Table 1: Glacier measurements from air photos 1969 and landsat images 1972 and 1973

The extent of snow cover at the end of the ablation season on glaciers in the Tyrolean Alps in 1972 and 1973 was determined from Landsat-1 Multispectral Scanner (MSS) images. For snovv mapping the MSS-images with a ground resolution of 80 meters were enlarged to a scale of 1: 100.000 by photographic methods. Different appearance of snow cover in the 4 MSS-channels is discussed in connection with ground truth control. The accuracy of snow and ice mapping from Landsat images was checked on 15 glaciers with an area from 1 to 10 km2 by aerial photography and/or ground truth control. These comparisons imply the usefulness of Landsat images for snow mapping on glaciers of a few square kilometers. The altitude of the equilibrium line was determined from Landsat images for 53 glaciers in the Tyrolean Alps. The regional differences in the equilibrium line altitude correspond to the regional precipitation patterns. The equilibrium line was identical with the snow line at the end of the budget year 1971/1972; therefore it was possible to determine the equilibrium line from satellite images. For 1968/69 the equilibrium line was mapped from aerial photographs for several glaciers. In 1972/73 mass balance was strongly negative and the equilibrimn line was within the firn area of the glaciers. Therefore it was not possible to distinguish between accumulation and ablation areas from the Landsat images of September 1973; however, snow and ice areas could be olearly differentiated. The ratios of accumulation area 01' snow area to the total area of the glaciers were determineel from satellite images and aerial photography separately for aelvancing anel for retreating glaciers and were relateel to the mass balance. In the budget years 1968/69 and 1972/73 with negative mass balance the accumulation area ratios of the advancing glacien; were olearly different from the ratios of the retreating glaciers, in 1971/72 with positive 01' balanced mass budget the differences between advancing and retreating glaciers were not significant.

Overlapping Range Images using Genetics Algorithms

El artículo aborda el problema del encaje de diversas imágenes de una misma escena capturadas por escáner 3d para generar un único modelo tridimensional. Para ello se utilizaron algoritmos genéticos. ABSTRACT: This work introduces a solution based on genetic algorithms to find the overlapping area between two point cloud captures obtained from a three-dimensional scanner. Considering three translation coordinates and three rotation angles, the genetic algorithm evaluates the matching points in the overlapping area between the two captures given that transformation. Genetic simulated annealing is used to improve the accuracy of the results obtained by the genetic algorithm.

Systematic Monitoring of Wound Healing Using a Hand-held Near-Infrared Optical Scanner

According to the American Podiatric Medical Association, about 15 percent of the patients with diabetes would develop a diabetic foot ulcer. Furthermore, foot ulcerations leads to 85 percent of the diabetes-related amputations. Foot ulcers are caused due to a combination of factors, such as lack of feeling in the foot, poor circulation, foot deformities and the duration of the diabetes. To date, the wounds are inspected visually to monitor the wound healing, without any objective imaging approach to look before the wound’s surface. Herein, a non-contact, portable handheld optical device was developed at the Optical Imaging Laboratory as an objective approach to monitor wound healing in foot ulcer. This near-infrared optical technology is non-radiative, safe and fast in imaging large wounds on patients. The FIU IRB-approved study will involve subjects that have been diagnosed with diabetes by a physician and who have developed foot ulcers. Currently, in-vivo imaging studies are carried out every week on diabetic patients with foot ulcers at two clinical sites in Miami. Near-infrared images of the wound are captured on subjects every week and the data is processed using customdeveloped Matlab-based image processing tools. The optical contrast of the wound to its peripheries and the wound size are analyzed and compared from the NIR and white light images during the weekly systematic imaging of wound healing.

Automated Assessment of Image Quality and Dose Attributes in Clinical CT Images

Computed tomography (CT) is a valuable technology to the healthcare enterprise as evidenced by the more than 70 million CT exams performed every year. As a result, CT has become the largest contributor to population doses amongst all medical imaging modalities that utilize man-made ionizing radiation. Acknowledging the fact that ionizing radiation poses a health risk, there exists the need to strike a balance between diagnostic benefit and radiation dose. Thus, to ensure that CT scanners are optimally used in the clinic, an understanding and characterization of image quality and radiation dose are essential.

The state-of-the-art in both image quality characterization and radiation dose estimation in CT are dependent on phantom based measurements reflective of systems and protocols. For image quality characterization, measurements are performed on inserts imbedded in static phantoms and the results are ascribed to clinical CT images. However, the key objective for image quality assessment should be its quantification in clinical images; that is the only characterization of image quality that clinically matters as it is most directly related to the actual quality of clinical images. Moreover, for dose estimation, phantom based dose metrics, such as CT dose index (CTDI) and size specific dose estimates (SSDE), are measured by the scanner and referenced as an indicator for radiation exposure. However, CTDI and SSDE are surrogates for dose, rather than dose per-se.

Currently there are several software packages that track the CTDI and SSDE associated with individual CT examinations. This is primarily the result of two causes. The first is due to bureaucracies and governments pressuring clinics and hospitals to monitor the radiation exposure to individuals in our society. The second is due to the personal concerns of patients who are curious about the health risks associated with the ionizing radiation exposure they receive as a result of their diagnostic procedures.

An idea that resonates with clinical imaging physicists is that patients come to the clinic to acquire quality images so they can receive a proper diagnosis, not to be exposed to ionizing radiation. Thus, while it is important to monitor the dose to patients undergoing CT examinations, it is equally, if not more important to monitor the image quality of the clinical images generated by the CT scanners throughout the hospital.

The purposes of the work presented in this thesis are threefold: (1) to develop and validate a fully automated technique to measure spatial resolution in clinical CT images, (2) to develop and validate a fully automated technique to measure image contrast in clinical CT images, and (3) to develop a fully automated technique to estimate radiation dose (not surrogates for dose) from a variety of clinical CT protocols.