A novel genetic algorithm (GA) is presented here that performs level set curve evolution using texture and shape information to automatically segment the prostate on pelvic images in computed tomography and magnetic resonance imaging modalities. Here, the segmenting contour is represented as a level set function. The contours in a typical level set evolution are deformed by minimizing an energy function using the gradient descent method. In these methods, the computational complexity of computing derivatives increases as the number of terms (needed for curve evolution) in the energy function increase. In contrast, a genetic algorithm optimizes the level-set function without the need to compute derivatives, thereby making the introduction of new curve evolution terms straightforward. The GA developed here uses the texture of the prostate gland and its shape derived from manual segmentations to perform curve evolution. Using these highlevel features makes automatic segmentation possible.
In REF , a GA is used to perform level set curve evolution using texture and shape information to automatically segment the prostate in CT and MRI pelvic images.
A Genetic Algorithm-based based Level Set et Curve Evolution for Prostate Segmentation on Pelvic CT and MRI Images
Computer Science