John E. Ball

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We present a mammographic computer aided diagnosis (CAD) system, which uses an adaptive level set segmentation method (ALSSM), which segments suspicious masses in the polar domain and adaptively adjusts the border threshold at each angle to provide high-quality segmentation results. The primary contribution of this paper is the adaptive speed function for(More)
This letter presents an automated mammographic computer aided diagnosis (CAD) system to detect and segment spicules in digital mammograms, termed spiculation segmentation with level sets (SSLS). SSLS begins with a segmentation of the suspicious mass periphery, which is created using a previously developed adaptive level set segmentation algorithm (ALSSM) by(More)
We present a supervised hyperspectral classification procedure consisting of an initial distance-based segmentation method that uses best band analysis (BBA), followed by a level set enhancement that forces localized region homogeneity. The proposed method is tested on two hyperspectral images of an urban and rural nature. The proposed method is compared to(More)
Most end-to-end Computer Aided Diagnosis (CAD) systems follow a three step approach - (1) Image enhancement and segmentation, (2) Feature extraction, and, (3) Classification. While the state of the art in image enhancement and segmentation can now very accurately identify regions of interest for feature extraction, they typically result in very high(More)
We present mammographic mass core segmentation, based on the Chan-Vese level set method. The proposed method is analyzed via resulting feature efficacies. Additionally, the core segmentation method is used to investigate the idea of a three stage segmentation approach, i.e. segment the mass core, periphery, and spiculations (if any exist) and use features(More)
This paper presents a semi-automated supervised level set hyperspectral image segmentation algorithm. The proposed method uses near-optimal speed functions (which control the level set segmentation) that are composed of a spectral similarity term and a stopping term. The spectral similarity term is used to compare pixels to class training signatures and is(More)
A case study in pixel unmixing is performed using the singular value decomposition method. Using a linear mixing model, mixed pixels are created from a subset of the hyperspectral data from an Analytical Spectral Devices handheld spectroradiometer. These pixels are unmixed using the other portion of the hyperspectral data. Simulation results are presented(More)