Automated inspection of semiconductor defect data has become increasingly important over the past several years as a means of quickly understanding and controlling contamination sources and process faults, which impact product yield. To address the issue of too much data and too little time, automation technologies in defect detection and review are being developed by universities, laboratories, industry, and semiconductor equipment suppliers. In this thesis, a new rule-based approach is proposed to segment defect images. Several segmentation techniques already exist but they often focus on the constraints of a specific application and therefore they lack of generality and flexibility. This limits the use of computer vision in all those tasks where the visual data content and the purpose of the defect analysis are not known a priori. Moreover, the limited generality increases the costs for the design of unsupervised defect image analysis systems.