Cardiac Image Analysis based on K Means Clustering by NAVEEN TOKAS

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Submitted By:
NAVEEN TOKAS
M.Tech ECE/2nd Sem
ASET/AUH
Guided By:
Ms.Shruti K.
ASET/AUH

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•Introduction
•Segmentation Techniques
•Introduction to K Means Clustering
•Block Diagram
•Future Scope

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•The heart is an essential organ of the human circulatory system. There should be
a proper functioning of the heart to avoid Cardio Vascular Diseases (CVD).
•In the cardiac image analysis we have to examine the cardiac function, structure
in the left ventricle of the heart which is the main area for the stenosis or
blockage.
• Cardiac Image Analysis could be done with the help of one of the segmentation
techniques known as K Means clustering.
•The heart consists of four parts including left atrium, right atrium and the lower
parts includes left and right ventricles. The most internal layer, i.e. the layer in
contact with the blood, is called the endocardium the outer most layers, i.e. the
layer which envelopes the heart, is called the epicardium. In between the
endocardium and the epicardium is the myocardium, in which the muscular tissue
responsible for the contraction of the heart.
INTRODUCTION:

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Fig 01: Structure of Heart

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SEGMENTATION:
•Segmentation is an operation that is used to portioning the images according to similarity,
discontinuity or by determining the edges to analyze the information with the help of
multiple partitions. It is an essential part that is used in medical image analysis. The
different techniques under segmentation that used in the cardiac image analysis are
categorized into following:
•Thresholding Methods
•Region Based Methods
•Edge Based Methods
•Clustering Methods

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• Clustering Method refers to the pixels having similar properties grouped together known as
clusters. Grouping is based on maximizing the similarities, if inter class similarities is increase
then the quantity of clusters are automatically increased to get the optimum results.
•In the K Means Clustering the letter K is referred to the number of clusters that is to be
decided in the starting of the algorithm. In this we have to define the K centers one for each
cluster. The center should be far from the others so that the distance could be calculated easily
and the data points could predict their clusters very precisely.
Steps of K Means Clustering:
•The number of clusters must be known to K.
•The number of clusters center should be far from each other.
•Consider each data point and assign it to the number of cluster which is closest.
•Re-Calculate the cluster center by finding mean of the data points
•Repeat third and forth step till shifting centers among the cluster could be observed.

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FUTURE SCOPE:
•Every technique has its advantages or disadvantages like in K Means
Clustering has an advantage of best implementation technique under
segmentation but has an disadvantage of non automatically of chosen
the ROI (Region of Interest) if this could be find automatically then the
probability of stenosis would be less.
•Cardiac Diseases always be a part of life and this method definitely
helps the doctor to cure CV diseases through the MRI and CT scan
means medical image analysis. It definitely help to calculate the
parameters or analysis the data by the radiologist.