Medical image are considered important in medical world. Medical image
can be used for surgery preparation and simulation, radiotherapy planning, and for
monitoring wound development. Analising medical image is not an easy task,
expertise and experience are needed to do this task. Even with expertise doing this
task can take an amount of time. In this case computer is used to analise medical
image, by using image processing technique, image segmentation.
Medical image segmentation can be done using Distance Regularization
Level Set Evolution (DRLSE) method. This method were developed from the
level set method, which is used to eliminate the irregularities from level set
method. By using this method, medical image segmentation can be done, but long
processing time occur when using large files. To accelerate segmentation time,
computation that were done sequentially are replaced with parallel computation
by using Compute Unified Device Architecture (CUDA), which is developed by
NVIDIA enabling access to Graphic Processing Unit (GPU).
Using CUDA, the process of medical image segmentation using DRLSE can
be accelerated. Images used are in 64x64, 128x128, 256x256, 512x512, and
1024x1024 sizes. Segementation time on 64x64 image shows no significant time
difference between CPU and GPU. On larger files, computation time on each
processing unit show different result. Segmentation time on 1024x1024 image,
GPU can process image seven times faster than CPU.