In this dissertation work, performed an far-reaching experimental study to minimize time of recognition and verification and increase the great accuracy of signature sample which extract some features from signature sample in their training phase. In previous research on signature verification was based on similarity training of machine used in neural network has shown better accuracy than other techniques but somewhere neural network is complex in operation for example different algorithms for supervised learning. So in this work feature extraction and then it's database has been prepared which takes less time than supervised training in NN is used and for verification, correlation is used to match features of signature with the database which is also easier in operation and faster in processing then BP algorithms in NN. In this system more than 400 signature samples as used for recognition, and it gives far-accuracy than NN system.

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Data mining techniques are widely used for data processing from large data set such as data center and data warehouse. An Image mining technique is a new form of data mining technique in the processing of image data. In the medical field, day by day size of medical images data is increasing. MRI images are one of them. The medical images like as CT scan, MR images are widely used in brain tumor detection, cancer detection from the human body. It is quite challenging and complicated work to detect abnormal cells and tissue such as tumor from MR image data sets. Due to higher importance and de medical image data, it is necessary to process it correctly and efficiently. Image Segmentation has an important role in the field medical image processing. In that way, MRI has become a useful medical diagnostic tool for the diagnosis of brain & o medical images.

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