Segmentation of a Knee Bone in 3D
To quantify and measure the properties of a component in a volume, segmentation is a necessary first step. To segment the bone tissue in an MRT volume, a clustering algorithm is used to achieve a rough segmentation and apply a grow-cut algorithm to obtain the final result.
The volume is preprocessed using MedianFilter to regularize noise. An initial segmentation is performed by clustering voxel intensities using ClusteringComponents. This partitions the data into three regions: empty space, muscle tissue, and the rest, which includes bone, fat, and skin.
Extract the segment with highest mean intensity, which depicts bone, fat, and skin tissue.
GrowCutComponents can provide a nice final segmentation. In order to create two markers for bone and no-bone areas, one can use morphological operations.
The bone marker can be computed by eroding the segment using a radius 4, which deletes all thin skin and fat layers and also erodes some of the bone.
For the no-bone marker, dilate the bone core more than what was eroded and extract the surface around the dilated volume.
Run the 3D grow-cut-algorithm to refine the segmentation.
Visualize the segmentation.
Size and density measurements can be calculated for each segment.