Result of Medical Diagnosis System Using Ripplet Transform

Hagote Kiran Kedarnath, B. S. Borkar

Abstract: Content-based image retrieval (CBIR) approach permits the user to extract a picture from an enormous database based mostly upon a question. An efficient and effective retrieval performance is achieved by selecting the simplest transform and classification techniques. However, the current transform techniques like Fourier Transform, cosine transform, wavelet transform suffer from discontinuities like edges in pictures. To overcome this disadvantage use Ripplet Transform (RT) has been implemented along with the neural network based mostly classifier referred to as Multilayered perceptron (MLP) for locating a good retrieval of image. Medical image fusion using PCNN and modified spatial frequency based on the ripplet transform type I. the source medical picture are divide by discrete RT (DRT), the low frequency sub bands (LFSs) are fused using the max selection rule. For the fusion of high frequency subbands (HFSs) a PCNN model is utilized. In the proposed medical diagnosis system use different technique like Mutual Information (MI), Spatial Frequency (SF) and Entropy (EN).

Keywords: content-based image retrieval CBIR, Image Fusion, Ripplet Transform, PCNN