AEDS: a novel technique for detecting DNA bands in autoradiograph images

The discipline of image processing is haunted by many problems, such as poor edge detection in low contrast images, speed of recognition, high computational cost and impracticality. Thus, new measures are required to solve these problems. Scale space analysis is an efficient solution to the edge detection of objects in low to high contrast images. However, this approach is time consuming and computationally expensive. The parallel processing properties of a neural network provide an ideal solution to managing the large amounts of data processed in image analysis, however their application to multiscale analysis is still in its infancy. This paper reports on a new approach to solving the aforementioned problems. The novel idea is based on combining neural network arbitration and scale space analysis to automatically select one optimum scale for the entire image at which scale space edge detection can be applied. This new approach to edge detection is formalised in the automatic edge detection scheme (AEDS). The AEDS is implemented on a real-life problem namely, the detection of bands within DNA autoradiograph images