wavelet Transform (SWT). SWT is redundant, linear and shift invariant, that's why it gives a better approximation than the DWT. In this paper a novel texture segmentation method based on ―SWT and Textural Properties‖ is proposed. Multi scale SWT with Textural Properties and morphological treatment is used in the present study to detect fine edges from texture images for a fine segmentation.

We present an approach for unsupervised segmentation of natural and textural images based on active contour, differential geometry and information theoretical concept. More precisely, we propose a new texture descriptor which intrinsically defines the geometry of textural regions using the shape operator borrowed from differential geometry. Then, we use the popular Kullback-Leibler distance to define an active contour model which distinguishes the background and textural objects of interest represented by the probability density functions of our new texture descriptor. We prove the existence of a solution to the proposed segmentation model. Finally, a fast and easy to implement texture segmentation algorithm is introduced to extract meaningful objects. We present promising synthetic and real-world results and compare our algorithm to other state-of-the-art techniques.

Ultrasound (US) imaging is one of the most cost-effective imaging modality that utilizes sound waves for generating medical images of anatomical structure. However, the presence of speckle noise and low contrast in the US images makes it difficult to use for proper classification of anatomical structures in clinical scenarios. Hence, it is important to devise a method that is robust and accurate even in the presence of speckle noise and is not affected by the low image contrast. In this work, a novel approach for thyroid texture characterization based on extracting features utilizing higher order spectral analysis (HOSA) was used. A Support Vector Machine (SVM) was applied on the extracted features to classify the thyroid texture. Since HOSA is a well suited technique for processing non-Gaussian data involving non-linear dynamics, good classification of thyroid texture can be obtained in US images as they also contain non-Gaussian Speckle noise and nonlinear characteristics. A final accuracy of 93.27%, sensitivity of 0.92 and specificity of 0.62 were obtained using the proposed approach.

An improved image segmentation algorithm based on support vector machines was proposed, which belonged to hybrid segmentation techniques. Considering image segmentation based on support vector machines required the user to provide the training data, an automatic data providing method was proposed to obtain training data used by support vector machines instead of directly taking some pieces of the image by user. In the improved algorithm, feature vectors of homogeneous region were firstly classified using unsupervised classification technique, and then feature vectors and class labels were fed into support vector machines for training and latter for predicting the labels of unknown samples once the training was complete. The experiments show that the proposed algorithm is efficient for both smooth image segmentation and texture image segmentation. Meanwhile, the classified model trained using one representative image can be applied to the set of similar images and 3D volume data.

7 - A survey on neutrosophic medical image segmentation

Perceptual segmentation of a boundary between two textures is conventionally thought to be based upon differences in their Fourier energy, i.e. in their low-order texture statistics. Most evidence supporting (or contradicting) this idea has arisen from studies using various synthetic texture patterns. But what role, if any, do higher-order texture statistics play in segmenting natural images? Here we extracted high resolution texture regions from monochrome photographs of natural scenes, rich in higher-order statistics. By phase-scrambling these textures, we could remove their high-order statistics, leaving mean luminance and RMS contrast unchanged. Using pairs of natural or phase-scrambled textures, we created RMS-balanced texture quilt boundaries in half-disc stimuli. We also created similar contrast boundaries from individual textures. Employing forced choice judgments of boundary orientation (left- vs. right-oblique), we measured modulation-depth thresholds for both contrast and texture boundaries. If only the low-order statistics are used, then phase-scrambling should have no effect on psychophysical performance. Boundaries between these texture pairs could usually be segregated (thresholds: 35–70%), though in some cases even 100% modulation-depth did not produce reliable performance. In most instances, phase-scrambling made the task impossible. However in a minority of scrambled texture pairs, thresholds were measurable and in some, performance was improved. Contrast boundaries yielded lower modulation-depth thresholds (10–30%) which were impervious to or improved by phase-scrambling, particularly at lower texture contrasts. These results suggest that higher-order texture statistics contribute importantly to boundary segmentation in natural scenes.

Unsupervised segmentation of noisy and textured images using Markov random fields

The authors introduce a region growing algorithm working in conjunction with the K-means algorithm for the segmentation of textured cell images. The iterative algorithm initially segments the most easily classifiable regions which are grown in subsequent iterations. Only the most easily classifiable pixels in the remaining regions are segmented in each iteration . Experimental results of the procedure in segmenting breast cancer cells are demonstrated and compared to the K-means algorithm.