Analysis and Comparison of Developed 2D Medical Image Database Design using Registration Scheme, Retrieval Scheme, and Bag-of-Visual-Words

Abstract

There is certain to be a significant increase in the use of registration, retrieval and registration based retrieval of medical images in healthcare. But there is a related major concern that these initiatives are narrowly policy or technology led rather than evidence based or case based reasoning. Or else, either they will be based on old paradigm structures without considering the new paradigms that they create. It is planned to look at the image registration and retrieval from a unified perspective based on their performance for clinical diagnosis and treatment. It is concluded with an experiment carried out on histopathology and five types of anatomic (radiology) images. The results showed that this approach works better with more images from the Bag-Of-Visual words and Affine with B-Spline registration based retrieval for modality and histopathological images. Bag-of-Visual Words is more suitable for histopathological images than registration based retrieval techniques.

Hybrid transformation with 1’st order statistics for medical image analysis and classification

“…The input layer, the output layer, and a number of hidden layers make up the three ANN [21]. The network's accuracy is determined by the training procedures, which depend on the importance of the features, including the statistical or gradient techniques (applied in this work) [22]. The network is built using computed layer weight values to lessen the difference between calculated results and predicted values.…”