Design of a Knowledge-Based Geographic Information System

A GIS has been designed with limited Functionalities; but with a novel approach in Aits design. The spatial data model adopted in the design of KBGIS is the unlinked vector model. Each map entity is encoded separately in vector fonn, without referencing any of its neighbouring entities. Spatial relations, in other words, are not encoded. This approach is adequate for routine analysis of geographic data represented on a planar map, and their display (Pages 105-106). Even though spatial relations are not encoded explicitly, they can be extracted through the specially designed queries. This work was undertaken as an experiment to study the feasibility of developing a GIS using a knowledge base in place of a relational database. The source of input spatial data was accurate sheet maps that were manually digitised. Each identifiable geographic primitive was represented as a distinct object, with its spatial properties and attributes defined. Composite spatial objects, made up of primitive objects, were formulated, based on production rules defining such compositions. The facts and rules were then organised into a production system, using OPS5

Brain Tissue Classification from Multispectral MRI by Wavelet based Principal Component Analysis

In this paper, we propose a multispectral analysis system using wavelet based Principal Component Analysis (PCA), to improve the brain tissue classification from MRI images. Global transforms like PCA often neglects significant small abnormality details, while dealing with a massive amount of multispectral data. In order to resolve this issue, input dataset is expanded by detail coefficients from multisignal wavelet analysis. Then, PCA is applied on the new dataset to perform feature analysis. Finally, an unsupervised classification with Fuzzy C-Means clustering algorithm is used to measure the improvement in reproducibility and accuracy of the results. A detailed comparative analysis of classified tissues with those from conventional PCA is also carried out. Proposed method yielded good improvement in classification of small abnormalities with high sensitivity/accuracy values, 98.9/98.3, for clinical analysis. Experimental results from synthetic and clinical data recommend the new method as a promising approach in brain tissue analysis.