Automated Segmentation of Cells with IHC Membrane Staining

This study presents a fully automated membrane segmentation technique for immunohistochemical tissue images with membrane staining, which is a critical task in computerized immunohistochemistry (IHC). Membrane segmentation is particularly tricky in immunohistochemical tissue images because the cellular membranes are visible only in the stained tracts of the cell, while the unstained tracts are not visible. Our automated method provides accurate segmentation of the cellular membranes in the stained tracts and reconstructs the approximate location of the unstained tracts using nuclear membranes as a spatial reference. Accurate cell-by-cell membrane segmentation allows per cell morphological analysis and quantification of the target membrane proteins that is fundamental in several medical applications such as cancer characterization and classification, personalized therapy design, and for any other applications requiring cell morphology characterization. Experimental results on real datasets from different anatomical locations demonstrate the wide applicability and high accuracy of our approach in the context of IHC analysi

Outlier Mining Methods Based on Graph Structure Analysis

Outlier detection in high-dimensional datasets is a fundamental and challenging problem across disciplines that has also practical implications, as removing outliers from the training set improves the performance of machine learning algorithms. While many outlier mining algorithms have been proposed in the literature, they tend to be valid or efficient for specific types of datasets (time series, images, videos, etc.). Here we propose two methods that can be applied to generic datasets, as long as there is a meaningful measure of distance between pairs of elements of the dataset. Both methods start by defining a graph, where the nodes are the elements of the dataset, and the links have associated weights that are the distances between the nodes. Then, the first method assigns an outlier score based on the percolation (i.e., the fragmentation) of the graph. The second method uses the popular IsoMap non-linear dimensionality reduction algorithm, and assigns an outlier score by comparing the geodesic distances with the distances in the reduced space. We test these algorithms on real and synthetic datasets and show that they either outperform, or perform on par with other popular outlier detection methods. A main advantage of the percolation method is that is parameter free and therefore, it does not require any training; on the other hand, the IsoMap method has two integer number parameters, and when they are appropriately selected, the method performs similar to or better than all the other methods tested.Peer ReviewedPostprint (published version

Contextual Detection of Anomalies in Hyperspectral Images

The majority of anomaly detectors in Hyperspectral Imaging use only the statistical aspects of the spectral readings in the image. These detectors fail to use the spatial context that is contained in the images. The use of this information can yield detectors that out perform their spatially myopic counterparts. To demonstrate this, we will use an independent component analysis based detector, autonomous global anomaly detector (AutoGAD), developed at AFIT augmented to account for the spatial context of the detected anomalies. Through the use of segmentation algorithms, the anomalies identified are formed into regions. The size and shape of these regions are then used to decide if the region is anomalous or not. A Bayesian Belief Network structure is used to update a posterior probability of the region being anomalous

A taxonomy framework for unsupervised outlier detection techniques for multi-type data sets

The term "outlier" can generally be defined as an observation that is significantly different from the other values in a data set. The outliers may be instances of error or indicate events. The task of outlier detection aims at identifying such outliers in order to improve the analysis of data and further discover interesting and useful knowledge about unusual events within numerous applications domains. In this paper, we report on contemporary unsupervised outlier detection techniques for multiple types of data sets and provide a comprehensive taxonomy framework and two decision trees to select the most suitable technique based on data set. Furthermore, we highlight the advantages, disadvantages and performance issues of each class of outlier detection techniques under this taxonomy framework

Machine learning methods for the characterization and classification of complex data

This thesis work presents novel methods for the analysis and classification of medical images and, more generally, complex data. First, an unsupervised machine learning method is proposed to order anterior chamber OCT (Optical Coherence Tomography) images according to a patient's risk of developing angle-closure glaucoma. In a second study, two outlier finding techniques are proposed to improve the results of above mentioned machine learning algorithm, we also show that they are applicable to a wide variety of data, including fraud detection in credit card transactions. In a third study, the topology of the vascular network of the retina, considering it a complex tree-like network is analyzed and we show that structural differences reveal the presence of glaucoma and diabetic retinopathy. In a fourth study we use a model of a laser with optical injection that presents extreme events in its intensity time-series to evaluate machine learning methods to forecast such extreme events.El presente trabajo de tesis desarrolla nuevos métodos para el análisis y clasificación de imágenes médicas y datos complejos en general. Primero, proponemos un método de aprendizaje automático sin supervisión que ordena imágenes OCT (tomografía de coherencia óptica) de la cámara anterior del ojo en función del grado de riesgo del paciente de padecer glaucoma de ángulo cerrado. Luego, desarrollamos dos métodos de detección automática de anomalías que utilizamos para mejorar los resultados del algoritmo anterior, pero que su aplicabilidad va mucho más allá, siendo útil, incluso, para la detección automática de fraudes en transacciones de tarjetas de crédito. Mostramos también, cómo al analizar la topología de la red vascular de la retina considerándola una red compleja, podemos detectar la presencia de glaucoma y de retinopatía diabética a través de diferencias estructurales. Estudiamos también un modelo de un láser con inyección óptica que presenta eventos extremos en la serie temporal de intensidad para evaluar diferentes métodos de aprendizaje automático para predecir dichos eventos extremos.Aquesta tesi desenvolupa nous mètodes per a l’anàlisi i la classificació d’imatges mèdiques i dades complexes. Hem proposat, primer, un mètode d’aprenentatge automàtic sense supervisió que ordena imatges OCT (tomografia de coherència òptica) de la cambra anterior de l’ull en funció del grau de risc del pacient de patir glaucoma d’angle tancat. Després, hem desenvolupat dos mètodes de detecció automàtica d’anomalies que hem utilitzat per millorar els resultats de l’algoritme anterior, però que la seva aplicabilitat va molt més enllà, sent útil, fins i tot, per a la detecció automàtica de fraus en transaccions de targetes de crèdit. Mostrem també, com en analitzar la topologia de la xarxa vascular de la retina considerant-la una xarxa complexa, podem detectar la presència de glaucoma i de retinopatia diabètica a través de diferències estructurals. Finalment, hem estudiat un làser amb injecció òptica, el qual presenta esdeveniments extrems en la sèrie temporal d’intensitat. Hem avaluat diferents mètodes per tal de predir-los.Postprint (published version

The Ecology and Behavior of New Chimpanzee Mothers at Ngogo, Kibale National Park, Uganda.

Female mammals invest heavily in reproduction. Females require food to reproduce and thus mothers compete for food. In group-living species, females who form strong intrasexual social bonds reproduce more than females who do not. To reproduce successfully, females must meet both nutritional and social needs. Although mothers face certain challenges involving competition and affiliation, not all mothers are the same. One factor, parity, affects females across taxa. Due to inexperience, primiparas, mothers raising their first offspring, face challenges not experienced by multiparas, mothers with multiple offspring. Therefore, primiparas behave differently than multiparas. Chimpanzees are an excellent species to investigate whether behavior varies with parity. Chimpanzees are long-lived and primiparas live with multiparas in multi-female, multi-male, fission-fusion communities. I investigated the relationship between parity and behavior by conducting a 15-month study of female chimpanzees living in the community at Ngogo, Kibale National Park, Uganda. During the study, several females gave birth to their first offspring. This produced an ideal situation to compare the behavior of mothers who differed in parity, testing the prediction that primiparas and multiparas behave differently. To determine how females compete with one another, I examined ranging and intrasexual aggression, which influence food access. To examine female social bonds, I analyzed observations of association and grooming. Females at Ngogo utilized small, overlapping ranges within the community territory. Range sizes differed, but parity did not predict this variation. As reported elsewhere, mothers were often aggressive toward adolescent nulliparas. Female aggression varied with parity; primiparas displayed more aggression toward adolescents than did multiparas. Regarding affiliation, mothers mainly associated and groomed with mothers, rather than with adolescent nulliparas. Examining mothers by parity class showed that primiparas groomed with adolescent nulliparas more than did multiparas. These results indicate that behavioral differences existed between primiparous and multiparous female chimpanzees. These differences involved mothers’ social interactions with adolescent nulliparas in competitive and affiliative contexts. These results emphasize the importance of examining parity because considering mothers as a single category can mask behavioral variation. This indicates the importance of examining the lives of primiparas in order to understand how evolution has influenced the mothers’ behavior.PhDAnthropologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120683/1/bkaye_1.pd

Inferring Anomalies from Data using Bayesian Networks

Existing studies on data mining has largely focused on the design of measures and algorithms to identify outliers in large and high dimensional categorical and numeric databases. However, not much stress has been given on the interestingness of the reported outlier. One way to ascertain interestingness and usefulness of the reported outlier is by making use of domain knowledge. In this thesis, we present measures to discover outliers based on background knowledge, represented by a Bayesian network. Using causal relationships between attributes encoded in the Bayesian framework, we demonstrate that meaningful outliers, i.e., outliers which encode important or new information are those which violate causal relationships encoded in the model. Depending upon nature of data, several approaches are proposed to identify and explain anomalies using Bayesian knowledge. Outliers are often identified as data points which are ``rare'', ''isolated'', or ''far away from their nearest neighbors''. We show that these characteristics may not be an accurate way of describing interesting outliers. Through a critical analysis on several existing outlier detection techniques, we show why there is a mismatch between outliers as entities described by these characteristics and ``real'' outliers as identified using Bayesian approach. We show that the Bayesian approaches presented in this thesis has better accuracy in mining genuine outliers while, keeping a low false positive rate as compared to traditional outlier detection techniques

Caracterización de la alta troposfera-baja estratosfera (UTLS) subtropical: tropopausa y distribución vertical de ozono

En los últimos años se ha prestado un gran interés a la región atmosférica en torno a la tropopausa. En gran parte, este resurgimiento de los estudios sobre la tropopausa ha sido motivado por el reconocimiento del importante papel que la tropopausa desempeña en un buen número de tópicos. Así, por ejemplo, aparece estrechamente relacionada con cambios en el ozono estratosférico, parece ser un buen indicador del cambio climático y aparece como un elemento activo en el intercambio de varias especies químicas entre la estratosfera y la troposfera. Este estudio busca adquirir un conocimiento preciso de la estructura espacial y temporal de la tropopausa con el fin de mejorar nuestra comprensión sobre una serie de fenómenos relacionados con el clima y la química atmosférica global