Edge detection in prostate PSMA images

Prostate diseases are very common in adult and elderly men, and prostate boundary detection from images plays a key role in prostate disease diagnosis and treatment. The edges in an image usually refer to rapid changes in some physical properties, such as geometry, illumination. There are many ways to perform the edge detection. However, it may be grouped into two categories, that are gradient and Laplacian. The gradient method detects the edges by looking for the maximum and minimum in the first derivative of the image. It is traditionally implemented by convolving the image with masks. These masks are constructed using a first or second derivative operator. Thus, the problem of edge detection is therefore related to the problem of mask construction. Gaussian distribution has been used to build masks for the first and second derivative. However, this distribution has limit to only symmetric shape. This paper will use Weibull distribution to construct the masks, the Weibull distribution (WD) is more general than Gaussian because it has symmetric and asymmetric shape. The constructed masks are applied to images and we obtained good results.