Medical images modality classification using multi-scale dictionary learning

In this paper, we proposed a method for classification of medical images captured by different sensors (modalities) based on multi-scale wavelet representation using dictionary learning. Wavelet features extracted from an image provide discrimination useful for classification of medical images, namely, diffusion tensor imaging (DTI), magnetic resonance imaging (MRI), magnetic resonance angiography (MRA) and functional magnetic resonance imaging (FRMI). The ability of On-line dictionary learning (ODL) to achieve sparse representation of an image is exploited to develop dictionaries for each class using multi-scale representation (wavelets) feature. An experimental analysis performed on a set of images from the ICBM medical database demonstrates efficacy of the proposed method

Identificação de plantas por análise de textura foliar

A biodiversidade das espécies existentes no riquíssimo reino vegetal tornam os modelos tradicionais de taxonomia, na qual o processo de classificação é realizado manualmente, uma tarefa muito complexa e morosa. As dificuldades presentes nesse processo implicam na existência de poucas pesquisas de classificação vegetal utilizando métodos matemáticos e computacionais. Desta forma, visando contribuir com as técnicas de taxonomia manuais já desenvolvidas, esse estudo apresenta uma nova metodologia computacional de identificação de espécies vegetais por meio da análise da textura foliar. O método, chamado de Fractal Multi-Níveis, é baseado no cálculo da dimensão fractal de imagens binárias geradas a partir da textura. Os excelentes resultados obtidos demonstram como os métodos computacionais de análise de imagens, em especial análise de textura, podem\ud contribuir facilitando e agilizando a tarefa de identificação de espécies vegetais.The diversity of species in the plant kingdom become traditional models of taxonomy, in which the classification process is performed manually, a very difficult task. The present difficulties in this process imply in the existence of few studies of plant classification using mathematical and computational methods. Therefore, in order to contribute to the manuals methods of taxonomy already developed, this study presents a new computational method for identifying plant species through analysis of leaf texture. The method, called Fractal Multilevel, is based on the calculation of fractal dimension of binary images generated\ud from the texture. The excellent results show how the computational methods for image analysis, texture analysis in particular, can help the task of identifying plant species.CNPq (03746/2004-1; 504476/2007-6)FAPESP (06/54367-9; 06/54367-9; 08/57313-2

Texture image retrieval and image segmentation using composite sub-band gradient vectors

A new texture descriptor, called CSG vector, is proposed for image retrieval and image segmentation in this paper. The descriptor can be generated by composing the gradient vectors obtained from the sub-images through a wavelet decomposition of a texture image. By exercising a database containing 2400 images which were cropped from a set of 150 types of textures selected from the Brodatz Album, we demonstrated that 93% efficacy can be achieved in image retrieval. Moreover, using CSG vectors as the texture descriptor for image segmentation can generate very successful results for both synthesized and natural scene images. (c) 2006 Elsevier Inc. All rights reserved

Classification of Medical Data Based On Sparse Representation Using Dictionary Learning

Due to the increase in the sources of image acquisition and storage capacity, the search for relevant information in large medical image databases has become more challenging. Classification of medical data into different categories is an important task, and enables efficient cataloging and retrieval with large image collections. The medical image classification systems available today classify medical images based on modality, body part, disease or orientation. Recent work in this direction seek to use the semantics of medical data to achieve better classification. However, representation of semantics is a challenging task and sparse representation has been explored in this thesis for this task