Development of Unsupervised methods for medical Image Segmentation

Abstract

Image segmentation is the process of partitioning an image into meaningful parts. Image segmentation is used to locate objects and boundaries in images. It is the process of assigning a label to every pixel in an image such that pixels with the same label share certain visual characteristics.
The need for accurate segmentation tools in medical applications is driven by the increased capacity of the imaging devices. Due to high resolutions and a large number of image slices CT and MRI generated images cannot be examined manually. Furthermore, it is very difficult to visualize complex structures in three-dimensional image volumes without cutting away large portions of, perhaps important, data. Tools, such as segmentation, can aid the medical staff in browsing through such large images by highlighting objects of particular importance. In addition, segmentation in particular can output models of organs, tumors, and other structures for further analysis, quantification or simulation. We have used k means, fuzzy c means for better performance we map the input space onto a self-organising map and then the low dimensional input is clustered using the above methods.
A self-organising map (SOM) is a type of artificial neural network that is trained using unsupervised learning to produce a low-dimensional (typically two-dimensional), discretized representation of the input space of the training samples, called a map. This thesis is devoted to medical image segmentation techniques and their applications in clinical and research settings.