Paulo M. Azevedo-Marques

Effects of Cannabidiol (CBD) on Regional Cerebral Blood Flow

Animal and human studies have suggested that cannabidiol (CBD) may possess anxiolytic properties, but how these effects are mediated centrally is unknown. The aim of the present study was to investigate this using functional neuroimaging. Regional cerebral blood flow (rCBF) was measured at rest using 99mTc-ECD SPECT in 10 healthy male volunteers, randomly divided into two groups of five subjects. Each subject was studied on two occasions, 1 week apart. In the first session, subjects were given an oral dose of CBD (400 mg) or placebo, in a double-blind procedure. SPECT images were acquired 90 min after drug ingestion. The Visual Analogue Mood Scale was applied to assess subjective states. In the second session, the same procedure was performed using the drug that had not been administered in the previous session. Within-subject between-condition rCBF comparisons were performed using statistical parametric mapping (SPM). CBD significantly decreased subjective anxiety and increased mental sedation, while placebo did not induce significant changes. Assessment of brain regions where anxiolytic effects of CBD were predicted a priori revealed two voxel clusters of significantly decreased ECD uptake in the CBD relative to the placebo condition (p<0.001, uncorrected for multiple comparisons). These included a medial temporal cluster encompassing the left amygdala–hippocampal complex, extending into the hypothalamus, and a second cluster in the left posterior cingulate gyrus. There was also a cluster of greater activity with CBD than placebo in the left parahippocampal gyrus (p<0.001). These results suggest that CBD has anxiolytic properties, and that these effects are mediated by an action on limbic and paralimbic brain areas.

Content-based Retrieval of Mammograms Using Visual Features Related to Breast Density Patterns

This paper describes part of content-based image retrieval (CBIR) system that has been developed for mammograms. Details are presented of methods implemented to derive measures of similarity based upon structural characteristics and distributions of density of the fibroglandular tissue, as well as the anatomical size and shape of the breast region as seen on the mammogram. Well-known features related to shape, size, and texture (statistics of the gray-level histogram, Haralick’s texture features, and moment-based features) were applied, as well as less-explored features based in the Radon domain and granulometric measures. The Kohonen self-organizing map (SOM) neural network was used to perform the retrieval operation. Performance evaluation was done using precision and recall curves obtained from comparison between the query and retrieved images. The proposed methodology was tested with 1,080 mammograms, including craniocaudal and mediolateral-oblique views. Precision rates obtained are in the range from 79% to 83% considering the total image set. Considering the first 50% of the retrieved mages, the precision rates are in the range from 78% to 83%; the rates are in the range from 79% to 86% considering the first 25% of the retrieved images. Results obtained indicate the potential of the implemented methodology to serve as a part of a CBIR system for mammography.

An Association Rule-Based Method to Support Medical Image Diagnosis With Efficiency

In this paper, we propose a method based on association rule-mining to enhance the diagnosis of medical images (mammograms). It combines low-level features automatically extracted from images and high-level knowledge from specialists to search for patterns. Our method analyzes medical images and automatically generates suggestions of diagnoses employing mining of association rules. The suggestions of diagnosis are used to accelerate the image analysis performed by specialists as well as to provide them an alternative to work on. The proposed method uses two new algorithms, PreSAGe and HiCARe. The PreSAGe algorithm combines, in a single step, feature selection and discretization, and reduces the mining complexity. Experiments performed on PreSAGe show that this algorithm is highly suitable to perform feature selection and discretization in medical images. HiCARe is a new associative classifier. The HiCARe algorithm has an important property that makes it unique: it assigns multiple keywords per image to suggest a diagnosis with high values of accuracy. Our method was applied to real datasets, and the results show high sensitivity (up to 95%) and accuracy (up to 92%), allowing us to claim that the use of association rules is a powerful means to assist in the diagnosing task.

Efficient Content-Based Image Retrieval through Metric Histograms

This paper presents a new and efficient method for content-based image retrieval employing the color distribution of images. This new method, called metric histogram, takes advantage of the correlation among adjacent bins of histograms, reducing the dimensionality of the feature vectors extracted from images, leading to faster and more flexible indexing and retrieval processes. The proposed technique works on each image independently from the others in the dataset, therefore there is no pre-defined number of color regions in the resulting histogram. Thus, it is not possible to use traditional comparison algorithms such as Euclidean or Manhattan distances. To allow the comparison of images through the new feature vectors given by metric histograms, a new metric distance function MHD( ) is also proposed. This paper shows the improvements in timing and retrieval discrimination obtained using metric histograms over traditional ones, even when using images with different spatial resolution or thumbnails. The experimental evaluation of the new method, for answering similarity queries over two representative image databases, shows that the metric histograms surpass the retrieval ability of traditional histograms because they are invariant on geometrical and brightness image transformations, and answer the queries up to 10 times faster than the traditional ones.

Fractal analysis of image textures for indexing and retrieval by content

This paper proposes the use of fractal analysis as a means to discriminate textured segmented regions of medical images. We show that the use of the fractals can boost the representation level of traditional image features allowing high rates of precision when answering similarity queries over images employing a variance weighted Manhattan distance. The cost to compute the fractal measurements is linear on the image size, what makes their use a suitable choice for large sets of images.

How to add content-based image retrieval capability in a PACS

This paper presents a new picture archiving and communication system (PACS), called cbPACS (content-based PACS), which has content-based image retrieval resources. cbPACS answers similarity (range and nearest-neighbor) queries, taking advantage of a metric access method embedded into the image database manager. The images are compared via their features, which are extracted by an image processing system module. The system works on features based on the color distribution of the images through normalized histograms as well as metric histograms. Metric histograms are invariant with regard to scale, translation and rotation of images and also to brightness transformations. cbPACS is prepared to integrate new image features, based on the texture and shape of the main objects in the image.

Diagnóstico auxiliado por computador na radiologia

Varios desenvolvimentos tecnologicos estao convergindo de forma a aumentar a influencia da area de imagens nas pesquisas biomedicas e na medicina clinica. Muitos pesquisadores tem trabalhado no desenvolvimento de sistemas computadorizados para deteccao automatizada e quantificacao de anormalidades em imagens radiologicas. Estes sistemas sao dedicados ao diagnostico auxiliado por computador. Este artigo discute os conceitos basicos relacionados ao diagnostico auxiliado por computador e apresenta uma revisao bibliografica sobre o assunto.

Using an image-extended relational database to support content-based image retrieval in a PACS

This paper presents a new Picture Archiving and Communication System (PACS), called cbPACS, which has content-based image retrieval capabilities. The cbPACS answers range and k-nearest- neighbor similarity queries, employing a relational database manager extended to support images. The images are compared through their features, which are extracted by an image-processing module and stored in the extended relational database. The database extensions were developed aiming at efficiently answering similarity queries by taking advantage of specialized indexing methods. The main concept supporting the extensions is the definition, inside the relational manager, of distance functions based on features extracted from the images. An extension to the SQL language enables the construction of an interpreter that intercepts the extended commands and translates them to standard SQL, allowing any relational database server to be used. By now, the system implemented works on features based on color distribution of the images through normalized histograms as well as metric histograms. Metric histograms are invariant regarding scale, translation and rotation of images and also to brightness transformations. The cbPACS is prepared to integrate new image features, based on texture and shape of the main objects in the image.

The Metric Histogram: A New and Efficient Approach for Content-based Image Retrieval

This paper presents the metric histogram, a new and efficient technique to capture the brightness feature of images, allowing faster retrieval of images based on their content. Histograms provide a fast way to chop down large subsets of images, but are difficult to be indexed in existing data access methods. The proposed metric histograms reduce the dimensionality of the feature vectors leading to faster and more flexible indexing and retrieval processes. A new metric distance function DM( ) to measure the dissimilarity between images through their metric histograms is also presented. This paper shows the improvements obtained using the metric histograms over the traditional ones, through experiments for answering similarity queries over two databases containing respectively 500 and 4,247 magnetic resonance medical images. The experiments performed showed that metric histograms are more than 10 times faster than the traditional approach of using histograms and keep the same recovering capacity.

Color image processing and content-based image retrieval techniques for the analysis of dermatological lesions

This paper presents color image processing methods for the analysis of dermatological images in the context of a content-based image retrieval (CBIR) system. Tests were conducted on the classification of tissue components in skin lesions, in terms of necrotic tissue, fibrin, granulation, and mixed composition. The images were classified based on color components by an expert dermatologist following a black-yellow-red model. Indexing and retrieval of images were performed based on texture information obtained from the red, green, blue, hue, and saturation components of the color images. The performance of the CBIR system was measured in terms of precision and recall. Initial results demonstrate the potential of the proposed methods with the best precision result of 70% obtained for the characterization of mixed tissue composition.