Improving tibia and femur segmentation based on double labelling technique

Abstract

X-Ray image contains very important information. The information is useful for medical field to diagnose or to analyze any disease or malfunctioned cell. In the analyzing of x-ray image based on computer aided application, object segmentation is very important first step, including tibia and femur segmentation which are part of knee segmentation. Segmentation based Active Shape Model (ASM) can not give perfect result for tibia and femur segmentation. Original version of ASM can not separate each object (tibia and femur) so it creates connection between two segmented object which is disturb the information for image analyzing. To solve this problem, a recovery method for segmentation result based on double labelling object is proposed. Different from other labelling techniques, double labelling technique is performed to achieve more reliable results. The proposed method is a simple to implement by using labeling each object and then removing unused object. To complete this proposed method, dilation is implemented to the two objects which is selected by certain criteria. Experimental results are performed on 10 different images. Based on experiment, it shows that the proposed method gives better result for regular form disturbing area and unused area.