Disaster Behavioral Health Response Training, 2010

Participants will learn how the Disaster Behavioral Health Response Team will function in disaster settings, including factors affecting individual responses to disaster, phases of disaster, "at risk" groups, concepts of loss and grief, post-disaster stress, and the disaster recovery process

Iowa Disaster Behavioral Health Response Team, 2010

Mission: To lessen the adverse mental health effects of trauma for victims, survivors, and responders of traumatic events, whether natural or man-made.

Disaster Behavioral Health Response Team Member Qualities, 2010

This document states the qualities that is desired for team members that would be on the disaster behavioral health response teams. Produced by the Iowa Department of Human Services.

Iowa Public Television’s Planning Targets 2011-2014

Agency Performance Plan, Iowa Public Television

Final Planning Report for City of Boone, Iowa, 1962, Vol. 1

This is volume 1 of the final report on the Comprehensive Plan for the City of Boone. Contained in this report are reproductions of the important maps and charts prepared in connection with this study.

Final Planning Report for City of Boone, Iowa, March 1962, Vol. 2

This is volume 2 of the final report on the Comprehensive Plan for the City of Boone. Contained in this report are reproductions of the important maps and charts prepared in connection with this study.

Final planning report for City of Boone, Iowa, 1962, Vol. 3

This is volume 3 of the final report on the Comprehensive Plan for the City of Boone. Contained in this report are reproductions of the important maps and charts prepared in connection with this study.

Segmentation of Head and Neck Lymph Node Regions for Radiotherapy Planning Using Active Contour-Based Atlas Registration

In this paper, we present the segmentation of the headand neck lymph node regions using a new active contourbased atlas registration model. We propose to segment thelymph node regions without directly including them in theatlas registration process; instead, they are segmentedusing the dense deformation field computed from theregistration of the atlas structures with distinctboundaries. This approach results in robust and accuratesegmentation of the lymph node regions even in thepresence of significant anatomical variations between theatlas-image and the patient's image to be segmented. Wealso present a quantitative evaluation of lymph noderegions segmentation using various statistical as well asgeometrical metrics: sensitivity, specificity, dicesimilarity coefficient and Hausdorff distance. Acomparison of the proposed method with two other state ofthe art methods is presented. The robustness of theproposed method to the atlas selection, in segmenting thelymph node regions, is also evaluated.

Model-based Segmentation and Image Fusion of 3D Computed Tomography and 3D Ultrasound of the Eye for Radiotherapy Planning

For radiotherapy treatment planning of retinoblastoma inchildhood, Computed Tomography (CT) represents thestandard method for tumor volume delineation, despitesome inherent limitations. CT scan is very useful inproviding information on physical density for dosecalculation and morphological volumetric information butpresents a low sensitivity in assessing the tumorviability. On the other hand, 3D ultrasound (US) allows ahigh accurate definition of the tumor volume thanks toits high spatial resolution but it is not currentlyintegrated in the treatment planning but used only fordiagnosis and follow-up. Our ultimate goal is anautomatic segmentation of gross tumor volume (GTV) in the3D US, the segmentation of the organs at risk (OAR) inthe CT and the registration of both. In this paper, wepresent some preliminary results in this direction. Wepresent 3D active contour-based segmentation of the eyeball and the lens in CT images; the presented approachincorporates the prior knowledge of the anatomy by usinga 3D geometrical eye model. The automated segmentationresults are validated by comparing with manualsegmentations. Then, for the fusion of 3D CT and USimages, we present two approaches: (i) landmark-basedtransformation, and (ii) object-based transformation thatmakes use of eye ball contour information on CT and USimages.