HISTORY OF THE METHODS/FLOW CHART
ABOUT THE AUTHOR/CV
1996, 1999, 2006
Applications to Imaging and Medical Imaging
The central perspective of appling level set methods to image segmentation comes from the approach of adopting the methodology of snakes to grow an interface from a seed point onto the selected boundary. This strategy was proposed by Malladi, Sethian, and Vemuri (Ref. 1 below) and Caselles, Catte, Coll, and Dibos in (Ref. 2). Malladi makes his approach fast by coupling it to Narrow Band Methods in Ref. 5, and couples the approach to Fast Marching Methods in Ref. 8. In more detail, using a level set method, Ref. 1 synthesizes a speed function F from the image gradient in order to extract the underlying boundary and realized that a speed F of the form
where I is the image gradient, would slow down and stop where the image gradient was large, indicating that boundary was reached. The interested reader is referred to a large amount of movies, interactive java applets, and details of interface-based medical image segmentation, showing how these techniques work.
A particular fast version of these techniques, given in the references below, uses the Fast Marching Method to perform an initial segmentation which produces a result close to the desired answer; further refinement then comes from resorting to the original algorithm.
Once these segmentation problems have been addressed, an additional issue is to perform some preprocessing image analysis which can enhance and denoise the image, creating an image which is more amenable to the above segmentation algorithm. Here again, considerable work has been done using partial differential equations based techniques. Some work which couples this level set enhancement/denoising ideas may be found in the references below.
Movies, interactive java applets, and details of medical image segmentation.
Movies, interactive java applets, and details of image denoising.
New Book and Resource on Level Set and Fast Marching Methods