To assess a radar system's instantaneous performance on any given day, detailed knowledge of the meteorological conditions is required due to the dependency of atmospheric refractivity on thermodynamic properties such as temperature, water vapor, and pressure. Because of the significant challenges involved in obtaining these data, recent efforts have focused on development of methods to obtain the refractivity structure inversely using radar measurements and radar wave propagation models. Such inversion techniques generally use simplified refractivity models in order to reduce the parameter space of the solution. Here the accuracy of three simple refractivity models is examined for the case of an evaporation duct. The models utilize the basic log linear shape classically associated with evaporation ducts, but each model depends on various parameters that affect different aspects of the profile, such as its shape and duct height. The model parameters are optimized using radiosonde data, and their performance is compared to these atmospheric measurements. The optimized models and data are also used to predict propagation using a parabolic equation code with the refractivity prescribed by the models and measured data, and the resulting propagation patterns are compared. The results of this study suggest that the best log linear model formulation for an inversion problem would be a two‐layer model that contains at least three parameters: duct height, duct curvature, and mixed layer slope. This functional form permits a reasonably accurate fit to atmospheric measurements as well as embodies key features of the profile required for correct propagation prediction with as few parameters as possible.

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Previously we presented an unsupervised self-organizing map (SOM) for segmentation of the breast region in screening mammograms. This study improves upon our earlier technique by (1) enhancing the detection of the breast region near the skin line, as well as (2) reducing the computational complexity. Contrary to the initial technique, the improved one exploits global image properties extracted at different scales. These properties were used to both generate the SOM training samples and obtain a preliminary segmentation. Subsequently, a multi-step strategy was implemented to automatically outline a wide band around the skin line for further analysis. This additional step reduces the computational complexity by focusing the analysis on the set of pixels that creates clinically the highest ambiguity. Specifically, the same (already trained) SOM was applied to classify the ambiguous pixels around the skin line. The study was performed on 400 screening mammograms from the digital database for screening mammography (DDSM). Visual examination of the segmentation results confirmed an improvement in the detection of the low-contrast region near the skin line. The performance was consistent regardless of mammographic view and/or breast density. Furthermore, the computational cost of processing can be reduced by up to 80% of the original value.

Computer assisted detection systems (CAD) in mammography incorporate two critical stages: (i) prescreening to localize suspicious regions and (ii) detailed analysis of the regions for false positive reduction. In this work, we present a new technique for automatic detection of suspicious masses for prescreening mammograms. The hypothesis of the proposed technique is that malignant masses manifestate as superimposed concentric layers. Morphological characterization of these layers can form the foundation of an automated scheme for detection of suspicious masses. The study was based on fifty nine screening mammograms from the digital database of screening mammography (DDSM). Overall, the proposed scheme performs at 85.7% sensitivity with an average of 0.53 false positives per image. The scheme targets malignant masses and, thus it can provide a second opinion to radiologists without sending benign masses to unnecessary biopsy.