A web-based framework for visualization, annotation, and automatic exploitation of high-resolution bioimages using tree-based machine learning methods

CYTOMIN

Segmentation of the breast region with pectoral muscle suppression and automatic breast density classification

Projecte final de carrera fet en col.laboració amb Université catholique de Louvain. Ecole Polytechnique de LouvainBreast cancer is one of the major causes of death among women. Nowadays screening mammography is the most adopted technique to perform an early breast cancer detection ahead other procedures like screen film mammography (SFM) or ultrasound scan. Computer assisted diagnosis (CAD) of mammograms attempts to help radiologists providing an automatic procedure to detect possible cancers in mammograms. Suspicious breast cancers appear as white spots in mammograms, indicating small clusters of micro-calcifications. Mammogram sensitivity decreases due some factors like density of the breast, presence of labels, artifacts or even pectoral muscle. The pre-processing of mammogram images is a very important step in the breast cancer analysis and detection because it might reduce the number of false positives. In this thesis we propose a method to segment and classify automatically mammograms according to their density. We perform several procedures including pre-processing (enhancement of the image, noise reduction, orientation finding or borders removal) and segmentation (separate the breast from the background, labels and pectoral muscle present in the mammograms) in order to increase the sensitivity of our CAD system. The final goal is the classification for diagnosis, in other words, finding the density class for an incoming mammography in order tot determine if more tests are needed to find possible cancers in the image. This functionality will be included in a new clinical imaging annotation system for computer aided breast cancer screening developed by the Communications and Remote Sensing Department at the Université Catholique de Louvain. The source code for the pre-processing and segmentation step has been programmed in C++ using the library of image processing ITK and CMake compiler. The performed method has been applied to medio-lateral oblique-view (MLO) mammograms as well as on caniocauldal mammograms (CC) belonging to different databases. The classification step has been implemented in Matlab. We have tested our pre-processing method obtaining a rate of 100% success removing labels and artifacts from mammograms of mini-MIAS database. The pectoral removal rate has been evaluated subjectively obtaining a rate of good removal of 57.76%. Finally, for the classification step, the best recognition rate that we have obtained was 76.25% using only pixel values, and 77.5% adding texture features, classifying images belonging to mini-MIAS database into 3 different density types. These results can be compared with the actual state of the art in segmentation and classification of biomedical images.El cáncer de mama es una de las mayores causas de muerte entre las mujeres. Actualmente, las mamografías digitales son la técnica más adoptada para realizar una previa detección de estos cánceres antes que otros procedimientos como "screen film mammography (SFM)" o escáneres de ultrasonidos. Los programas de diagnóstico automático (CAD) ayudan a los radiólogos proveyéndolos de un procedimiento automático para detectar posibles cánceres en las mamografías. Posibles cánceres aparecen en las mamografías como puntos blancos indicando pequeños grupos de micro-calcificaciones. La sensibilidad de las mamografias decrece debido a algunos factores como la densidad del pecho, presencia de etiquetas o artefactos o incluso de músculo pectoral. El pre-procesado de las mamografías es un paso muy importante en la detección de posibles cánceres de mama ya que puede reducir el número de falsos positivos. En esta tesis proponemos un método para segmentar y clasificar automáticamente las mamografías según su densidad. Hemos realizado varios procedimientos incluyendo, pre-procesado (realce de la imagen, reducción de ruido, descubrimiento de la orientación o supresión de bordes) y segmentación (separar el pecho de fondo, etiquetas y músculo pectoral presente en mamografías) para incrementar la sensibilidad de nuestro sistema CAD. El objetivo final es la clasificación para diagnosis, en otras palabras, encontrar la clase de densidad para una mamografía entrante y determinar si son necesarios más pruebas para encontrar posibles cánceres en las imágenes. Esta funcionalidad va a ser incluida en una nueva aplicación ara anotación de imágenes clínicas desarrollada por el Departamento de Comunicación y Detección Remota de la Universidad Católica de Lovaina. El código fuente para el pre-procesado y la segmentación ha sido desarrollado en C++ utilizando la librería de procesado de imagen ITK y el compilador CMake. El método implementado puede ser aplicado a tanto medio-lateral (MLO) como a caniocauldal mamografías (CC) pertenecientes a diferentes bases de datos. El método de clasificación ha sido implementado en Matlab. Hemos testeado nuestro método de pre-procesado obteniendo una tasa de suceso próxima al 100% en la eliminación de etiquetas y artefactos de la base de datos de mamografías mini-MIAS. La tasa de supresión de músculo pectoral ha sido evaluada de forma subjetiva obteniendo un 57.76%. Finalmente, en el método de clasificación se ha obtenido un 76.25% usando sólo información de los píxeles y un 77.5% usando información de texturas. Los resultados pueden ser comparados con el actual estado del arte en segmentación y clasificación de imágenes biomédicas.El càncer de mama és una de les majors causes de mort entre les dones. Actualment, les mamografies digitals són la tècnica més utilitzada per realitzar una prèvia detecció d'aquests càncers abans que altres procediments com "screen film mammography (SFM)" o escàners d'ultrasons. Els programes de diagnòstic automàtic (CAD) ajuden als radiòlegs proveïnt d'un procediment automàtic per detectar possibles càncers a les mamografies. Possibles càncers apareixen en les mamografies com punts blancs indicant petits grups de micro-calcificacions. La sensibilitat de les mamografies decreix a causa d'alguns factors com la densitat del pit, presència d'etiquetes o artefactes o fins i tot de múscul pectoral. El pre-processat de les mamografies és un pas molt important en la detecció de possibles càncers de mama ja que pot reduir el nombre de falsos positius. En aquesta tesi proposem un mètode per segmentar i classificar automàticament les mamografies segons la seva densitat. Hem realitzat diversos procediments incloent, pre-processat (realç de la imatge, reducció de soroll, descobriment de l'orientació o supressió de vores) i segmentació (separar el pit de fons, etiquetes i múscul pectoral present en mamografies) per incrementar la sensibilitat de nostre sistema CAD. L'objectiu final és la classificació per diagnosi, en altres paraules, trobar la classe de densitat per a una mamografia entrant i determinar si són necessaris més proves per trobar possibles càncers en les imatges. Aquesta funcionalitat serà inclosa en una nova aplicació ara anotació d'imatges clíniques desenvolupada pel Departament de Comunicació i Detecció Remota de la Universitat Catòlica de Lovaina. El codi font per al pre-processat i la segmentació ha estat desenvolupat en C + + utilitzant la llibreria de processat d'imatge ITK i el compilador CMake. El mètode implementat pot ser aplicat a tant mediolateral (MLO) com a caniocauldal mamografies (CC) pertanyents a diferents bases de dades. El mètode de classificació ha estat implementat en Matlab. Hem testejat el nostre mètode de pre-processat obtenint una taxa de succés propera al 100% en l'eliminació d'etiquetes i artefactes de la base de dades de mamografies mini-MIAS. La taxa de supressió de múscul pectoral ha estat avaluada de manera subjectiva obtenint un 57.76%. Finalment, en el mètode de classificació s'ha obtingut un 76.25% usant només informació dels píxels i un 77.5% usant informació de textures. Els resultats poden ser comparats amb l'actual estat de l'art en segmentació i classificació d'imatges biomèdiques

Feature evaluation for building facade images - an empirical study

The classification of building facade images is a challenging problem that receives a great deal of attention in the photogrammetry community. Image classification is critically dependent on the features. In this paper, we perform an empirical feature evaluation task for building facade images. Feature sets we choose are basic features, color features, histogram features, Peucker features, texture features, and SIFT features. We present an approach for region-wise labeling using an efficient randomized decision forest classifier and local features. We conduct our experiments with building facade image classification on the eTRIMS dataset, where our focus is the object classes building, car, door, pavement, road, sky, vegetation, and window

Random clustering ferns for multimodal object recognition

The final publication is available at link.springer.comWe propose an efficient and robust method for the recognition of objects exhibiting multiple intra-class modes, where each one is associated with a particular object appearance. The proposed method, called random clustering ferns, combines synergically a single and real-time classifier, based on the boosted assembling of extremely randomized trees (ferns), with an unsupervised and probabilistic approach in order to recognize efficiently object instances in images and discover simultaneously the most prominent appearance modes of the object through tree-structured visual words. In particular, we use boosted random ferns and probabilistic latent semantic analysis to obtain a discriminative and multimodal classifier that automatically clusters the response of its randomized trees in function of the visual object appearance. The proposed method is validated extensively in synthetic and real experiments, showing that the method is capable of detecting objects with diverse and complex appearance distributions in real-time performance.Peer ReviewedPostprint (author's final draft

Learning Pose Invariant and Covariant Classifiers from Image Sequences

Object tracking and detection over a wide range of viewpoints is a long-standing problem in Computer Vision. Despite significant advance in wide-baseline sparse interest point matching and development of robust dense feature models, it remains a largely open problem. Moreover, abundance of low cost mobile platforms and novel application areas, such as real-time Augmented Reality, constantly push the performance limits of existing methods. There is a need to modify and adapt these to meet more stringent speed and capacity requirements. In this thesis, we aim to overcome the difficulties due to the multi-view nature of the object detection task. We significantly improve upon existing statistical keypoint matching algorithms to perform fast and robust recognition of image patches independently of object pose. We demonstrate this on various 2D and 3D datasets. The statistical keypoint matching approaches require massive amounts of training data covering a wide range of viewpoints. We have developed a weakly supervised algorithm to greatly simplify their training for 3D objects. We also integrate this algorithm in a 3D tracking-by-detection system to perform real-time Augmented Reality. Finally, we extend the use of a large training set with smooth viewpoint variation to category-level object detection. We introduce a new dataset with continuous pose annotations which we use to train pose estimators for objects of a single category. By using these estimators' output to select pose specific classifiers, our framework can simultaneously localize objects in an image and recover their pose. These decoupled pose estimation and classification steps yield improved detection rates. Overall, we rely on image and video sequences to train classifiers that can either operate independently of the object pose or recover the pose parameters explicitly. We show that in both cases our approaches mitigate the effects of viewpoint changes and improve the recognition performance

Uncertainty Quantification of the CO2 Storage Process in the Bunter Closure 36 Model

The UK plans to bring all greenhouse gas emissions to net-zero by 2050. Carbon capture and storage (CCS), an important strategy to reduce global CO2 emissions, is one of the critical objectives of this UK net-zero plan. Among the possible storage site options, saline aquifers are one of the most promising candidates for long-term CO2 sequestrations. Despite its promising potential, few studies have been conducted on the CO2 storage process in the Bunter Closure 36 model located off the eastern shore of the UK. Located amid a number of oil fields, Bunter is one of the primary candidates for CO2 storage in the UK, with plans to store more than 280 Mt of CO2 from injections starting in 2027. As saline aquifers are usually sparsely drilled with minimal dynamic data, any model is subject to a level of uncertainty. This is the first study on the impact of the model and fluid uncertainties on the CO2 storage process in Bunter. This study attempted to fully accommodate the uncertainty space on Bunter by performing twenty thousand forward simulations using a vertical equilibrium-based simulator. The joint impact of five uncertain parameters using data-driven models was analysed. The results of this work will improve our understanding of the carbon storage process in the Bunter model before the injection phase is initiated. Due to the complexity of the model, it is not recommended to make a general statement about the influence of a single variable on CO2 plume migration in the Bunter model. The reservoir temperature was shown to have the most impact on the plume dynamics (overall importance of 41%), followed by pressure (21%), permeability (17%), elevation (13%), and porosity (8%), respectively. The results also showed that a lower temperature and higher pressure in the Bunter reservoir condition would result in a higher density and, consequently, a higher structural capacity

Efficient extraction of semantic information from medical images in large datasets using random forests

Large datasets of unlabelled medical images are increasingly becoming available; however only a small subset tend to be manually semantically labelled as it is a tedious and extremely time-consuming task to do for large datasets. This thesis aims to tackle the problem of efficiently extracting semantic information in the form of image segmentations and organ localisations from large datasets of unlabelled medical images. To do so, we investigate the suitability of supervoxels and random classification forests for the task. The first contribution of this thesis is a novel method for efficiently estimating coarse correspondences between pairs of images that can handle difficult cases that exhibit large variations in fields of view. The proposed methods adapts the random forest framework, which is a supervised learning algorithm, to work in an unsupervised manner by automatically generating labels for training via the use of supervoxels. The second contribution of this thesis is a method that extends our first contribution so as to be applicable efficiently on a large dataset of images. The proposed method is efficient and can be used to obtain correspondences between a large number of object-like supervoxels that are representative of organ structures in the images. The method is evaluated for the applications of organ-based image retrieval and weakly-supervised image segmentation using extremely minimal user input. While the method does not achieve image segmentation accuracies for all organs in an abdominal CT dataset compared to current fully-supervised state-of-the-art methods, it does provide a promising way for efficiently extracting and parsing a large dataset of medical images for the purpose of further processing.Open Acces