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Electroencephalograms (EEGs) are becoming increasingly important measurements of brain activity and they have great potential for the diagnosis and treatment of mental and brain diseases and abnormalities (7). The frequency range of EEG signal is 0 to 64 Hz (8). These nonstationary signals are may contain indicators of current disease, or even warnings about impending diseases. An original investigative move toward for data mining of EEG signal based on continuous wavelet transformation (CWT) investigation is introduced and applied. This paper describes the relevance of wavelet transform (WT) model for categorization of electroencephalogram (EEG) signals which provides a system oriented scientific conclusion. Decision making was performed in two steps: development of a data bank for dissimilar EEG signals using the wavelet transform (WT) and identification of different EEG signals there in the data bank to wrap up a judgment making [14-16]. Two types of EEG signals were used as input patterns and illustrated as case1 and case2. Within this practice the applied signal has been compared in a chronological order with divergent cases in existence in the database [17]. The signal under consideration was evaluated and distinguished the holder 100% truthfully

Mainly image processing is used for detection of objects with feasible number of constraints with different detection meth-odologies is defined used by camera-based detection. This method is used to find correspondence with respect to different objects. So that, in this paper, we propose Novel and simple method which is worked based on different region of interests present in video or image. This method mainly worked based on Histogram Oriented Gradients in image processing events. Our method also uses filtering approach with sequential data presentation to access interested data from image or video. Our experimental results mainly show effective visualization results with respect to different selection of regions.

In medical field applications there is a need for image fusion. A single method of examination about patient condition may not be sufficient to cure the disease. Fusion of CT and MRI images are used in this project. CT image of person gives information about hard tissues where as MRI gives soft tissues details. The fusion of both images gives detail information about the patient. This paper provides a new hybrid fusion method that improves the quality of image. The fusion techniques used in this project are DWT, DSWT and hybrid fusion method which is the combination of any two methods. The multi-modal fused image performance can be compared by using image Quality metrics like PSNR, MSE, RMSE, Entropy and other parameters. By observing the results, it can be proved that proposed method is more efficient for fusion.