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.
Citation:
Munasingha, S.C. (2020). Capsule network based super resolution method for medical image enhancement. [Master's theses, University of Moratuwa]. Institutional Repository University of Moratuwa. http://dl.lib.uom.lk/handle/123/21206