how to develop vector quantization matlab code used for segmentation

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.