Automatic and robust system for correcting microarray images' rotations and isolating spots.

Microarray images contain a large volume of genetic data in the form of thousands of spots that need to be extracted and analyzed using digital image processing. Automatic extraction, gridding, is therefore necessary to save time, to remove user-dependent variations, and, hence, to obtain repeatable results. In this research paper, an algorithm that involves four steps is proposed to efficiently grid microarray images. A set of real and synthetic microarray images of different sizes and degrees of rotations is used to assess the proposed algorithm, and its efficiency is compared with the efficiencies of other methods from the literature.

Predicting neuropathic ulceration: analysis of static temperature distributions in thermal images.

Foot ulcers affect millions of Americans annually. Conventional methods used to assess skin integrity, including inspection and palpation, may be valuable approaches, but they usually do not detect changes in skin integrity until an ulcer has already developed. We analyze the feasibility of thermal imaging as a technique to assess the integrity of the skin and its many layers. Thermal images are analyzed using an asymmetry analysis, combined with a genetic algorithm, to examine the infrared images for early detection of foot ulcers. Preliminary results show that the proposed technique can reliably and efficiently detect inflammation and hence effectively predict potential ulceration.