: This study examines the projected impacts of climate change on the nation of Colombia and explores their implications for its future national and regional security. It argues that many of Colombia's existing security threats, which stem from internal problems of poverty, social inequality, and persistent levels of organized violence, will likely continue in the future and be affected by climate change. The most likely climate-driven effects with security implications are natural disasters particularly flooding-and the social and economic shocks these generate, and the security challenges entailed in large-scale human dislocation and economic change. In addition, several Colombian military bases are individually threatened by flooding or sea-level rise, and changes in river levels, sea levels, and the frequency and intensity of El Nifio/La Nifia events will affect to some degree tactical operations in river, jungle, and coastal operations. In regard to regional security, Colombia is likely to be called upon to participate in humanitarian and emergency relief. In a future context of limited government and international resources, it will be critical that countries of the region, including the United States, strengthen regional institutions that can coordinate joint planning and response to these relief efforts.

Previously, we developed a simple Laguerre–Gauss (LG) channelized Hotelling observer (CHO) for incorporation into our mass computer-aided detection (CAD) system. This LG-CHO was trained using initial detection suspicious region data and was empirically optimized for free parameters. For the study presented in this paper, we wish to create a more optimal mass detection observer based on a novel combination of LG channels. A large set of LG channels with differing free parameters was created. Each of these channels was applied to the suspicious regions, and an output test statistic was determined. A stepwise feature selection algorithm was used to determine which LG channels would combine best to detect masses. These channels were combined using a HO to create a single template for the mass CAD system. Results from free-response receiver operating characteristic curves demonstrated that the incorporation of the novel LG-CHO into the CAD system slightly improved performance in high-sensitivity regions.

In this article, we present a characterization of the effect of difference of Gaussians (DoG) filters in the detection of mammographic regions. DoG filters have been used previously in mammographic mass computer-aided detection (CAD) systems. As DoG filters are constructed from the subtraction of two bivariate Gaussian distributions, they require the specification of three parameters: the size of the filter template and the standard deviations of the constituent Gaussians. The influence of these three parameters in the detection of mammographic masses has not been characterized. In this work, we aim to determine how the parameters affect (1) the physical descriptors of the detected regions, (2) the true and false positive rates, and (3) the classification performance of the individual descriptors. To this end, 30 DoG filters are created from the combination of three template sizes and four values for each of the Gaussians' standard deviations. The filters are used to detect regions in a study database of 181 craniocaudal-view mammograms extracted from the Digital Database for Screening Mammography. To describe the physical characteristics of the identified regions, morphological and textural features are extracted from each of the detected regions. Differences in the mean values of the features caused by altering the DoG parameters are examined through statistical and empirical comparisons. The parameters' effects on the true and false positive rate are determined by examining the mean malignant sensitivities and false positives per image (FPpI). Finally, the effect on the classification performance is described by examining the variation in FPpI at the point where 81% of the malignant masses in the study database are detected. Overall, the findings of the study indicate that increasing the standard deviations of the Gaussians used to construct a DoG filter results in a dramatic decrease in the number of regions identified at the expense of missing a small number of malignancies. The sharp reduction in the number of identified regions allowed the identification of textural differences between large and small mammographic regions. We find that the classification performances of the features that achieve the lowest average FPpI are influenced by all three of the parameters.

The purpose of this paper is to present a new segmentation routine developed for mammographic masses. We previously developed a computer-aided detection (CAD) system for mammographic masses that employed a simple but imprecise segmentation procedure. To improve the systems performance, an iterative, linear segmentation routine was developed. The routine begins by employing a linear discriminant function to determine the optimal threshold between estimates of an objects interior and exterior pixels. After applying the threshold and identifying the objects outline, two constraints are applied to minimize the influence of extraneous background structures. Each iteration further refines the outline until the stopping criterion is reached. The segmentation algorithm was tested on a database of 181 mammographic images that contained forty-nine malignant and fifty benign masses. A set of suspicious regions of interest (ROIs) was found using the previous CAD system. Twenty features were measured from the regions before and after applying the new segmentation routine. The difference in the features discriminatory ability was examined via receiver operating characteristic (ROC) analysis. A significant performance difference was observed in many features, particularly those describing the object border. Free-response ROC (FROC) curves were utilized to examine how the overall CAD system performance changed with the inclusion of the segmentation routine. The FROC performance appeared to be improved, especially for malignant masses. When detecting 90% of the malignant masses, the previous system achieved 4.4 false positives per image (FPpI) compared to the post-segmentation systems 3.7 FPpI. At 85%, the respective FPpI are 4.1 and 2.1.

In previous research, we have developed a computer-aided detection (CAD) system designed to detect masses in mammograms. The previous version of our system employed a simple but imprecise method to localize the masses. In this research, we present a more robust segmentation routine for use with mammographic masses. Our hypothesis is that by more accurately describing the morphology of the masses, we can improve the CAD system's ability to distinguish masses from other mammographic structures. To test this hypothesis, we incorporated the new segmentation routine into our CAD system and examined the change in performance. The developed iterative, linear segmentation routine is a gray level-based procedure. Using the identified regions from the previous CAD system as the initial seeds, the new segmentation algorithm refines the suspicious mass borders by making estimates of the interior and exterior pixels. These estimates are then passed to a linear discriminant, which determines the optimal threshold between the interior and exterior pixels. After applying the threshold and identifying the object's outline, two constraints on the border are applied to reduce the influence of background noise. After the border is constrained, the process repeats until a stopping criterion is reached. The segmentation routine was tested on a study database of 183 mammographic images extracted from the Digital Database for Screening Mammography. Eighty-three of the images contained 50 malignant and 50 benign masses; 100 images contained no masses. The previously developed CAD system was used to locate a set of suspicious regions of interest (ROIs) within the images. To assess the performance of the segmentation algorithm, a set of 20 features was measured from the suspicious regions before and after the application of the developed segmentation routine. Receiver operating characteristic (ROC) analysis was employed on the ROIs to examine the discriminatory capabilities of each individual feature before and after the segmentation routine. A statistically significant performance increase was found in many of the individual features, particularly those describing the mass borders. To examine how the incorporation of the segmentation routine affected the performance of the overall CAD system, free-response ROC (FROC) analysis was employed. When considering only malignant masses, the FROC performance of the system with the segmentation routine appeared better than the previous system. When detecting 90% of the malignant masses, the previous system achieved 4.9 false positives per image (FPpI) compared to the post-segmentation system's 4.2 FPpI. At 80% sensitivity, the respective FPpI were 3.5 and 1.6.

In this paper, we present preliminary results from a highly sensitive and specific CAD system for mammographic masses. For false positive reduction, the system incorporated features derived from shape, fractal, and channelized Hotelling observer (CHO) measurements. The database for this study consisted of 80 craniocaudal mammograms randomly extracted from USF's digital database for screening mammography. The database contained 49 mass findings (24 malignant, 25 benign). To detect initial mass candidates, a difference of Gaussians (DOG) filter was applied through normalized cross correlation. Suspicious regions were localized in the filtered images via multi-level thresholding. Features extracted from the regions included shape, fractal dimension, and the output from a Laguerre-Gauss (LG) CHO. Influential features were identified via feature selection techniques. The regions were classified with a linear classifier using leave-one-out training/testing. The DOG filter achieved a sensitivity of 88% (23/24 malignant, 20/25 benign). Using the selected features, the false positives per image dropped from ~20 to ~5 with no loss in sensitivity. This preliminary investigation of combining multi-level thresholded DOG-filtered images with shape, fractal, and LG-CHO features shows great promise as a mass detector. Future work will include the addition of more texture and mass-boundary descriptive features as well as further exploration of the LG-CHO.

We propose to investigate the use of a Laguerre-Gauss Channelized Hotelling Observer (LG-CHO) for the basis of a computer aided detection scheme for masses in mammography.A database of 1320 regions of interest was selected from the DDSM database collected by the University of South Florida. The breakdown of the cases was: 656 normals, 307 benigns, and 357 cancers. For the detection task, cancer and benign cases were considered positive and normal was considered negative. A 25 channel LG-CHO was designed to best classify regions as containing a mass or not. Application of this LG-CHO to the database gave a ROC area under the curve of 0.936 and a partial area of 0.648. Additionally, at 98% sensitivity the classifier had a specificity of 44.8% and a positive predictive value of 64.2%Preliminary results suggest that using a LG-CHO can provide a strong backbone for a CAD scheme to help radiologists with detection. These initial results should be able to be incorporated into a. larger CAD system for higher performance either as a false positive reduction scheme or as an initial filter used for mass detection.

The purpose of this study was to develop a knowledge-based scheme for the detection of masses on digitized screening mammograms. The computer-assisted detection (CAD) scheme utilizes a knowledge databank of mammographic regions of interest (ROIs) with known ground truth. Each ROI in the databank serves as a template. The CAD system follows a template matching approach with mutual information as the similarity metric to determine if a query mammographic ROI depicts a true mass. Based on their information content, all similar ROIs in the databank are retrieved and rank-ordered. Then, a decision index is calculated based on the query's best matches. The decision index effectively combines the similarity indices and ground truth of the best-matched templates into a prediction regarding the presence of a mass in the query mammographic ROI. The system was developed and evaluated using a database of 1465 ROIs extracted from the Digital Database for Screening Mammography. There were 809 ROIs with confirmed masses (455 malignant and 354 benign) and 656 normal ROIs. CAD performance was assessed using a leave-one-out sampling scheme and Receiver Operating Characteristics analysis. Depending on the formulation of the decision index, CAD performance as high as A(zeta) = 0.87 +/- 0.01 was achieved. The CAD detection rate was consistent for both malignant and benign masses. In addition, the impact of certain implementation parameters on the detection accuracy and speed of the proposed CAD scheme was studied in more detail.

We describe a probit regression approach for maximum-likelihood (ML) estimation of a linear observer template from human-observer data in two-alternative forced-choice experiments. Like a previous approach to ML estimation in this problem [Abbey & Eckstein, Proc. SPIE, Vol. 4324, 2001], our approach does not make any assumptions about the distribution of the images. The previous approach utilized a regularizing prior distribution to control the degrees of freedom in the problem. In this work, we constrain the observer template to be represented by a limited number of linear features. Standard methods of probit regression are described for estimating the feature weights, and hence the observer templates.We have used this probit regression method to estimate human-observer templates for the detection of a small (5mm diameter) round simulated mass embedded in digitized mammograms. Our estimated templates for detecting the mass contain a band of heavily weighted spatial frequencies from 0.08 to 0.3 cycles/mm. We show comparisons between the human-observer template data, and the templates of a number of linear model observers that have been investigated as perceptual models of the human.

This paper describes the development of a computer-aided diagnosis (CAD) tool for solitary pulmonary nodules. This CAD tool is built upon physically meaningful features that were selected because of their relevance to shape and texture. These features included a modified version of the Hotelling statistic (HS), a channelized HS, three measures of fractal properties, two measures of spicularity, and three manually measured shape features. These features were measured from a difficult database consisting of 237 regions of interest (ROIs) extracted from digitized chest radiographs. The center of each 256x256 pixel ROI contained a suspicious lesion which was sent to follow-up by a radiologist and whose nature was later clinically determined. Linear discriminant analysis (LDA) was used to search the feature space via sequential forward search using percentage correct as the performance metric. An optimized feature subset, selected for the highest accuracy, was then fed into a three layer artificial neural network (ANN). The ANN s performance was assessed by receiver operating characteristic (ROC) analysis. A leave-one-out testing/training methodology was employed for the ROC analysis. The performance of this system is competitive with that of three radiologists on the same database.

We propose to investigate a. novel use of the Hotelling observer for the task of discrimination of solitary pulmonary nodules from a database of regions that were all deemed suspicious. A database of 239 regions of interest (ROIs) was collected from digitized chest radiographs. Each of these 256x256 pixel ROIs contained a suspicious lesion in the center for which we have a truth file. For our study, 25 separate Hotelling observers were set up in a 5x5 grid across the center of the ROIs. Each separate observer was designed to observe a 15x15 pixel area of the image. Leave-one-out training was used to generate 25 output observer features. These 25 features were then narrowed down using a sequential forward searching linear discriminant analysis. The forward search was continued until the accuracy declined at 13 features and the subset was used as the input layer to an artificial neural network (ANN). This network was trained to minimize mean squared error and the output was the area under the ROC curve. The trained ANN gave an ROC area of .86. In comparison, three radiologists performed at ROC area indexes of .72, .79, and .83.

We propose to investigate the use of subregion Hotelling observers (SRHOs) in conjunction with perceptrons for the computerized classification of suspicious regions in chest radiographs for being nodules requiring follow up. Previously, 239 regions of interest (ROIs), each containing a suspicious lesion with proven classification, were collected. We chose to investigate the use of SRHOs as part of a multilayer classifier to determine the presence of a nodule. Each SRHO incorporates information about signal, background, and noise correlation for classification. For this study, 225 separate Hotelling observers were set up in a grid across each ROI. Each separate observer discriminates an 8 by 8 pixel area. A round robin sampling scheme was used to generate the 225 features, where each feature is the output of the individual observers. These features were then rank ordered by the magnitude of the weights of a perceptron. Once rank ordered, subsets of increasing number of features were selected to be used in another perceptron. This perceptron was trained to minimize mean squared error and the output was a continuous variable representing the likelihood of the region being a nodule. Performance was evaluated by receiver operating characteristic (ROC) analysis and reported as the area under the curve (Az). The classifier was optimized by adding additional features until the Az declined. The optimized subset of observers then were combined using a third perceptron. A subset of 80 features was selected which gave an Az of 0.972. Additionally, at 98.6% sensitivity, the classifier had a specificity of 71.3% and increased the positive predictive value from 60.7% to 84.1 %. Preliminary results suggest that using SRHOs in combination with perceptrons can provide a successful classification scheme for pulmonary nodules. This approach could be incorporated into a larger computer aided detection system for decreasing false positives.