Google Hacking. - Lancour, Lisa (2005)
Work in the area of Google Hacking resulted in two presentations, 2 publications, a grant of $2500, and incorporation of results in two courses (Web Security CSC553)and (Information Security CSC453).
Keywords: dept.csc; faculty_collection; sabbatical_report

An approach to microCT image processing - Clark, Michael R; Daponte, John S; Sadowski, Thomas (2005)
In scientific imaging, it is crucial to obtain precise images to facilitate accurate observations for the given application. However, often times the imaging equipment used to acquire such images introduces error into the observed image. Therefore, there is a fundamental need to remove the error associated with these images in order to facilitate accurate observations. This study investigates the effectiveness of an image processing technique utilizing an iterative deconvolution algorithm to remove error from micro-CT images. This technique is applied to several sets of in-vivo micro CT scans of mice, and its effectiveness is evaluated by qualitative comparison of the resultant thresholded binary images to thresholded binary images produced by more conventional image processing techniques; namely Gaussian filtering and straight thresholding. Results for this study suggest that iterative deconvolution as a pre-processing step produces superior qualitative results as compared to the more conventional methods tested. The groundwork for future quantitative verification is motivated. ©2005 IEEE.
Keywords: Algorithms; Binary images; Biomedical engineering; Computerized tomography; Deconvolution algorithms; Errors; Image processing; Imaging systems; Iterative methods; Micro-CT image processing; Natural sciences computing; Scientific imaging; dept.csc

Visual enhancement of micro CT bone density images - Daponte, John S; Clark, Michael R; Damon, Megan; Kamins, Rebecca; Sadowski, Thomas; Tirrell, Charles (2005)
The primary goal of this research was to provide image processing support to aid in the identification of those subjects most affected by bone loss when exposed to weightlessness and provide insight into the causes for large variability. Past research has demonstrated that genetically distinct strains of mice exhibit different degrees of bone loss when subjected to simulated weightlessness. Bone loss is quantified by in vivo computed tomography (CT) imaging. The first step in evaluating bone density is to segment gray scale images into separate regions of bone and background. Two of the most common methods for implementing image segmentation are thresholding and edge detection. Thresholding is generally considered the simplest segmentation process which can be obtained by having a user visually select a threshold using a sliding scale. This is a highly subjective process with great potential for variation from one observer to another. One way to reduce inter-observer variability is to have several users independently set the threshold and average their results but this is a very time consuming process. A better approach is to apply an objective adaptive technique such as the Riddler / Calvard method. In our study we have concluded that thresholding was better than edge detection and pre-processing these images with an iterative deconvolution algorithm prior to adaptive thresholding yields superior visualization when compared with images that have not been pre-processed or images that have been pre-processed with a filter.
Keywords: Adaptive technique; Algorithms; Bone; Bone density; Bone loss; Computerized tomography; Edge detection; Gray scale images; Image analysis; Image enhancement; Image segmentation; Medical imaging; Visualization; dept.csc