3DPIFCM Segmentation Algorithm for brain MRI

02/05/2020 ∙ by Arie Agranonik, et al. ∙ 8

We present a novel algorithm named 3DPIFCM, for automatic segmentation of noisy MRI Brain images. The algorithm is an extension of a well-known IFCM (Improved Fuzzy Mean Clustering) algorithm. It performs fuzzy segmentation and introduces a fitness function that is affected by proximity of the voxels and by the color intensity in 3D images. The 3DPIFCM algorithm uses PSO (Particle Swarm Optimization) in order to optimize the fitness function. In addition, the 3DPIFCM uses 3D features of near voxels to better adjust the noisy artifacts. In our experiments, we evaluate 3DPIFCM on T1 Brainweb dataset with noise levels ranging from 1 in 3D. The analysis of the segmentation results shows a significant improvement in the segmentation quality of up to 28 noisy images and up to 60 Clustering). We conducted experiments with the parallel version of the algorithm to itself in CPU version and also to IFCMPSO (Improved Fuzzy Mean Clustering using Particle Swarm Optimization), GAIFCM (Genetic Algorithm Fuzzy Mean Clustering) which are genetic algorithm counterparts of the 3DPIFCM algorithm. Our purpose was to test for raw execution speed in seconds of each algorithm given the same hyper parameters and same server. Our speedup results show that the parallel version of the algorithm performs up to 27x faster than the original sequential version and 68x faster than GAIFCM algorithm. We show that the speedup of the parallel version increases as we increase the size of the image due to better utilization of cores in the GPU. Also, we show a speedup of up to 5x in our Brainweb experiment compared to other generic variants such as IFCMPSO and GAIFCM.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 10

page 11

page 16

page 22

page 34

page 40

page 41

This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.