T. Beach, P. Gibbs, M. D. Pickles, L. Turnbull Centre for MR Investigations, University of Hull, Hull, United Kingdom Introduction Quantitative textural analysis is an established method of image classification in aerial and satellite photography. In recent years attempts have been made to utilise texture in MRI, particularly in the brain [1-6], but also in other organs such as breast [7] and l...

We study the reliability of a set of stereo-based 3-D textural features in classiication in response to diier-ent training data. Two types of training data, \labeling-based" and \chip-based," are investigated. Experiments have been carried out to compare the 3-D features with a set of 2-D features based on co-occurrence analysis. Results show that the 3-D features consistently outper-form the 2...

In this paper an automatic classification algorithm is proposed for the diagnosis of the liver steatosis, also known as, fatty liver, from ultrasound images. The features, automatically extracted from the ultrasound images used by the classifier, are basically the ones used by the physicians in the diagnosis of the disease based on visual inspection of the ultrasound images. The main novelty of...

In high resolution data classification process, combining texture features with spectral bands can effectively improve the classification accuracy. However, the window size which is difficult to choose is regarded as an important factor influencing overall classification accuracy in textural classification and current approaches to image texture analysis only depend on a single moving window wh...

Textural features are applied for detection of morphological pathologies of vocal cords. Cooccurrence matrices as statistical features are presented as well as filter bank analysis by Gabor filters. Both methods are extended to handle color images. Their robustness against camera movement and vibration of vocal cords is evaluated. Classification results due to three in vivo sequences are in bet...

Recognizing plants from imagery is a complex task due to their irregular nature. In this research, three tree species, Japanese yew (Taxus cuspidata Sieb. & Zucc.), Hicks yew (Taxus x media), and eastern white pine (Pinus strobus L.), were identified using their textural properties. First, the plants were separated from their backgrounds in digital images based on a combination of textural feat...

The problem of 3D object recognition is of immense practical importance, with the last decade witnessing a number of breakthroughs in the state of the art. Most of the previous work has focused on the matching of textured objects using local appearance descriptors extracted around salient image points. The recently proposed bag of boundaries method was the first to address directly the problem ...

The research work deals with an approach to perform texture and morphological based retrieval on a corpus of food grain images. The work has been carried out using Image Warping and Image analysis approach. The method has been employed to normalize food grain images and hence eliminating the effects of orientation using image warping technique with proper scaling. The images have been properly ...

In this paper, a new technique is proposed for automatic segmentation of multiple sclerosis (MS) lesions from brain magnetic resonance imaging (MRI) data. The technique uses a trained support vector machine (SVM) to discriminate between the blocks in regions of MS lesions and the blocks in non-MS lesion regions mainly based on the textural features with aid of the other features. The classifica...

Introduction: Previous studies [1,2] have suggested that genetic subtypes of breast cancer are associated with distinct imaging phenotypes on DCEMRI. However, previous attempts to distinguish molecular subtypes of breast cancer are based on qualitative, visual examination of the tumors. In our previous work, we developed a computer aided diagnosis (CAD) system that exploited textural changes wi...