Abnormality Feature Extraction in the Spinal
CordMRI Using K-Means Clustering
In this project author is describing concept to detect
abnormalities from spinal cord by extracting features from MRI images and then
calculating histogram and applying KMEANS algorithm on gray scale images. Histogram
are the pixel intensity values obtained from the images and it has 0 to 255
(black and white pixels) colour ranges. If there is large number of white
pixels encounter in MRI then there is chance of tumour in the image. After
finding variations in histogram values we can apply KMEANS algorithm to
separate black and white colours into different clusters. All white colour
cluster pixels can be used to identify abnormalities in MRI image.
This project consists of 3 modules:
1)
Upload MRI
Images: Using this module we can upload MRI images to the application.
2)
Histogram
Calculation: Using this module we will extract pixels from images using python
OPENCV algorithm and then calculate pixel intensity and put in an array to form
histogram. If all pixels in histogram contains only black colour then no abnormal
changes detected in MRI image and application will stop here only. If white
colour pixels encountered inside histogram then abnormal MRI image detected and
then we can apply KMEANS algorithm to detect abnormal part in MRI.
3)
KMEANS Algorithm:
On extracted pixels we will apply KMEANS algorithm to separate different
colours in different clusters and then extract and plot all white pixels to
detect abnormal patch in MRI.
About histogram and KMEANS algorithm you can read concept in
paper. All images used in this project are extracted from base paper and put
inside MRI folder. You can upload image from MRI folder and run application.
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