Automated Identification and Reconstruction of 3D Images on the Mastoid Air Cell System

Abstract

The mastoid air cell system (MACS) protects the structures in the ear and regulates air pressure in the ear cavity. MACS segmentation is very difficult because of the many overlapping object characteristics in the temporal bone. This study aims to accurately identify and measure MACS areas from CT-scan images of mastoiditis patients. The data tested consisted of 128 CT images from 13 different patients. Images were taken using the Siemens SOMATOM Perspective CT Scanner model 10662260 axially. The extended Adaptive Threshold (eAT) method was developed to produce optimal threshold values for each test image. Furthermore, the eAT results are used to convert the test image into a binary image and then applied to the identification and extraction model automatically for reconstruction from 2D to 3D images. Smaller MACS sizes indicate inflammation of the mastoid bone and require intensive care. Thus, this research can be used to help doctors make the right decisions in carrying out further medical actions.