Background: Accurate and early detection of non-alcoholic fatty liver, which is a major cause of chronic diseases is very important and is vital to prevent the complications associated with this disease. Ultrasound of the liver is the most common and widely performed method of diagnosing fatty liver. However, due to the low quality of ultrasound images, the need for an automatic and intelligent...

Features such as edges and corners play an important role in visual data processing. The proposed research employs features to facilitate image segmentation, texture synthesis and visual data approximation. Edges capture primary color/intensity changes in an image, which can be utilized to accelerate image segmentation. In example-based texture synthesis, edges and ridges/valleys can also help ...

In this paper, research carried out to test the wavelet and co-occurrence matrix based features for rotation invariant texture image retrieval using fuzzy logic classifier. Energy and Standard Deviation features of DWT coefficients up to fifth level of decomposition and eight features are extracted from co-occurrence matrix of whole image and each sub-band of first level DWT decomposition. The ...

This paper utilizes a spatial texture correlation and the intelligent classification algorithm (ICA) search strategy to speed up the encoding process and improve the bit rate for fractal image compression. Texture features is one of the most important properties for the representation of an image. Entropy and maximum entry from co-occurrence matrices are used for representing texture features i...

Texture boundaries or edges are useful information for segmenting heterogeneous textures into several classes. Texture edge detection is different from the conventional edge detection that is based on the pixel-wise changes of gray level intensities, because textures are formed by patterned placement of texture elements over some regions. We propose a prediction-based texture edge detection met...

Texture synthesis aims to define and reproduce discriminating image features. These features are used to associate with and differentiate between two textures. Often texture is composed of a pattern with an element of randomness in each feature’s appearance, position and orientation. The goal is to imitate the sample texture in such a way that sample and synthesised texture are perceived to be ...

In this paper, we present a neural approach for unsupervised natural color-texture image segmentation, which is based on both Kohonen maps and mathematical morphology, using a combination of the texture and the image color information of the image, namely, the fractal features based on fractal dimension are selected to present the information texture, and the color features presented in RGB col...

In this paper, we employ a new adaptive basis of functions — brushlets for extracting texture properties. Brushlets are functions which are well localized with only one peak in the frequency domain. Hence, a representation of texture in terms of spatial frequency distributions can be constructed. The Brushlet features are used in texture image retrieval experiments to assess its effectiveness b...

We have carried out a detailed evaluation of the use of texture features in a query-by-example approach to image retrieval. We used 3 radically different texture feature types motivated by i) statistical, ii) psychological and iii) signal processing points of view. The features were evaluated and tested on retrieval tasks from the Corel and TRECVID2003 image collections. For the latter we also ...

We present a morphological texture contrast (MTC) operator that allows detection of textural and non texture regions in images. We show that in contrast to other approaches, the MTC discriminates between texture details and isolated features and does not extend borders of texture regions. A comparison with other methods used for texture detection is provided. Using the ideas underlying the MTC ...