Segmentation of Pathological and Healthy Tissues with Reduced Weighted Vectors Using Hybrid Intelligence Techniques

For a doctor to make a better diagnosis of the tissues he needs perfect repots of the pathological tissues. There are various segmentation methods followed for obtaining the pictures but the proposed algorithm is better than other methods. The proposed method is for detection of pathological tissues and normal tissues and CSF of human brain buy MRI. We will use hybrid intelligence technique by which we can achieve high resolution pixels of the pathological tissues. In this paper we will discuss how we can pictures of the pathological tissues by employing weighed vectors execution method in magnetic resonance method.

Brief on the working algorithm:

In human body the healthy tissues have the ability to control their growth and their divisions and they have a similar array of structure whereas the tumors have capability to regenerate and we can differentiate so that we can identify the healthy and effected tissues. The magnetic resonance technique is capable of showing the difference between the healthy tissues and pathological tissues by using non invasive technique by exposing ionized rays and we can get high contrast images with a good pixel resolution.

Here clustering method is applied on the obtained data which has the capability of processing a grouped data where the similar group of data within the cluster is maximized and the similarity of the data with the other clusters maximized which helps in pattern recognition in an image. Here hybrid intelligence segmentation technique is applied on the processed image which calculates the weighted vectors for the HSOM and FCM which use additional features like entropy and energy and idm. We use fuzzy clustering technique which uses six features and gives the result of the lowest weighted vectors which the image of the tumor in the analysis.

Conclusions:

There are various methods for obtaining the pictures of the tissues but they do not give the exact detail of the infected tissue. Here we apply the clustering technique which gives the values of the low weighted vectors by which on the detail of the tumor is obtained in the computer aided analysis. By using this technique we can make a detailed diagnosis of the tumor which is helpful for the doctors for treating the infected areas.

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