Brain tumor boundary detection in MR image with generalized fuzzy operator

Boundary detection in MR image with brain tumor is an important image processing technique applied in radiology for 3D reconstruction. The nonhomogeneities density tissue of the brain with tumor can result in achieving the inaccurate location in any boundary detection algorithms. Recently, some studies using the contour deformable model with regional base technique, the performance is insufficient to obtain the fine edge in the tumor, and the considerable error in accuracy is existed. Moreover, even in some of the normal tissue region, edge created by this method has also been encompassed. In this paper, we propose a new approach to detect the boundary of brain tumor based on the generalized fuzzy operator (GFO). One typical example is used for evaluating this method with the contour deformable model.published_or_final_versio

Closed boundary extraction of cancer cells using fuzzy edge linking technique

ABSTRACT Edge linking is an important task in a boundary extraction problem. In this paper, a fuzzy approach is proposed. The proposed system consists of a fuzzy edge detection module and a fuzzy edge linking module, respectively. The fuzzy edge detector gives a fuzzy membership matrix and an initial edge map. Once the initial edge map is obtained, the pairing of the start and end points of the edges as well as the linking of appropriate segments of edges in the map can be found using the fuzzy edge linking module, as follows: First, for any start point, the corresponding end point is found by searching for the shortest distance between the points. Second, for any start point as a reference point, we search for the greatest fuzzy membership value among its neighbors, and connect the reference point to that neighbor. Then with that neighbor as the new reference point, this step of searching is repeated until the end point is reached. After applying the above technique to all the pairs of start and end points, all the open boundaries will be connected. A simulation evaluation of the proposed technique was carried out on an image "Cancer". The simulation result shows an improvement on the outlines of the cancer cells. The proposed algorithm is simple and low cost, and can be implemented easily in some real-time applications

Interoperable services based on activity monitoring in ambient assisted living environments

Ambient Assisted Living (AAL) is considered as the main technological solution that will enable the aged and people in recovery to maintain their independence and a consequent high quality of life for a longer period of time than would otherwise be the case. This goal is achieved by monitoring human’s activities and deploying the appropriate collection of services to set environmental features and satisfy user preferences in a given context. However, both human monitoring and services deployment are particularly hard to accomplish due to the uncertainty and ambiguity characterising human actions, and heterogeneity of hardware devices composed in an AAL system. This research addresses both the aforementioned challenges by introducing 1) an innovative system, based on Self Organising Feature Map (SOFM), for automatically classifying the resting location of a moving object in an indoor environment and 2) a strategy able to generate context-aware based Fuzzy Markup Language (FML) services in order to maximize the users’ comfort and hardware interoperability level. The overall system runs on a distributed embedded platform with a specialised ceiling- mounted video sensor for intelligent activity monitoring. The system has the ability to learn resting locations, to measure overall activity levels, to detect specific events such as potential falls and to deploy the right sequence of fuzzy services modelled through FML for supporting people in that particular context. Experimental results show less than 20% classification error in monitoring human activities and providing the right set of services, showing the robustness of our approach over others in literature with minimal power consumption