Improving medical image perception by hierarchical clustering based segmentation

It has been well documented that radiologists' performance is not perfect: they make both false positive and false negative decisions. For example, approximately thirty percent of early lung cancer is missed on chest radiographs when the evidence is clearly visible in retrospect. Currently computer-aided detection (CAD) uses software, designed to reduce errors by drawing radiologists' attention to possible abnormalities by placing prompts on images. Alberdi et al examined the effects of CAD prompts on performance, comparing the negative effect of no prompt on a cancer case with prompts on a normal case. They showed that no prompt on a cancer case can have a detrimental effect on reader sensitivity and that the reader performs worse than if the reader was not using CAD. This became particularly apparent when difficult cases were being read. They suggested that the readers were using CAD as a decision making tool instead of a prompting aid. They conclude that "incorrect CAD can have a detrimental effect on human decisions". The goal of this paper is to explore the possibility of using hierarchical clustering based segmentation (HSC), as a perceptual aid, to improve the performance of the reader

Improving medical image perception by hierarchical clustering based segmentation

It has been well documented that radiologists' performance is not perfect: they make both false positive and false negative decisions. For example, approximately thirty percent of early lung cancer is missed on chest radiographs when the evidence is clearly visible in retrospect [1]. Currently Computer-Aided Detection (CAD) uses software, designed to reduce errors by drawing radiologists' attention to possible abnormalities by placing prompts on images. Alberdi et al examined the effects of CAD prompts on performance, comparing the negative effect of no prompt on a cancer case with prompts on a normal case. They showed that no prompt on a cancer case can have a detrimental effect on reader sensitivity and that the reader performs worse than if the reader was not using CAD. This became particularly apparent when difficult cases were being read. They suggested that the readers were using CAD as a decision making tool instead of a prompting aid. They conclude that "incorrect CAD can have a detrimental effect on human decisions" [2]. The goal of this paper is to explore the possibility of using Hierarchical Clustering based Segmentation (HCS) [3], as a perceptual aid, to improve the performance of the reader

Aerodynamic design for improved manueverability by use of three-dimensional transonic theory

Improvements in transonic maneuver performance by the use of three-dimensional transonic theory and a transonic design procedure were examined. The FLO-27 code of Jameson and Caughey was used to design a new wing for a fighter configuration with lower drag at transonic maneuver conditions. The wing airfoil sections were altered to reduce the upper-surface shock strength by means of a design procedure which is based on the iterative application of the FLO-27 code. The plan form of the fighter configuration was fixed and had a leading edge sweep of 45 deg and an aspect ratio of 3.28. Wind-tunnel tests were conducted on this configuration at Mach numbers from 0.60 to 0.95 and angles of attack from -2 deg to 17 deg. The transonic maneuver performance of this configuration was evaluated by comparison with a wing designed by empirical methods and a wing designed primarily by two-dimensional transonic theory. The configuration designed by the use of FLO-27 had the same or lower drag than the empirical wing and, for some conditions, lower drag than the two-dimensional design. From some maneuver conditions, the drag of the two-dimensional design was somewhat lower

The construction and evaluation of four series of lessons to stimulate the flow of ideas in the creative writing of fourth, fifth, and sixth grade pupils.

Thesis (Ed.M.)--Boston Universit

Neuro-Dynamic Programming for Radiation Treatment Planning

In many cases a radiotherapy treatment is delivered as a series of smaller dosages over a period of time. Currently, it is difficult to determine the actual dose that has been delivered at each stage, precluding the use of adaptive treatment plans. However, new generations of machines will give more accurate information of actual dose delivered, allowing a planner to compensate for errors in delivery. We formulate a model of the day-to-day planning problem as a stochastic linear program and exhibit the gains that can be achieved by incorporating uncertainty about errors during treatment into the planning process. Due to size and time restrictions, the model becomes intractable for realistic instances. We show how neuro-dynamic programming can be used to approximate the stochastic solution, and derive results from our models for realistic time periods. These results allow us to generate practical rules of thumb that can be immediately implemented in current planning technologies.\ud \ud This material is based on research partially supported by the National Science Foundation Grants ACI-0113051 and CCR-9972372, the Air Force Office of Scientific Research Grant F49620-01-1-0040, Microsoft Corporation and the Guggenheim Foundation

A biophysical model of prokaryotic diversity in geothermal hot springs

Recent field investigations of photosynthetic bacteria living in geothermal hot spring environments have revealed surprisingly complex ecosystems, with an unexpected level of genetic diversity. One case of particular interest involves the distribution along hot spring thermal gradients of genetically distinct bacterial strains that differ in their preferred temperatures for reproduction and photosynthesis. In such systems, a single variable, temperature, defines the relevant environmental variation. In spite of this, each region along the thermal gradient exhibits multiple strains of photosynthetic bacteria adapted to several distinct thermal optima, rather than the expected single thermal strain adapted to the local environmental temperature. Here we analyze microbiology data from several ecological studies to show that the thermal distribution field data exhibit several universal features independent of location and specific bacterial strain. These include the distribution of optimal temperatures of different thermal strains and the functional dependence of the net population density on temperature. Further, we present a simple population dynamics model of these systems that is highly constrained by biophysical data and by physical features of the environment. This model can explain in detail the observed diversity of different strains of the photosynthetic bacteria. It also reproduces the observed thermal population distributions, as well as certain features of population dynamics observed in laboratory studies of the same organisms

Elections, Economic Outcomes and Policy in Canada: 1870 - 2015

In this paper we examine the relationship between economic and electoral outcomes in Canada since Confederation (1867) and the role that economic policy has played in influencing this relationship. The results are consistent with voter concern for the overall performance of the economy in the incumbent’s governing term—the average growth rate of per capita GDP and average unemployment rate—while rejecting the presence of a political business/budget cycle response in the period leading into an upcoming election. Ev