Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating recipes having platform-independent, resource-agnostic commands for performing work in a cloud computing platform. One of the systems includes a plurality of execution engines, each execution engine being installed on a particular instance of an underlying computing platform, wherein each execution engine is operable to receive, through an execution engine API by a first execution engine of the plurality of execution engines installed on a first instance of a particular underlying computing platform, a plurality of platform-independent, resource-specific requests from an executor to operate on a resources in an underlying computing platform and in response, perform one or more platform-independent, resource instance-specific operations to operate on a first resource on the first instance of the particular underlying computing platform.

Abstract: A system for collecting, storing and displaying dermatological images for the purpose of monitoring and diagnosis of skin conditions and skin cancers, including melanoma. A hand-held unit illuminates a section of the patient's skin, and an imaging device generates imaging signals from light derived from a skin section. Pairs of light output ports in the hand-held unit are arranged such that their intensity distributions overlap at their half-intensity levels so that the resulting summation of their intensities has a flat central region. Three image stores are maintained, one for lesion images, one for “nearby skin” images, and one for reference-white images. The “nearby skin” images are used by the system software to automatically determine the skin/lesion border. The reference white images are used to set the dynamic range of the instrument and to compensate for lighting irregularities.

Abstract: An image (502) of an area of skin that includes a lesion (304) is captured. An annular variance test is performed on pixels around the lesion (304) (step 514). Based on the results of the annular variance test, either a seeded region growing method (step 516) or a colour clustering method (step 520) is applied to the image (502) to calculate a boundary of the lesion (304). The colour cluster method may produce multiple selectable boundaries. Provision is also made for a lesion boundary to be manually traced (step 522).