Chest Breadths to Predict Individuals' Age - A Case Based View

It is well known that rib cage dimensions depend on the gender and vary with the age of the individual. Under this setting it is therefore possible to assume that a computational approach to the problem may be thought out and, consequently, this work will focus on the development of an Artificial Intelligence grounded decision support system to predict individual’s age, based on such measurements. On the one hand, using some basic image processing techniques it were extracted such descriptions from chest X-rays (i.e., its maximum width and height). On the other hand, the computational framework was built on top of a Logic Programming Case Base approach to knowledge representation and reasoning, which caters for the handling of incomplete, unknown, or even contradictory information. Furthermore, clustering methods based on similarity analysis among cases were used to distinguish and aggregate collections of historical data in order to reduce the search space, therefore enhancing the cases retrieval and the overall computational process. The accuracy of the proposed model is satisfactory, close to 90%.

An Artificial Intelligence Approach to Individuals' Age Assessment

A link between patterns of pelvic growth and human life history is supported by the finding that, cross-culturally, variation in maturation rates of female pelvis are correlated with variation in ages of menarche and first reproduction, i.e., it is well known that the human dimensions of the pelvic bones depend on the gender and vary with the age. Indeed, one feature in which humans appear to be unique is the prolonged growth of the pelvis after the age of sexual maturity. Both the total superoinferior length and mediolateral breadth of the pelvis continues to grow markedly after puberty, and do not reach adult proportions until the late teens years. This continuation of growth is accomplished by relatively late fusion of the separate centers of ossification that form the bones of the pelvis. Hence, in this work we will focus on the development of an intelligent decision support system to predict individual’s age based on a pelvis' dimensions criteria. Some basic image processing techniques were applied in order to extract the relevant features from pelvic X-rays, being the computational framework built on top of a Logic Programming approach to Knowledge Representation and Reasoning that caters for the handling of incomplete, unknown, or even self-contradictory information, complemented with a Case Base approach to computing.

Detection of Glass pattern configurations by the primary visual cortex

The neurons in the primary visual cortex that respond to the orientation of visual stimuli were discovered in the late 1950s (Hubel, D.H. & Wiesel, T.N. 1959. J. Physiol. 148:574-591) but how they achieve this response is poorly understood. Recently, experiments have demonstrated that the visual cortex may use the image processing techniques of cross or auto-correlation to detect the streaks in random dot patterns (Barlow, H. & Berry, D.L. 2010. Proc. R. Soc. B. 278: 2069-2075). These experiments made use of sinusoidally modulated random dot patterns and of the so-called Glass patterns - where randomly positioned dot pairs are oriented in a parallel configuration (Glass, L. 1969. Nature. 223: 578-580). The image processing used by the visual cortex could be inferred from how the threshold of detection of these patterns in the presence of random noise varied as a function of the dot density in the patterns. In the present study, the detection thresholds have been measured for other types of patterns including circular, hyperbolic, spiral and radial Glass patterns and an indication of the type of image processing (cross or auto-correlation) by the visual cortex is presented. As a result, it is hoped that this study will contribute to an understanding of what David Marr called the ‘computational goal’ of the primary visual cortex (Marr, D. 1982. Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. New York: Freeman.)

Processamento e classificação de imagens biológicas: aplicação à histologia

A Histologia, o estudo de tecidos, é uma das áreas fundamentais da Biologia que permitiu enormes avanços científicos. Sendo uma tarefa exigente, meticulosa e demorada, será importante aproveitar a existência de ferramentas e algoritmos computacionais no seu auxílio, tornando o processo mais rápido e possibilitando a descoberta de informação que poderá não estar visível à partida. Esta dissertação tem como principal objectivo averiguar se um animal foi ou não sujeito à ingestão de um xenobiótico. Com esse objectivo em vista, utilizaram-se técnicas de processamento e segmentação de imagem aplicadas a imagens de tecido renal de ratos saudáveis e ratos que ingeriram o xenobiótico. Destas imagens extraíram-se inúmeras características do corpúsculo renal que após serem analisadas através de vários algoritmos de classificação mostraram ser possível saber se o animal ingeriu ou não o xenobiótico, com um reduzido grau de incerteza. ABSTRACT: Histology, the study of tissues, is one of the key areas of Biology that has allowed huge advances in Science. Being a demanding, meticulous and time consuming task, it is important to use the existence of computational tools and algorithms in its aid, making the process faster and enabling the discovery of information that may not be initially visible. The main goal of this thesis is to ascertain if an animal was subjected or not to the ingestion of a xenobiotic. With this in mind, were used image processing and segmentation techniques applied on images of kidney tissue from healthy rats and rats that ingested the xenobiotic. From these images were extracted several features of renal glomeruli that after being analyzed by various classification algorithms had shown to be possible to know, with an acceptable degree of certainty, if the animal ingested or not the xenobiotic.