Madiha Arshad1, Mahmood Qureshi1, Omair Inam1, and Hammad Omer1
1Medical Image Processing Research Group (MIPRG), Department of Electrical and Computer Engineering, COMSATS University, Islamabad, Pakistan
1Medical Image Processing Research Group (MIPRG), Department of Electrical and Computer Engineering, COMSATS University, Islamabad, Pakistan
The
proposed Deep k-means can accurately extract tissues from the human brain
images reconstructed from the acquired highly under-sampled data. Moreover, the
proposed method reduces the computational burden by avoiding the tedious job of
creating accurate segmentation masks.
Figure 1: Block diagram of the proposed method: A Deep k-means Based Tissue Extraction from the reconstructed Human Brain MR Image. (A) shows the deep learning approach for image reconstruction and (B) shows the k-means clustering algorithm used for the brain tissue extraction.
Figure
3: Results obtained from the proposed Deep k-means and CG-SENSE
k-means: (A) shows the reconstruction results obtained from U-Net and CG-SENSE,
(B) shows the segmentation results obtained from k-means clustering algorithm. (C-E)
show the extracted white matter, gray matter and CSF from the proposed method and
CG-SENSE k-means.
