Authors :
Abhishek Kumar K, Nagaraja Hebbar N, Jaison Dsouza, Kavya, Manoja Kumara
Volume/Issue :
Volume 5 - 2020, Issue 4 - April
Google Scholar :
https://gg.gg/DF9R4u 564
Scribd :
https://bit.ly/3ggGGek
Abstract :
Segmenting bone tissue automatically in
Magnetic Resonance Imaging(MRI) scans is difficult for
low signal-to-noise ratios, lack of consistency in lighting
conditions is very high, and variability within bone cells
in the scanned images. Current methods available are
either it is partially automatic system or depends on
databases of prior manually segmentation result. Fast
and accurate segmentation of knee structures from MRI
is essential for clinical feasibility of these techniques.
However, manually segmenting bone tissue is time
consuming. The main objective is to first design a
system for automatic segmentation of bone structures
for MRI data with a bilateral filtering and clustering
based methods.
Keywords :
Segmentation, Bilateral filtering, K-means, Fuzzy C-means, MRI.
Segmenting bone tissue automatically in
Magnetic Resonance Imaging(MRI) scans is difficult for
low signal-to-noise ratios, lack of consistency in lighting
conditions is very high, and variability within bone cells
in the scanned images. Current methods available are
either it is partially automatic system or depends on
databases of prior manually segmentation result. Fast
and accurate segmentation of knee structures from MRI
is essential for clinical feasibility of these techniques.
However, manually segmenting bone tissue is time
consuming. The main objective is to first design a
system for automatic segmentation of bone structures
for MRI data with a bilateral filtering and clustering
based methods.
Keywords :
Segmentation, Bilateral filtering, K-means, Fuzzy C-means, MRI.
