Capsule network based super resolution method for medical image enhancement

Abstract

Medical imaging has been one of the most attentive research and development areas since the 1950s, particularly due to the contribution to disease diagnosis. Despite the fact that imaging technologies have been advanced in multiple ways, yet resolution limitations can be observed. To overcome the resolution limitations, various image enhancement techniques have been used. Image Super-Resolution (SR) is the latest technique in the list to achieve higher resolution with much lower resolution images. Earlier, frequency based and interpolation based SR techniques were used for SR. The afterward achievements in SR techniques are obtained via Convolution Neural Network (SRCNN) based methods and have several flaws. Capsule net (Caps Net) is the state of the art alternative methodology for the problems which were previously solved by CNN. One recent attempt was made to assess the Caps Net for SR task. This new area has a lot to be explored. Especially the time inefficiencies of this approach should be addressed along with accuracy improvements. In this research several capsule network routing mechanisms have been investigated for Super Resolution pipeline with a medical image dataset. Standard Dynamic Routing and Expectation Maximization Routing methods are re-configured to improve the accuracy. Above all, a novel integration of state of the art routing mechanism, Inverted Dot Product based Attention Routing mechanism is introduced for Super Resolution task. With 300,000 medical image training pairs and 2,500 evaluation pairs, every model was evaluated. Along with different image quality indexes, it was shown that the Dynamic Routing based method outperformed all methods and the newest Attention Routing based approach has shown similar image quality performance to that of the state of the art method FSRCNN and less time complexity to that of the existing Caps Net based approaches. This implies that clinicians can use this system effectively in a clinical setting.