Infected region recognition in human body members based on wavenet with minimum distance

Abstract EN

Image identification plays a great role in industrial, remote sensing, medical and military applications.

It is concerned with the generation of a signature to the image.

This work proposes a dynamic program (use Neural Network) to classify the texture of human member image then identify whether the member is infected or not.

The program has the ability of determining which part of that member is infected depending on the comparison between the healthy member images stored in advance with a test image.

The first step is to make approximation to the image using wavelet network (Wave net) technique.

Through this technique we shall get an approximated image with reduced data.

In addition, we shall get implicit information to that image.

The second step is to subdivide the resultant image from the first step into 16 equally subparts then deal with each subpart as a unique image.

Finally, in the third step, the minimum distance (Mahalanobias Distance) approach is employed for subpart identification.

All programs are written using MATLAB VER.

6.5 ackage.