Texture analysis is quite important operation in medical image processing. Wavelet based texture analysis is developed which gives better way of extracting inherent object and properties in medical image. It gives a robust technique in disease diagnosis and future of biophysical disorder ness in a body. Image processing has an important influence on the medical decision making process and even ...

This paper outlines the exploration of two methods to detect texture in a digital cryosection image from the Visible Human Project. For the purpose of this research, texture is defined as a regular or irregular placement of color in an image. A higher-level decision-making algorithm was employed to extract different body tissues: fat, muscle, and bone. This algorithm was designed on the premise...

Image content based retrieval is emerging as an important research area with application to digital libraries and multimedia databases. The focus of this paper is on the image processing aspects and in particular using texture information for browsing and retrieval of large image data. We propose the use of Gabor wavelet features for texture analysis and provide a comp~rehensive experimental ev...

With the development of ultra-high-resolution display devices, the visual perception of fine texture details is becoming increasingly important. Traditional image upsampling methods suffer from either loss of highfrequency texture details or very high time cost. In this paper, we propose an iterative projection reconstruction (IPR) method for fast and efficient image upsampling. The proposed me...

Image retrieval method utilizing texture information which is derived from Discrete Wavelet Transformation: DWT together with color information is proposed. One of the specific features of the texture information extracted from portions of image is based on Dyadic wavelet transformation with forming texture feature vector by using energy derived from Gabor transform on 7 by 7 pixel neighbor of ...

This paper presents a two-dimensional deformable model-based image segmentation method that integrates texture feature analysis into the model evolution process. Typically, the deformable models use edge and intensity-based features as the influencing image forces. Incorporation of the image texture information can increase the methods effectiveness and application possibilities. The algorithm ...

Color, texture, shape and luminance are the prominent features for image segmentation. Texture is an organized group of spatial repetitive arrangements in an image and it is a vital attribute in many image processing and computer vision applications. The objective of this work is to segment the texture sub images from the given arbitrary image. The main contribution of this work is to introduce...

This paper proposes, a hybrid approach employing texture and colour feature is investigated. A modified approach for performing texture based feature extraction by gray level co-occurrence matrix and colour based feature extraction by colour cooccurrence vector. The Euclidean distance classifier is used for finding the similarity measures between the query image and the database image. In our p...

BACKGROUND There is increasing interest in applying image texture quantifiers to assess the intra-tumor heterogeneity observed in FDG-PET images of various cancers. Use of these quantifiers as prognostic indicators of disease outcome and/or treatment response has yielded inconsistent results. We study the general applicability of some well-established texture quantifiers to the image data uniqu...

This paper proposes an approach to image segmentation using Iterated Graph Cuts based on local texture features of wavelet coefficients. Using Haar Wavelet based Multiresolution Analysis, the lowfrequency range (smoothed image) is used for the n-link and the highfrequency range (local texture features) is used for the t-link along with the color histogram. The proposed method can segment an obj...