NLG Museum Connecting to Collections, Statewide Planning Grant, Developing a Statewide Disaster Preparedness and Response Plan for Iowa, Final report.

The Iowa Conservation and Preservation Consortium, Iowa Museum Association, the State Historical Society of Iowa and the State Library requested the IMLS CTC grant funds so we could develop a statewide plan to help the stewards of cultural property plan for protection and disaster recovery of collections. We are very pleased with the results of our grant activities. Thanks to the IMLS CTC grant over 200 Iowa collection care takers have received basic training in disaster preparedness and response, 24 care takers have signed up to become cultural collection first responders, a dozen disaster planning/response trainers are available upon request, and over 40 institutions have ReAct Paks. We have created a variety of training tools ranging from basic awareness to in-depth training, established a website of disaster resources, and geo-referenced hundreds of cultural collection sites around Iowa. In addition, the IMLS grant was just the motivator we needed to participate in other national/international efforts which in turn strengthened our IMLS grant project.

Classification of urban multi-angular image sequences by aligning their manifolds

When dealing with multi-angular image sequences, problems of reflectance changes due either to illumination and acquisition geometry, or to interactions with the atmosphere, naturally arise. These phenomena interplay with the scene and lead to a modification of the measured radiance: for example, according to the angle of acquisition, tall objects may be seen from top or from the side and different light scatterings may affect the surfaces. This results in shifts in the acquired radiance, that make the problem of multi-angular classification harder and might lead to catastrophic results, since surfaces with the same reflectance return significantly different signals. In this paper, rather than performing atmospheric or bi-directional reflection distribution function (BRDF) correction, a non-linear manifold learning approach is used to align data structures. This method maximizes the similarity between the different acquisitions by deforming their manifold, thus enhancing the transferability of classification models among the images of the sequence.

Objective assessment of image quality in conventional and digital mammography taking into account dynamic range.

The goal of this work is to develop a method to objectively compare the performance of a digital and a screen-film mammography system in terms of image quality. The method takes into account the dynamic range of the image detector, the detection of high and low contrast structures, the visualisation of the images and the observer response. A test object, designed to represent a compressed breast, was constructed from various tissue equivalent materials ranging from purely adipose to purely glandular composition. Different areas within the test object permitted the evaluation of low and high contrast detection, spatial resolution and image noise. All the images (digital and conventional) were captured using a CCD camera to include the visualisation process in the image quality assessment. A mathematical model observer (non-prewhitening matched filter), that calculates the detectability of high and low contrast structures using spatial resolution, noise and contrast, was used to compare the two technologies. Our results show that for a given patient dose, the detection of high and low contrast structures is significantly better for the digital system than for the conventional screen-film system studied. The method of using a test object with a large tissue composition range combined with a camera to compare conventional and digital imaging modalities can be applied to other radiological imaging techniques. In particular it could be used to optimise the process of radiographic reading of soft copy images.

MBIS: multivariate Bayesian image segmentation tool.

We present MBIS (Multivariate Bayesian Image Segmentation tool), a clustering tool based on the mixture of multivariate normal distributions model. MBIS supports multichannel bias field correction based on a B-spline model. A second methodological novelty is the inclusion of graph-cuts optimization for the stationary anisotropic hidden Markov random field model. Along with MBIS, we release an evaluation framework that contains three different experiments on multi-site data. We first validate the accuracy of segmentation and the estimated bias field for each channel. MBIS outperforms a widely used segmentation tool in a cross-comparison evaluation. The second experiment demonstrates the robustness of results on atlas-free segmentation of two image sets from scan-rescan protocols on 21 healthy subjects. Multivariate segmentation is more replicable than the monospectral counterpart on T1-weighted images. Finally, we provide a third experiment to illustrate how MBIS can be used in a large-scale study of tissue volume change with increasing age in 584 healthy subjects. This last result is meaningful as multivariate segmentation performs robustly without the need for prior knowledge.

Calibration of a desktop scanner and digital image analysis procedure for quantification of a root morphology

Selostus: Tasoskannerin ja digitaalisen kuva-analyysimenetelmän kalibrointi juurten morfologian kvantifioimiseksi

Collections in Institutional Herbaria listed as Rhizopogon, but not belonging to this genus

Fifty-five collections misidentified and stored in various institutional herbaria, under severa1 Rhizopogon species narnes, are revised. Among these, twelve belong to Ascomycotina and 9 were identified to species level; the other collections, immature fruitbodies wcre impossible to identify with the data available. Forty-two collections are Basidiomycotina and only an immature Scleroderma remain without identification to the species level.