Medical imaging is undoubtedly one of the most impacting technologies in modern society.
In the last few decades, various domains of medical imaging paradigm have advanced …

Medical image processing typically deals with the exploration of several medical image
datasets for attaining an effective solution in diagnosing the affected patients. Medical image …

Due to the exponential advancement of information technology, computer, storage systems
and networking technology, medical devices and clinical diagnosis has acquired …

Medical imaging is playing a pivotal role in the domain of medical diagnosis. But the major
problems related to medical images is that most of the images (of different modalities) are …

Dynamic threshold neural P systems (DTNP systems) are a distributed parallel computing
model with an interesting mechanism involving the cooperative spiking of neurons in a local …

The synthesis of visual information from multiple medical imaging inputs to a single fused
image without any loss of detail and distortion is known as multimodal medical image fusion …

This thesis identifies some of the features and major limitations of current Artificial Neural
Network (ANN) architectures and learning laws and explore an efficient method of …

The process of generating quality visual information by integrating the salient features of the
medical images captured from multi-modalities is said to be medical image fusion. In such a …

According to the pros and cons of contourlet transform and multimodality medical imaging,
here we propose a novel image fusion algorithm that combines nonlinear approximation of …

Medical imaging is a process of acquiring physiological and functional information on body
parts or organs from their images to identify and study diseases and disorders. The image is …