Atlas-based Segmentation of Tumor-bearing Brain Images

In diagnostic neuroradiology as well as in radiation oncology and neurosurgery, there is an increasing demand for accurate segmentation of tumor-bearing brain images. Atlas-based segmentation is an appealing automatic technique thanks to its robustness and versatility. However, atlas-based segmentation of tumor-bearing brain images is challenging due to the confounding effects of the tumor in the patient image. In this article, we provide a brief background on brain tumor imaging and introduce the clinical perspective, before we categorize and review the state of the art in the current literature on atlas-based segmentation for tumor-bearing brain images. We also present selected methods and results from our own research in more detail. Finally, we conclude with a short summary and look at new developments in the field, including requirements for future routine clinical use.

Assessment of Transverse Isotropy in Clinical-Level CT Images of Trabecular Bone Using the Gradient Structure Tensor

The aim of this study was to develop a GST-based methodology for accurately measuring the degree of transverse isotropy in trabecular bone. Using femoral sub-regions scanned in high-resolution peripheral QCT (HR-pQCT) and clinical-level-resolution QCT, trabecular orientation was evaluated using the mean intercept length (MIL) and the gradient structure tensor (GST) on the HR-pQCT and QCT data, respectively. The influence of local degree of transverse isotropy (DTI) and bone mineral density (BMD) was incorporated into the investigation. In addition, a power based model was derived, rendering a 1:1 relationship between GST and MIL eigenvalues. A specific DTI threshold (DTI thres) was found for each investigated size of region of interest (ROI), above which the estimate of major trabecular direction of the GST deviated no more than 30° from the gold standard MIL in 95% of the remaining ROIs (mean error: 16°). An inverse relationship between ROI size and DTI thres was found for discrete ranges of BMD. A novel methodology has been developed, where transversal isotropic measures of trabecular bone can be obtained from clinical QCT images for a given ROI size, DTI thres and power coefficient. Including DTI may improve future clinical QCT finite-element predictions of bone strength and diagnoses of bone disease.

Quantification of nanoparticles at the single-cell level: an overview about state-of-the-art techniques and their limitations.

With the increasing production and use of engineered nanoparticles it is crucial that their interaction with biological systems is understood. Due to the small size of nanoparticles, their identification and localization within single cells is extremely challenging. Therefore, various cutting-edge techniques are required to detect and to quantify metals, metal oxides, magnetic, fluorescent, as well as electron-dense nanoparticles. Several techniques will be discussed in detail, such as inductively coupled plasma atomic emission spectroscopy, flow cytometry, laser scanning microscopy combined with digital image restoration, as well as quantitative analysis by means of stereology on transmission electron microscopy images. An overview will be given regarding the advantages of those visualization/quantification systems, including a thorough discussion about limitations and pitfalls.

Localization and Segmentation of 3D Intervertebral Discs in MR Images by Data Driven Estimation

This paper addresses the problem of fully-automatic localization and segmentation of 3D intervertebral discs (IVDs) from MR images. Our method contains two steps, where we first localize the center of each IVD, and then segment IVDs by classifying image pixels around each disc center as foreground (disc) or background. The disc localization is done by estimating the image displacements from a set of randomly sampled 3D image patches to the disc center. The image displacements are estimated by jointly optimizing the training and test displacement values in a data-driven way, where we take into consideration both the training data and the geometric constraint on the test image. After the disc centers are localized, we segment the discs by classifying image pixels around disc centers as background or foreground. The classification is done in a similar data-driven approach as we used for localization, but in this segmentation case we are aiming to estimate the foreground/background probability of each pixel instead of the image displacements. In addition, an extra neighborhood smooth constraint is introduced to enforce the local smoothness of the label field. Our method is validated on 3D T2-weighted turbo spin echo MR images of 35 patients from two different studies. Experiments show that compared to state of the art, our method achieves better or comparable results. Specifically, we achieve for localization a mean error of 1.6-2.0 mm, and for segmentation a mean Dice metric of 85%-88% and a mean surface distance of 1.3-1.4 mm.

Automatic assessment of time-resolved OCT images for selective retina therapy

Purpose In recent years, selective retina laser treatment (SRT), a sub-threshold therapy method, avoids widespread damage to all retinal layers by targeting only a few. While these methods facilitate faster healing, their lack of visual feedback during treatment represents a considerable shortcoming as induced lesions remain invisible with conventional imaging and make clinical use challenging. To overcome this, we present a new strategy to provide location-specific and contact-free automatic feedback of SRT laser applications. Methods We leverage time-resolved optical coherence tomography (OCT) to provide informative feedback to clinicians on outcomes of location-specific treatment. By coupling an OCT system to SRT treatment laser, we visualize structural changes in the retinal layers as they occur via time-resolved depth images. We then propose a novel strategy for automatic assessment of such time-resolved OCT images. To achieve this, we introduce novel image features for this task that when combined with standard machine learning classifiers yield excellent treatment outcome classification capabilities. Results Our approach was evaluated on both ex vivo porcine eyes and human patients in a clinical setting, yielding performances above 95 % accuracy for predicting patient treatment outcomes. In addition, we show that accurate outcomes for human patients can be estimated even when our method is trained using only ex vivo porcine data. Conclusion The proposed technique presents a much needed strategy toward noninvasive, safe, reliable, and repeatable SRT applications. These results are encouraging for the broader use of new treatment options for neovascularization-based retinal pathologies.

"Contemporary Art and Anthropology". De Arnd Schneider y Christopher Wright (Editores). En: Avá, nº 9

Entre Arte e Antropologia: diálogos e apropriações A relação entre Arte e Ciência é um dos temas dos mais complexos e evolutivos, sempre dependendo do contexto histórico e do objetivo desta "relação". Por isto, resolvi começar esta resenha do livro ContemporaryArt and Anthropology com uma visão ampla e clássica deste problema epistemológico .

Microstructural segmentation of 3D images of alsphalt specimen using Matlab engine

En este proyecto se ha desarrollado un código de MATLAB para el procesamiento de imágenes tomográficas 3D, de muestras de asfalto de carreteras en Polonia. Estas imágenes en 3D han sido tomadas por un equipo de investigación de la Universidad Tecnológica de Lodz (LUT). El objetivo de este proyecto es crear una herramienta que se pueda utilizar para estudiar las diferentes muestras de asfalto 3D y pueda servir para estudiar las pruebas de estrés que experimentan las muestras en el laboratorio. Con el objetivo final de encontrar soluciones a la degradación sufrida en las carreteras de Polonia, debido a diferentes causas, como son las condiciones meteorológicas. La degradación de las carreteras es un tema que se ha investigado desde hace muchos años, debido a la fuerte degradación causada por diferentes factores como son climáticos, la falta de mantenimiento o el tráfico excesivo en algunos casos. Es en Polonia, donde estos tres factores hacen que la composición de muchas carreteras se degrade rápidamente, sobre todo debido a las condiciones meteorológicas sufridas a lo largo del año, con temperaturas que van desde 30° C en verano a -20° C en invierno. Esto hace que la composición de las carreteras sufra mucho y el asfalto se levante, lo que aumenta los costos de mantenimiento y los accidentes de carretera. Este proyecto parte de la base de investigación que se lleva a cabo en la LUT, tratando de mejorar el análisis de las muestras de asfalto, por lo que se realizarán las pruebas de estrés y encontrar soluciones para mejorar el asfalto en las carreteras polacas. Esto disminuiría notablemente el costo de mantenimiento. A pesar de no entrar en aspectos muy técnicos sobre el asfalto y su composición, se ha necesitado realizar un estudio profundo sobre todas sus características, para crear un código capaz de obtener los mejores resultados. Por estas razones, se ha desarrollado en Matlab, los algoritmos que permiten el estudio de los especímenes 3D de asfalto. Se ha utilizado este software, ya que Matlab es una poderosa herramienta matemática que permite operar con matrices para realización de operaciones rápidamente, permitiendo desarrollar un código específico para el tratamiento y procesamiento de imágenes en 3D. Gracias a esta herramienta, estos algoritmos realizan procesos tales como, la segmentación de la imagen 3D, pre y post procesamiento de la imagen, filtrado o todo tipo de análisis microestructural de las muestras de asfalto que se están estudiando. El código presentado para la segmentación de las muestras de asfalto 3D es menos complejo en su diseño y desarrollo, debido a las herramientas de procesamiento de imágenes que incluye Matlab, que facilitan significativamente la tarea de programación, así como el método de segmentación utilizado. Respecto al código, este ha sido diseñado teniendo en cuenta el objetivo de facilitar el trabajo de análisis y estudio de las imágenes en 3D de las muestras de asfalto. Por lo tanto, el principal objetivo es el de crear una herramienta para el estudio de este código, por ello fue desarrollado para que pueda ser integrado en un entorno visual, de manera que sea más fácil y simple su utilización. Ese es el motivo por el cual todos estos algoritmos y funciones, que ha sido desarrolladas, se integrarán en una herramienta visual que se ha desarrollado con el GUIDE de Matlab. Esta herramienta ha sido creada en colaboración con Jorge Vega, y fue desarrollada en su proyecto final de carrera, cuyo título es: Segmentación microestructural de Imágenes en 3D de la muestra de asfalto utilizando Matlab. En esta herramienta se ha utilizado todo las funciones programadas en este proyecto, y tiene el objetivo de desarrollar una herramienta que permita crear un entorno gráfico intuitivo y de fácil uso para el estudio de las muestras de 3D de asfalto. Este proyecto se ha dividido en 4 capítulos, en un primer lugar estará la introducción, donde se presentarán los aspectos más importante que se va a componer el proyecto. En el segundo capítulo se presentarán todos los datos técnicos que se han tenido que estudiar para desarrollar la herramienta, entre los que cabe los tres temas más importantes que se han estudiado en este proyecto: materiales asfálticos, los principios de la tomografías 3D y el procesamiento de imágenes. Esta será la base para el tercer capítulo, que expondrá la metodología utilizada en la elaboración del código, con la explicación del entorno de trabajo utilizado en Matlab y todas las funciones de procesamiento de imágenes utilizadas. Además, se muestra todo el código desarrollado, así como una descripción teórica de los métodos utilizados para el pre-procesamiento y segmentación de las imagenes en 3D. En el capítulo 4, se mostrarán los resultados obtenidos en el estudio de una de las muestras de asfalto, y, finalmente, el último capítulo se basa en las conclusiones sobre el desarrollo de este proyecto. En este proyecto se ha llevado han realizado todos los puntos que se establecieron como punto de partida en el anteproyecto para crear la herramienta, a pesar de que se ha dejado para futuros proyectos nuevas posibilidades de este codigo, como por ejemplo, la detección automática de las diferentes regiones de una muestra de asfalto debido a su composición. Como se muestra en este proyecto, las técnicas de procesamiento de imágenes se utilizan cada vez más en multitud áreas, como pueden ser industriales o médicas. En consecuencia, este tipo de proyecto tiene multitud de posibilidades, y pudiendo ser la base para muchas nuevas aplicaciones que se puedan desarrollar en un futuro. Por último, se concluye que este proyecto ha contribuido a fortalecer las habilidades de programación, ampliando el conocimiento de Matlab y de la teoría de procesamiento de imágenes. Del mismo modo, este trabajo proporciona una base para el desarrollo de un proyecto más amplio cuyo alcance será una herramienta que puedas ser utilizada por el equipo de investigación de la Universidad Tecnológica de Lodz y en futuros proyectos. ABSTRACT In this project has been developed one code in MATLAB to process X-ray tomographic 3D images of asphalt specimens. These images 3D has been taken by a research team of the Lodz University of Technology (LUT). The aim of this project is to create a tool that can be used to study differents asphalt specimen and can be used to study them after stress tests undergoing the samples. With the final goal to find solutions to the degradation suffered roads in Poland due to differents causes, like weather conditions. The degradation of the roads is an issue that has been investigated many years ago, due to strong degradation suffered caused by various factors such as climate, poor maintenance or excessive traffic in some cases. It is in Poland where these three factors make the composition of many roads degrade rapidly, especially due to the weather conditions suffered along the year, with temperatures ranging from 30 o C in summer to -20 ° C in winter. This causes the roads suffers a lot and asphalt rises shortly after putting, increasing maintenance costs and road accident. This project part of the base that research is taking place at the LUT, in order to better analyze the asphalt specimens, they are tested for stress and find solutions to improve the asphalt on Polish roads. This would decrease remarkable maintenance cost. Although this project will not go into the technical aspect as asphalt and composition, but it has been required a deep study about all of its features, to create a code able to obtain the best results. For these reasons, there have been developed in Matlab, algorithms that allow the study of 3D specimens of asphalt. Matlab is a powerful mathematical tool, which allows arrays operate fastly, allowing to develop specific code for the treatment and processing of 3D images. Thus, these algorithms perform processes such as the multidimensional matrix sgementation, pre and post processing with the same filtering algorithms or microstructural analysis of asphalt specimen which being studied. All these algorithms and function that has been developed to be integrated into a visual tool which it be developed with the GUIDE of Matlab. This tool has been created in the project of Jorge Vega which name is: Microstructural segmentation of 3D images of asphalt specimen using Matlab engine. In this tool it has been used all the functions programmed in this project, and it has the aim to develop an easy and intuitive graphical environment for the study of 3D samples of asphalt. This project has been divided into 4 chapters plus the introduction, the second chapter introduces the state-of-the-art of the three of the most important topics that have been studied in this project: asphalt materials, principle of X-ray tomography and image processing. This will be the base for the third chapter, which will outline the methodology used in developing the code, explaining the working environment of Matlab and all the functions of processing images used. In addition, it will be shown all the developed code created, as well as a theoretical description of the methods used for preprocessing and 3D image segmentation. In Chapter 4 is shown the results obtained from the study of one of the specimens of asphalt, and finally the last chapter draws the conclusions regarding the development of this project.

Learning 3D structure from 2D images using LBP features

An automatic machine learning strategy for computing the 3D structure of monocular images from a single image query using Local Binary Patterns is presented. The 3D structure is inferred through a training set composed by a repository of color and depth images, assuming that images with similar structure present similar depth maps. Local Binary Patterns are used to characterize the structure of the color images. The depth maps of those color images with a similar structure to the query image are adaptively combined and filtered to estimate the final depth map. Using public databases, promising results have been obtained outperforming other state-of-the-art algorithms and with a computational cost similar to the most efficient 2D-to-3D algorithms.

Age regression from soft aligned face images using low computational resources

The initial step in most facial age estimation systems consists of accurately aligning a model to the output of a face detector (e.g. an Active Appearance Model). This fitting process is very expensive in terms of computational resources and prone to get stuck in local minima. This makes it impractical for analysing faces in resource limited computing devices. In this paper we build a face age regressor that is able to work directly on faces cropped using a state-of-the-art face detector. Our procedure uses K nearest neighbours (K-NN) regression with a metric based on a properly tuned Fisher Linear Discriminant Analysis (LDA) projection matrix. On FG-NET we achieve a state-of-the-art Mean Absolute Error (MAE) of 5.72 years with manually aligned faces. Using face images cropped by a face detector we get a MAE of 6.87 years in the same database. Moreover, most of the algorithms presented in the literature have been evaluated on single database experiments and therefore, they report optimistically biased results. In our cross-database experiments we get a MAE of roughly 12 years, which would be the expected performance in a real world application.