A multilayered agent society for flexible image processing

• Main Author: Hassan, Qais Mahmoud
• Other Authors: Phillips, Roger
• Published: University of Hull 2008
• Subjects: 006.6; Computer science
• Online Access: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.494888

Medical imaging is revolutionising the practise of medicine, and it is becoming an indispensable tool for several important tasks, such as, the inspection of internal structures, radiotherapy planning and surgical simulation. However, accurate and efficient segmentation and labelling of anatomical structures is still a major obstacle to computerised medical image analysis. Hundreds of image segmentation algorithms have been proposed in the literature, yet most of these algorithms are either derivatives of low-level algorithms or created in an ad-hoc manner in order to solve a particular segmentation problem. This research proposes the Agent Society for Image Processing (ASIP), which is an intelligent customisable framework for image segmentation motivated by active contours and MultiAgent systems. ASIP is presented in a hierarchical manner as a multilayer system consisting of several high-level agents (layers). The bottom layers contain a society of rational reactive MicroAgents that adapt their behaviour according to changes in the world combined with their knowledge about the environment. On top of these layers are the knowledge and shape agents responsible for creating the artificial environment and setting up the logical rules and restrictions for the MicroAgents. At the top layer is the cognitive agent, in charge of plan handling and user interaction. The framework as a whole is comparable to an enhanced active contour model (body) with a higher intelligent force (mind) initialising and controlling the active contour. The ASIP framework was customised for the automatic segmentation of the Left Ventricle (LV) from a 4D MRI dataset. Although no pre-computed knowledge were utilised in the LV segmentation, good results were obtained from segmenting several patients' datasets. The output of the segmentation were compared with several snake based algorithms and evaluated against manually segmented "reference images" using various empirical discrepancy measurements.