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 detection of tumor in mri using vector quantization segmentation with matlab code

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.
ABSTRACT

Segmenting a MRI images into homogeneous texture regions representing disparate tissue types is often a useful preprocessing step in the computer-assisted detection of breast cancer. That is why we proposed new algorithm to detect cancer in mammogram breast cancer images. In this paper we proposed segmentation using vector quantization technique. Here we used Linde Buzo-Gray algorithm (LBG) for segmentation of MRI images. Initially a codebook of size 128 was generated for MRI images. These code vectors were further clustered in 8 clusters using same LBG algorithm. These 8 images were displayed as a result. This approach does not leads to over segmentation or under segmentation. For the comparison purpose we displayed results of watershed segmentation and Entropy using Gray Level Co-occurrence Matrix along with this method.
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