Posterior shape models

We present a method to compute the conditional distribution of a statistical shape model given partial data. The result is a "posterior shape model", which is again a statistical shape model of the same form as the original model. This allows its direct use in the variety of algorithms that include prior knowledge about the variability of a class of shapes with a statistical shape model. Posterior shape models then provide a statistically sound yet easy method to integrate partial data into these algorithms. Usually, shape models represent a complete organ, for instance in our experiments the femur bone, modeled by a multivariate normal distribution. But because in many application certain parts of the shape are known a priori, it is of great interest to model the posterior distribution of the whole shape given the known parts. These could be isolated landmark points or larger portions of the shape, like the healthy part of a pathological or damaged organ. However, because for most shape models the dimensionality of the data is much higher than the number of examples, the normal distribution is singular, and the conditional distribution not readily available. In this paper, we present two main contributions: First, we show how the posterior model can be efficiently computed as a statistical shape model in standard form and used in any shape model algorithm. We complement this paper with a freely available implementation of our algorithms. Second, we show that most common approaches put forth in the literature to overcome this are equivalent to probabilistic principal component analysis (PPCA), and Gaussian Process regression. To illustrate the use of posterior shape models, we apply them on two problems from medical image analysis: model-based image segmentation incorporating prior knowledge from landmarks, and the prediction of anatomically correct knee shapes for trochlear dysplasia patients, which constitutes a novel medical application. Our experiments confirm that the use of conditional shape models for image segmentation improves the overall segmentation accuracy and robustness

Probabilistic methods in the analysis of protein interaction networks

Imperial Users onl

Design, poverty and sustainable development

Design in a poor context, or for the alleviation of poverty, has received little or no attention. An informal discourse analysis shows that design and poverty have not been linked, the two being seen as mutually exclusive. This paper aims to examine the relationships between design and designers, poverty and the poor, and sustainable development, which aims to alleviate poverty. On the face of it, there would appear to be little that links them; however, this paper aims to identify specific design initiatives that relate to poor people in the southern hemisphere as producers and consumers of designed goods.1 It briefly outlines definitions of poverty and sustainable development, then describes selected design interventions. It analyzes the contribution that these initiatives make to the reduction of poverty, and to the different aspects of sustainable development

Human infection with Gongylonema pulchrum

A 43 year old woman developed a painful tumor at the left buccal mucosa. Following local anti-inflammatory treatment a 35 mm long, living female adult worm of Gongylonema pulchrum was extracted from the affected side. No further treatment was needed and recovery was complete 5 days after extraction. Infection had occurred possibly 6 weeks before in Hungary with ingestion of contaminated water from an open draw well. Although commonly occurring as parasitic infection of domestic cattle and other vertebrates, gongylonemiasis is very rare in humans. Only 48 cases have been described in the literature since 1864. Life cycle and pathology of G. pulchrum are discussed