Online Mammographic Images Database for Development and Comparison of CAD Schemes

Considering the difficulties in finding good-quality images for the development and test of computer-aided diagnosis (CAD), this paper presents a public online mammographic images database free for all interested viewers and aimed to help develop and evaluate CAD schemes. The digitalization of the mammographic images is made with suitable contrast and spatial resolution for processing purposes. The broad recuperation system allows the user to search for different images, exams, or patient characteristics. Comparison with other databases currently available has shown that the presented database has a sufficient number of images, is of high quality, and is the only one to include a functional search system.

Automatic dynamic mask extraction for PIV images containing an unsteady interface, bubbles, and a moving structure

When performing Particle Image Velocimetry (PIV) measurements in complex fluid flows with moving interfaces and a two-phase flow, it is necessary to develop a mask to remove non-physical measurements. This is the case when studying, for example, the complex bubble sweep-down phenomenon observed in oceanographic research vessels. Indeed, in such a configuration, the presence of an unsteady free surface, of a solid–liquid interface and of bubbles in the PIV frame, leads to generate numerous laser reflections and therefore spurious velocity vectors. In this note, an image masking process is developed to successively identify the boundaries of the ship and the free surface interface. As the presence of the solid hull surface induces laser reflections, the hull edge contours are simply detected in the first PIV frame and dynamically estimated for consecutive ones. As for the unsteady surface determination, a specific process is implemented like the following: i) the edge detection of the gradient magnitude in the PIV frame, ii) the extraction of the particles by filtering high-intensity large areas related to the bubbles and/or hull reflections, iii) the extraction of the rough region containing these particles and their reflections, iv) the removal of these reflections. The unsteady surface is finally obtained with a fifth-order polynomial interpolation. The resulted free surface is successfully validated from the Fourier analysis and by visualizing selected PIV images containing numerous spurious high intensity areas. This paper demonstrates how this data analysis process leads to PIV images database without reflections and an automatic detection of both the free surface and the rigid body. An application of this new mask is finally detailed, allowing a preliminary analysis of the hydrodynamic flow.

Aplicación de técnicas de inteligencia artificial para contribuir en la detección de microcalcificaciones en mamografía digitalizada

A nivel mundial, el cáncer de mama es el tipo de cáncer más frecuente además de una de las principales causas de muerte entre la población femenina. Actualmente, el método más eficaz para detectar lesiones mamarias en una etapa temprana es la mamografía. Ésta contribuye decisivamente al diagnóstico precoz de esta enfermedad que, si se detecta a tiempo, tiene una probabilidad de curación muy alta. Uno de los principales y más frecuentes hallazgos en una mamografía, son las microcalcificaciones, las cuales son consideradas como un indicador importante de cáncer de mama. En el momento de analizar las mamografías, factores como la capacidad de visualización, la fatiga o la experiencia profesional del especialista radiólogo hacen que el riesgo de omitir ciertas lesiones presentes se vea incrementado. Para disminuir dicho riesgo es importante contar con diferentes alternativas como por ejemplo, una segunda opinión por otro especialista o un doble análisis por el mismo. En la primera opción se eleva el coste y en ambas se prolonga el tiempo del diagnóstico. Esto supone una gran motivación para el desarrollo de sistemas de apoyo o asistencia en la toma de decisiones. En este trabajo de tesis se propone, se desarrolla y se justifica un sistema capaz de detectar microcalcificaciones en regiones de interés extraídas de mamografías digitalizadas, para contribuir a la detección temprana del cáncer demama. Dicho sistema estará basado en técnicas de procesamiento de imagen digital, de reconocimiento de patrones y de inteligencia artificial. Para su desarrollo, se tienen en cuenta las siguientes consideraciones: 1. Con el objetivo de entrenar y probar el sistema propuesto, se creará una base de datos de imágenes, las cuales pertenecen a regiones de interés extraídas de mamografías digitalizadas. 2. Se propone la aplicación de la transformada Top-Hat, una técnica de procesamiento digital de imagen basada en operaciones de morfología matemática. La finalidad de aplicar esta técnica es la de mejorar el contraste entre las microcalcificaciones y el tejido presente en la imagen. 3. Se propone un algoritmo novel llamado sub-segmentación, el cual está basado en técnicas de reconocimiento de patrones aplicando un algoritmo de agrupamiento no supervisado, el PFCM (Possibilistic Fuzzy c-Means). El objetivo es encontrar las regiones correspondientes a las microcalcificaciones y diferenciarlas del tejido sano. Además, con la finalidad de mostrar las ventajas y desventajas del algoritmo propuesto, éste es comparado con dos algoritmos del mismo tipo: el k-means y el FCM (Fuzzy c-Means). Por otro lado, es importante destacar que en este trabajo por primera vez la sub-segmentación es utilizada para detectar regiones pertenecientes a microcalcificaciones en imágenes de mamografía. 4. Finalmente, se propone el uso de un clasificador basado en una red neuronal artificial, específicamente un MLP (Multi-layer Perceptron). El propósito del clasificador es discriminar de manera binaria los patrones creados a partir de la intensidad de niveles de gris de la imagen original. Dicha clasificación distingue entre microcalcificación y tejido sano. ABSTRACT Breast cancer is one of the leading causes of women mortality in the world and its early detection continues being a key piece to improve the prognosis and survival. Currently, the most reliable and practical method for early detection of breast cancer is mammography.The presence of microcalcifications has been considered as a very important indicator ofmalignant types of breast cancer and its detection and classification are important to prevent and treat the disease. However, the detection and classification of microcalcifications continue being a hard work due to that, in mammograms there is a poor contrast between microcalcifications and the tissue around them. Factors such as visualization, tiredness or insufficient experience of the specialist increase the risk of omit some present lesions. To reduce this risk, is important to have alternatives such as a second opinion or a double analysis for the same specialist. In the first option, the cost increases and diagnosis time also increases for both of them. This is the reason why there is a great motivation for development of help systems or assistance in the decision making process. This work presents, develops and justifies a system for the detection of microcalcifications in regions of interest extracted fromdigitizedmammographies to contribute to the early detection of breast cancer. This systemis based on image processing techniques, pattern recognition and artificial intelligence. For system development the following features are considered: With the aim of training and testing the system, an images database is created, belonging to a region of interest extracted from digitized mammograms. The application of the top-hat transformis proposed. This image processing technique is based on mathematical morphology operations. The aim of this technique is to improve the contrast betweenmicrocalcifications and tissue present in the image. A novel algorithm called sub-segmentation is proposed. The sub-segmentation is based on pattern recognition techniques applying a non-supervised clustering algorithm known as Possibilistic Fuzzy c-Means (PFCM). The aim is to find regions corresponding to the microcalcifications and distinguish them from the healthy tissue. Furthermore,with the aim of showing themain advantages and disadvantages this is compared with two algorithms of same type: the k-means and the fuzzy c-means (FCM). On the other hand, it is important to highlight in this work for the first time the sub-segmentation is used for microcalcifications detection. Finally, a classifier based on an artificial neural network such as Multi-layer Perceptron is used. The purpose of this classifier is to discriminate froma binary perspective the patterns built from gray level intensity of the original image. This classification distinguishes between microcalcifications and healthy tissue.

Casamento de modelos baseado em projeções radiais e circulares invariante a pontos de vista.

Este trabalho aborda o problema de casamento entre duas imagens. Casamento de imagens pode ser do tipo casamento de modelos (template matching) ou casamento de pontos-chaves (keypoint matching). Estes algoritmos localizam uma região da primeira imagem numa segunda imagem. Nosso grupo desenvolveu dois algoritmos de casamento de modelos invariante por rotação, escala e translação denominados Ciratefi (Circula, radial and template matchings filter) e Forapro (Fourier coefficients of radial and circular projection). As características positivas destes algoritmos são a invariância a mudanças de brilho/contraste e robustez a padrões repetitivos. Na primeira parte desta tese, tornamos Ciratefi invariante a transformações afins, obtendo Aciratefi (Affine-ciratefi). Construímos um banco de imagens para comparar este algoritmo com Asift (Affine-scale invariant feature transform) e Aforapro (Affine-forapro). Asift é considerado atualmente o melhor algoritmo de casamento de imagens invariante afim, e Aforapro foi proposto em nossa dissertação de mestrado. Nossos resultados sugerem que Aciratefi supera Asift na presença combinada de padrões repetitivos, mudanças de brilho/contraste e mudanças de pontos de vista. Na segunda parte desta tese, construímos um algoritmo para filtrar casamentos de pontos-chaves, baseado num conceito que denominamos de coerência geométrica. Aplicamos esta filtragem no bem-conhecido algoritmo Sift (scale invariant feature transform), base do Asift. Avaliamos a nossa proposta no banco de imagens de Mikolajczyk. As taxas de erro obtidas são significativamente menores que as do Sift original.

Spatial database of canopy plant densities created from satellite images over Galapagos in 2011, with intent to manage invasive species

Mapping is an important tool for the management of plant invasions. If landscapes are mapped in an appropriate way, results can help managers decide when and where to prioritize their efforts. We mapped vegetation with the aim of providing key information for managers on the extent, density and rates of spread of multiple invasive species across the landscape. Our case study focused on an area of Galapagos National Park that is faced with the challenge of managing multiple plant invasions. We used satellite imagery to produce a spatially-explicit database of plant species densities in the canopy, finding that 92% of the humid highlands had some degree of invasion and 41% of the canopy was comprised of invasive plants. We also calculated the rate of spread of eight invasive species using known introduction dates, finding that species with the most limited dispersal ability had the slowest spread rates while those able to disperse long distances had a range of spread rates. Our results on spread rate fall at the lower end of the range of published spread rates of invasive plants. This is probably because most studies are based on the entire geographic extent, whereas our estimates took plant density into account. A spatial database of plant species densities, such as the one developed in our case study, can be used by managers to decide where to apply management actions and thereby help curtail the spread of current plant invasions. For example, it can be used to identify sites containing several invasive plant species, to find the density of a particular species across the landscape or to locate where native species make up the majority of the canopy. Similar databases could be developed elsewhere to help inform the management of multiple plant invasions over the landscape.

An fMRI Normative Database for Connectivity Networks Using One-Class Support Vector Machines

The application of functional magnetic resonance imaging (fMRI) in neuroscience studies has increased enormously in the last decade. Although primarily used to map brain regions activated by specific stimuli, many studies have shown that fMRI can also be useful in identifying interactions between brain regions (functional and effective connectivity). Despite the widespread use of fMRI as a research tool, clinical applications of brain connectivity as studied by fMRI are not well established. One possible explanation is the lack of normal pattern, and intersubject variability-two variables that are still largely uncharacterized in most patient populations of interest. In the current study, we combine the identification of functional connectivity networks extracted by using Spearman partial correlation with the use of a one-class support vector machine in order construct a normative database. An application of this approach is illustrated using an fMRI dataset of 43 healthy Subjects performing a visual working memory task. In addition, the relationships between the results obtained and behavioral data are explored. Hum Brain Mapp 30:1068-1076, 2009. (C) 2008 Wiley-Liss. Inc.

Computer-aided recognition of dental implants in X-ray images

Dental implant recognition in patients without available records is a time-consuming and not straightforward task. The traditional method is a complete user-dependent process, where the expert compares a 2D X-ray image of the dental implant with a generic database. Due to the high number of implants available and the similarity between them, automatic/semi-automatic frameworks to aide implant model detection are essential. In this study, a novel computer-aided framework for dental implant recognition is suggested. The proposed method relies on image processing concepts, namely: (i) a segmentation strategy for semi-automatic implant delineation; and (ii) a machine learning approach for implant model recognition. Although the segmentation technique is the main focus of the current study, preliminary details of the machine learning approach are also reported. Two different scenarios are used to validate the framework: (1) comparison of the semi-automatic contours against implant’s manual contours of 125 X-ray images; and (2) classification of 11 known implants using a large reference database of 601 implants. Regarding experiment 1, 0.97±0.01, 2.24±0.85 pixels and 11.12±6 pixels of dice metric, mean absolute distance and Hausdorff distance were obtained, respectively. In experiment 2, 91% of the implants were successfully recognized while reducing the reference database to 5% of its original size. Overall, the segmentation technique achieved accurate implant contours. Although the preliminary classification results prove the concept of the current work, more features and an extended database should be used in a future work.

Tuning Iris Recognition for Noisy Images

The use of iris recognition for human authentication has been spreading in the past years. Daugman has proposed a method for iris recognition, composed by four stages: segmentation, normalization, feature extraction, and matching. In this paper we propose some modifications and extensions to Daugman's method to cope with noisy images. These modifications are proposed after a study of images of CASIA and UBIRIS databases. The major modification is on the computationally demanding segmentation stage, for which we propose a faster and equally accurate template matching approach. The extensions on the algorithm address the important issue of pre-processing that depends on the image database, being mandatory when we have a non infra-red camera, like a typical WebCam. For this scenario, we propose methods for reflection removal and pupil enhancement and isolation. The tests, carried out by our C# application on grayscale CASIA and UBIRIS images show that the template matching segmentation method is more accurate and faster than the previous one, for noisy images. The proposed algorithms are found to be efficient and necessary when we deal with non infra-red images and non uniform illumination.

INbreast: Toward a Full-field Digital Mammographic Database

Rationale and Objectives Computer-aided detection and diagnosis (CAD) systems have been developed in the past two decades to assist radiologists in the detection and diagnosis of lesions seen on breast imaging exams, thus providing a second opinion. Mammographic databases play an important role in the development of algorithms aiming at the detection and diagnosis of mammary lesions. However, available databases often do not take into consideration all the requirements needed for research and study purposes. This article aims to present and detail a new mammographic database. Materials and Methods Images were acquired at a breast center located in a university hospital (Centro Hospitalar de S. João [CHSJ], Breast Centre, Porto) with the permission of the Portuguese National Committee of Data Protection and Hospital's Ethics Committee. MammoNovation Siemens full-field digital mammography, with a solid-state detector of amorphous selenium was used. Results The new database—INbreast—has a total of 115 cases (410 images) from which 90 cases are from women with both breasts affected (four images per case) and 25 cases are from mastectomy patients (two images per case). Several types of lesions (masses, calcifications, asymmetries, and distortions) were included. Accurate contours made by specialists are also provided in XML format. Conclusion The strengths of the actually presented database—INbreast—relies on the fact that it was built with full-field digital mammograms (in opposition to digitized mammograms), it presents a wide variability of cases, and is made publicly available together with precise annotations. We believe that this database can be a reference for future works centered or related to breast cancer imaging.

Integrating forensic information in a crime intelligence database

Since 2008, Intelligence units of six states of the western part of Switzerland have been sharing a common database for the analysis of high volume crimes. On a daily basis, events reported to the police are analysed, filtered and classified to detect crime repetitions and interpret the crime environment. Several forensic outcomes are integrated in the system such as matches of traces with persons, and links between scenes detected by the comparison of forensic case data. Systematic procedures have been settled to integrate links assumed mainly through DNA profiles, shoemarks patterns and images. A statistical outlook on a retrospective dataset of series from 2009 to 2011 of the database informs for instance on the number of repetition detected or confirmed and increased by forensic case data. Time needed to obtain forensic intelligence in regard with the type of marks treated, is seen as a critical issue. Furthermore, the underlying integration process of forensic intelligence into the crime intelligence database raised several difficulties in regards of the acquisition of data and the models used in the forensic databases. Solutions found and adopted operational procedures are described and discussed. This process form the basis to many other researches aimed at developing forensic intelligence models.

Hand detection in cluttered scene images using Fourier-Mellin invariant features

This paper proposes an automatic hand detection system that combines the Fourier-Mellin Transform along with other computer vision techniques to achieve hand detection in cluttered scene color images. The proposed system uses the Fourier-Mellin Transform as an invariant feature extractor to perform RST invariant hand detection. In a first stage of the system a simple non-adaptive skin color-based image segmentation and an interest point detector based on corners are used in order to identify regions of interest that contains possible matches. A sliding window algorithm is then used to scan the image at different scales performing the FMT calculations only in the previously detected regions of interest and comparing the extracted FM descriptor of the windows with a hand descriptors database obtained from a train image set. The results of the performed experiments suggest the use of Fourier-Mellin invariant features as a promising approach for automatic hand detection.

Hand detection in cluttered scene images using Fourier-Mellin invariant features

This paper proposes an automatic hand detection system that combines the Fourier-Mellin Transform along with other computer vision techniques to achieve hand detection in cluttered scene color images. The proposed system uses the Fourier-Mellin Transform as an invariant feature extractor to perform RST invariant hand detection. In a first stage of the system a simple non-adaptive skin color-based image segmentation and an interest point detector based on corners are used in order to identify regions of interest that contains possible matches. A sliding window algorithm is then used to scan the image at different scales performing the FMT calculations only in the previously detected regions of interest and comparing the extracted FM descriptor of the windows with a hand descriptors database obtained from a train image set. The results of the performed experiments suggest the use of Fourier-Mellin invariant features as a promising approach for automatic hand detection.

Georeferenced database generation with the purpose of hydrologic molding in reservoirs of the hydrographic basin of Jaguaribe river in the state of Ceará, Brazil

The edafoclimatic conditions of the Brazilian semiarid region favor the water loss by surface runoff. The state of Ceará, almost completely covered by semiarid, has developed public policies for the construction of dams in order to attend the varied water demand. Several hydrological models were developed to support decisive processes in the complex management of reservoirs. This study aimed to establish a methodology for obtaining a georeferenced database suitable for use as input data in hydrological modeling in the semiarid of Ceará. It was used images of Landsat satellite and SRTM Mission, and soil maps of the state of Ceará. The Landsat images allowed the determination of the land cover and the SRTM Mission images, the automatic delineation of hydrographic basins. The soil type was obtained through the soil map. The database was obtained for Jaguaribe River hydrographic basin, in the state of Ceará, and is applicable to hydrological modeling based on the Curve Number method for estimating the surface runoff.

Développement d'un indice de vulnérabilité à l'érosion éolienne à partir d'images satellitales, dans le Bassin arachidier du Sénégal : cas de la région de Thiès.

L’érosion éolienne est un problème environnemental parmi les plus sévères dans les régions arides, semi-arides et les régions sèches sub-humides de la planète. L’érosion des sols accélérée par le vent provoque des dommages à la fois localement et régionalement. Sur le plan local, elle cause la baisse des nutriments par la mobilisation des particules les plus fines et de la matière organique. Cette mobilisation est une des causes de perte de fertilité des sols avec comme conséquence, une chute de la productivité agricole et une réduction de la profondeur de la partie arable. Sur le plan régional, les tempêtes de poussières soulevées par le vent ont un impact non négligeable sur la santé des populations, et la déposition des particules affecte les équipements hydrauliques tels que les canaux à ciel ouvert ainsi que les infrastructures notamment de transport. Dans les régions où les sols sont fréquemment soumis à l’érosion éolienne, les besoins pour des études qui visent à caractériser spatialement les sols selon leur degré de vulnérabilité sont grands. On n’a qu’à penser aux autorités administratives qui doivent décider des mesures à prendre pour préserver et conserver les potentialités agropédologiques des sols, souvent avec des ressources financières modestes mises à leur disposition. Or, dans certaines de ces régions, comme notre territoire d’étude, la région de Thiès au Sénégal, ces études font défaut. En effet, les quelques études effectuées dans cette région ou dans des contextes géographiques similaires ont un caractère plutôt local et les approches suivies (modèles de pertes des sols) nécessitent un nombre substantiel de données pour saisir la variabilité spatiale de la dynamique des facteurs qui interviennent dans le processus de l’érosion éolienne. La disponibilité de ces données est particulièrement problématique dans les pays en voie de développement, à cause de la pauvreté en infrastructures et des problèmes de ressources pour le monitoring continu des variables environnementales. L’approche mise de l’avant dans cette recherche vise à combler cette lacune en recourant principalement à l’imagerie satellitale, et plus particulièrement celle provenant des satellites Landsat-5 et Landsat-7. Les images Landsat couvrent la presque totalité de la zone optique du spectre exploitable par télédétection (visible, proche infrarouge, infrarouge moyen et thermique) à des résolutions relativement fines (quelques dizaines de mètres). Elles permettant ainsi d’étudier la distribution spatiale des niveaux de vulnérabilité des sols avec un niveau de détails beaucoup plus fin que celui obtenu avec des images souvent utilisées dans des études environnementales telles que AVHRR de la série de satellites NOAA (résolution kilométrique). De plus, l’archive complet des images Landsat-5 et Landsat-7 couvrant une période de plus de 20 ans est aujourd’hui facilement accessible. Parmi les paramètres utilisés dans les modèles d’érosion éolienne, nous avons identifiés ceux qui sont estimables par l’imagerie satellitale soit directement (exemple, fraction du couvert végétal) soit indirectement (exemple, caractérisation des sols par leur niveau d’érodabilité). En exploitant aussi le peu de données disponibles dans la région (données climatiques, carte morphopédologique) nous avons élaboré une base de données décrivant l’état des lieux dans la période de 1988 à 2002 et ce, selon les deux saisons caractéristiques de la région : la saison des pluies et la saison sèche. Ces données par date d’acquisition des images Landsat utilisées ont été considérées comme des intrants (critères) dans un modèle empirique que nous avons élaboré en modulant l’impact de chacun des critères (poids et scores). À l’aide de ce modèle, nous avons créé des cartes montrant les degrés de vulnérabilité dans la région à l’étude, et ce par date d’acquisition des images Landsat. Suite à une série de tests pour valider la cohérence interne du modèle, nous avons analysé nos cartes afin de conclure sur la dynamique du processus pendant la période d’étude. Nos principales conclusions sont les suivantes : 1) le modèle élaboré montre une bonne cohérence interne et est sensible aux variations spatiotemporelles des facteurs pris en considération 2); tel qu’attendu, parmi les facteurs utilisés pour expliquer la vulnérabilité des sols, la végétation vivante et l’érodabilité sont les plus importants ; 3) ces deux facteurs présentent une variation importante intra et inter-saisonnière de sorte qu’il est difficile de dégager des tendances à long terme bien que certaines parties du territoire (Nord et Est) aient des indices de vulnérabilité forts, peu importe la saison ; 4) l’analyse diachronique des cartes des indices de vulnérabilité confirme le caractère saisonnier des niveaux de vulnérabilité dans la mesure où les superficies occupées par les faibles niveaux de vulnérabilité augmentent en saison des pluies, donc lorsque l’humidité surfacique et la végétation active notamment sont importantes, et décroissent en saison sèche ; 5) la susceptibilité, c’est-à-dire l’impact du vent sur la vulnérabilité est d’autant plus forte que la vitesse du vent est élevée et que la vulnérabilité est forte. Sur les zones où la vulnérabilité est faible, les vitesses de vent élevées ont moins d’impact. Dans notre étude, nous avons aussi inclus une analyse comparative entre les facteurs extraits des images Landsat et celles des images hyperspectrales du satellite expérimental HYPERION. Bien que la résolution spatiale de ces images soit similaire à celle de Landsat, les résultats obtenus à partir des images HYPERION révèlent un niveau de détail supérieur grâce à la résolution spectrale de ce capteur permettant de mieux choisir les bandes spectrales qui réagissent le plus avec le facteur étudié. Cette étude comparative démontre que dans un futur rapproché, l’amélioration de l’accessibilité à ce type d’images permettra de raffiner davantage le calcul de l’indice de vulnérabilité par notre modèle. En attendant cette possibilité, on peut de contenter de l’imagerie Landsat qui offre un support d’informations permettant tout de même d’évaluer le niveau de fragilisation des sols par l’action du vent et par la dynamique des caractéristiques des facteurs telles que la couverture végétale aussi bien vivante que sénescente.