0
喜欢
0
书签
声明论文
and MRI Images Images Images Images   
摘  要:   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 this method, the computational complexity of computing derivatives increases as the number of terms (needed for curve evolution) in the energy function increases. In contrast, a genetic algorithm optimizes the level-set curve 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 high- level features makes automatic segmentation possible.

共享有0个版本
Bibtex
创新指数 
阅读指数 
重现指数 
论文点评
还没有人点评哦

Feedback
Feedback
Feedback
我想反馈:
排行榜