08-09-2015, 02:40 PM
1) Perform PCA to obtain the K–L transformation matrix for
the target VOI, determine the reduced dimension P for the
local intensity vector space, and calculate the K–L transformed
local intensity vector ωi = {ωi1, ωi2, . . . , ωiP }
for each voxel i = 1, . . . , I.
2) Set the classification threshold T as the maximum PC
variance, and set a value for the maximum class number
K based on prior anatomical knowledge.
3) i = 1, set the first voxel label v1 = 1, its local intensity
vector ω1 as the representative vector c1 for the first class,
n1 = 1 as the number of voxels belonging to class 1, and
K = 1 as the current number of classes.
4) i = i + 1, calculate the squared Euclidean distance
d(ωi, ck ) between the local intensity vector ωi of the
current voxel and the representative vector ck for each
existing class k = 1, . . . , K.
5) Let d(ωi, cm) = min1≤k≤K{d(ωi, ck )}, if d(ωi, cm) <
T or K = K, the label for the ith voxel is vi = m. cm is
updated by cm = (nm ∗ cm + ωi)/(nm + 1), and nm =
nm + 1.Otherwise, a newclassK = K + 1is generated
with representative vector cK = ωi , and the current voxel
is labeled as vi = K s.t. K <= K.
6) Repeat from step 4) until i = I to complete a whole scan.
7) If K < K, repeat steps 1) to 6) for another whole scan
while setting the classification threshold T to be the variance
of the second or higher-order PC until reaching the
desired number of tissue types K = K.
I want to know how can i develope this algorithm in matlab.