Oriol Pujol

Automatic Detection of Intestinal Juices in Wireless Capsule Video Endoscopy

Wireless capsule video endoscopy is a novel and challenging clinical technique, whose major reported drawback relates to the high amount of time needed for video visualization. In this paper, we propose a method for the rejection of the parts of the video resulting not valid for analysis by means of automatic detection of intestinal juices. We applied Gabor filters for the characterization of the bubble-like shape of intestinal juices in fasting patients. Our method achieves a significant reduction in visualization time, with no relevant loss of valid frames. The proposed approach is easily extensible to other image analysis scenarios where the described pattern of bubbles can be found

Blurred Shape Model for binary and grey-level symbol recognition

Many symbol recognition problems require the use of robust descriptors in order to obtain rich information of the data. However, the research of a good descriptor is still an open issue due to the high variability of symbols appearance. Rotation, partial occlusions, elastic deformations, intra-class and inter-class variations, or high variability among symbols due to different writing styles, are just a few problems. In this paper, we introduce a symbol shape description to deal with the changes in appearance that these types of symbols suffer. The shape of the symbol is aligned based on principal components to make the recognition invariant to rotation and reflection. Then, we present the Blurred Shape Model descriptor (BSM), where new features encode the probability of appearance of each pixel that outlines the symbols shape. Moreover, we include the new descriptor in a system to deal with multi-class symbol categorization problems. Adaboost is used to train the binary classifiers, learning the BSM features that better split symbol classes. Then, the binary problems are embedded in an Error-Correcting Output Codes framework (ECOC) to deal with the multi-class case. The methodology is evaluated on different synthetic and real data sets. State-of-the-art descriptors and classifiers are compared, showing the robustness and better performance of the present scheme to classify symbols with high variability of appearance.

Intravascular ultrasound images vessel characterization using Adaboost

This paper presents a method for accurate location of the vessel borders based on boosting of classifiers and feature selection. Intravascular Ultrasound Images (IVUS) are an excellent tool for direct visualization of vascular pathologies and evaluation of the lumen and plaque in coronary arteries. Nowadays, the most common methods to separate the tissue from the lumen are based on gray levels providing non-satisfactory segmentations. In this paper, we propose and analyze a new approach to separate tissue from lumen based on an ensemble method for classification and feature selection. We perform a supervised learning of local texture patterns of the plaque and lumen regions and build a large feature space using different texture extractors. A classifier is constructed by selecting a small number of important features using AdaBoost. Feature selection is achieved by a modification of the AdaBoost. A snake is set to deform to achieve continuity on the classified image. Different tests on medical images show the advantages.

Automatic IVUS segmentation of atherosclerotic plaque with stop & go snake

Since the upturn of intravascular ultrasound (IVUS) as an imaging technique for the coronary artery system, much research has been done to simplify the complicated analysis of the resulting images. In this study, an attempt to develop an automatic tissue characterization algorithm for IVUS images was done. The first step was the extraction of texture features. The resulting feature space was used for classification, constructing a likelihood map to represent different coronary plaques. The information in this map was organized using a recently developed geodesic snake formulation, the so-called Stop & Go snake. The novelty of our study lies in this last step, as it was the first time to apply the Stop & Go snake to segment IVUS images.

Supervised Texture Classification for Intravascular Tissue Characterization

Vascular disease, stroke, and arterial dissection or rupture of coronary arteries are considered some of the main causes of mortality in present days. The behavior of the atherosclerotic lesions depends not only on the degree of lumen narrowing but also on the histological composition that causes that narrowing. Therefore, tissue characterization is a fundamental tool for studying and diagnosing the pathologies and lesions associated to the vascular tree.

Handwritten Symbol Recognition by a Boosted Blurred Shape Model with Error Correction

One of the major difficulties of handwriting recognition is the variability among symbols because of the different writer styles. In this paper we introduce the boosting of blurred shape models with error correction, which is a robust approach for describing and recognizing handwritten symbols tolerant to this variability. A symbol is described by a probability density function of blurred shape model that encodes the probability of pixel densities of image regions. Then, to learn the most distinctive features among symbol classes, boosting techniques are used to maximize the separability among the blurred shape models. Finally, the set of binary boosting classifiers is embedded in the framework of Error Correcting Output Codes (ECOC). Our approach has been evaluated in two benchmarking scenarios consisting of handwritten symbols. Compared with state-of-the-art descriptors, our method shows higher tolerance to the irregular deformations induced by handwritten strokes.

Multi-class binary object categorization using Blurred shape models

The main difficulty in the binary object classification field lays in dealing with a high variability of symbol appearance. Rotation, partial occlusions, elastic deformations, or intra-class and inter-class variabilities are just a few problems. In this paper, we introduce a novel object description for this type of symbols. The shape of the object is aligned based on principal components to make the recognition invariant to rotation and reflection. We propose the Blurred Shape Model (BSM) to describe the binary objects. This descriptor encodes the probability of appearance of the pixels that outline the objects shape. Besides, we present the use of this descriptor in a system to improve the BSM performance and deal with binary objects multi-classification problems. Adaboost is used to train the binary classifiers, learning the BSM features that better split object classes. Then, the different binary problems learned by the Adaboost are embedded in the Error Correcting Output Codes framework (ECOC) to deal with the muti-class case. The methodology is evaluated in a wide set of object classes from the MPEG07 repository. Different state-of-the-art descriptors are compared, showing the robustness and better performance of the proposed scheme when classifying objects with high variability of appearance.

Adaboost to classify plaque appearance in IVUS images

Intravascular Ultrasound images represent a unique tool to analyze the morphological vessel structures and make decisions about plaque presence. Texture analysis is a robust way to detect and characterize different kind of vessel plaques. In this article, we make exhaustive comparison between different feature spaces to optimally describe plaque appearance and show that applying advanced classification techniques based on multiple classifiers (adaboost) significantly improves the final results. The validation tests on different kind of plaques are very encouraging.