Detecting abnormalities in endoscopic capsule images using color wavelet features and feed-forward neural networks

This paper presents a system to support medical diagnosis and detection of abnormal lesions by processing endoscopic images. Endoscopic images possess rich information expressed by texture. Texture information can be efficiently extracted from medium scales of the wavelet transform. The set of features proposed in this paper to encode textural information is named color wavelet covariance (CWC). CWC coefficients are based on the covariances of second order textural measures, an optimum subset of them is proposed. The proposed approach is supported by a classifier based on multilayer perceptron network for the characterization of the image regions along the video frames. The whole methodology has been applied on real data containing 6 full endoscopic exams and reached 87% specificity and 97.4% sensitivity.Center Algoritm

Textural-Contextual Labeling and Metadata Generation for Remote Sensing Applications

Despite the extensive research and the advent of several new information technologies in the last three decades, machine labeling of ground categories using remotely sensed data has not become a routine process. Considerable amount of human intervention is needed to achieve a level of acceptable labeling accuracy. A number of fundamental reasons may explain why machine labeling has not become automatic. In addition, there may be shortcomings in the methodology for labeling ground categories. The spatial information of a pixel, whether textural or contextual, relates a pixel to its surroundings. This information should be utilized to improve the performance of machine labeling of ground categories. Landsat-4 Thematic Mapper (TM) data taken in July 1982 over an area in the vicinity of Washington, D.C. are used in this study. On-line texture extraction by neural networks may not be the most efficient way to incorporate textural information into the labeling process. Texture features are pre-computed from cooccurrence matrices and then combined with a pixel's spectral and contextual information as the input to a neural network. The improvement in labeling accuracy with spatial information included is significant. The prospect of automatic generation of metadata consisting of ground categories, textural and contextual information is discussed

Texture wear analysis in textile floor coverings by using depth information

Considerable industrial and academic interest is addressed to automate the quality inspection of textile floor coverings, mostly using intensity images. Recently, the use of depth information has been explored to better capture the 3D structure of the surface. In this paper, we present a comparison of features extracted from three texture analysis techniques. The evaluation is based on how well the algorithms allow a good linear ranking and a good discriminance of consecutive wear labels. The results show that the use of Local Binary Patterns techniques result in a better ranking of the wear labels as well as in a higher amount of discrimination between features related to consecutive degrees of wear

Effective Method of Image Retrieval Using BTC with Gabor Wavelet Matrix

emergence of multimedia technology and the rapidly expanding image collections on the database have attracted significant research efforts in providing tools for effective retrieval and management of visual data. The need to find a desired image from a large collection. Image retrieval is the field of study concerned with searching and retrieving digital image from a collection of database .In real images, regions are often homogenous; neighboring pixels usually have similar properties (shape, color, texture). In this paper we proposed novel image retrieval based on Block Truncation Coding (BTC) with Gabor wavelet co-occurrence matrix. For image retrieval the features like shape, color, texture, spatial relation, and correlation and Eigen values are considered. BTC can be used for grayscale as well as for color images. The average precision and recall of all queries are computed and considered for performance analysis

Local and deep texture features for classification of natural and biomedical images

Developing efficient feature descriptors is very important in many computer vision applications including biomedical image analysis. In the past two decades and before the popularity of deep learning approaches in image classification, texture features proved to be very effective to capture the gradient variation in the image. Following the success of the Local Binary Pattern (LBP) descriptor, many variations of this descriptor were introduced to further improve the ability of obtaining good classification results. However, the problem of image classification gets more complicated when the number of images increases as well as the number of classes. In this case, more robust approaches must be used to address this problem. In this thesis, we address the problem of analyzing biomedical images by using a combination of local and deep features. First, we propose a novel descriptor that is based on the motif Peano scan concept called Joint Motif Labels (JML). After that, we combine the features extracted from the JML descriptor with two other descriptors called Rotation Invariant Co-occurrence among Local Binary Patterns (RIC-LBP) and Joint Adaptive Medina Binary Patterns (JAMBP). In addition, we construct another descriptor called Motif Patterns encoded by RIC-LBP and use it in our classification framework. We enrich the performance of our framework by combining these local descriptors with features extracted from a pre-trained deep network called VGG-19. Hence, the 4096 features of the Fully Connected 'fc7' layer are extracted and combined with the proposed local descriptors. Finally, we show that Random Forests (RF) classifier can be used to obtain superior performance in the field of biomedical image analysis. Testing was performed on two standard biomedical datasets and another three standard texture datasets. Results show that our framework can beat state-of-the-art accuracy on the biomedical image analysis and the combination of local features produce promising results on the standard texture datasets.Includes bibliographical reference

A neural system for deforestation monitoring on Landsat images of the Amazon Region

AbstractWe deal with the problem of automating the interpretation of satellite images of the Amazon region for deforestation monitoring. Our approach is based on a combination of image segmentation and classification techniques, the latter emplying a neural-network architecture that works on a fuzzy model of classification. The architecture implements a relaxation mechanism on top of a feedforward neural network, in order to take advantage of the interrelations among neighboring image segments. Our fuzzy, segment-based approach has numerous advantages over more traditional, pixel-based approaches employing statistical techniques. These advantages range from the possibility of treating transition and interference phenomena in the images to the ease with which complex information related to a region's geometry, texture, and contextual setting can be used. We report on a great variety of experiments on representative portions of the Amazon region, employing neural networks trained by the back-propagation algorithm. The results indicate very good overall performance, and allow us to draw some conclusions regarding the effectiveness of the various sources of information available as input to the system. In particular, it appears that simple spectral information, together with textural information on a region's entropy and correlation and simple contextual information, are effective in the classification for deforestation monitoring. It also appears that the effective incorporation of geometric information would require further investigation on possible enhancements to the system

A Hybrid DE-RGSO-ELM for Brain Tumor Tissue Categorization in 3D Magnetic Resonance Images

Medical diagnostics, a technique used for visualizing the internal structures and functions of human body, serves as a scientific tool to assist physicians and involves direct use of digital imaging system analysis. In this scenario, identification of brain tumors is complex in the diagnostic process. Magnetic resonance imaging (MRI) technique is noted to best assist tissue contrast for anatomical details and also carries out mechanisms for investigating the brain by functional imaging in tumor predictions. Considering 3D MRI model, analyzing the anatomy features and tissue characteristics of brain tumor is complex in nature. Henceforth, in this work, feature extraction is carried out by computing 3D gray-level cooccurence matrix (3D GLCM) and run-length matrix (RLM) and feature subselection for dimensionality reduction is performed with basic differential evolution (DE) algorithm. Classification is performed using proposed extreme learning machine (ELM), with refined group search optimizer (RGSO) technique, to select the best parameters for better simplification and training of the classifier for brain tissue and tumor characterization as white matter (WM), gray matter (GM), cerebrospinal fluid (CSF), and tumor. Extreme learning machine outperforms the standard binary linear SVM and BPN for medical image classifier and proves better in classifying healthy and tumor tissues. The comparison between the algorithms proves that the mean and standard deviation produced by volumetric feature extraction analysis are higher than the other approaches. The proposed work is designed for pathological brain tumor classification and for 3D MRI tumor image segmentation. The proposed approaches are applied for real time datasets and benchmark datasets taken from dataset repositories