THE IMPACT OF USE DIFFERENT TYPE OF IMAGE INTERPOLATION METHODS ON THE ACCURACY OF THE RECONSTRUCTION OF SKULL ANATOMICAL MODEL

Abstract

The paper presents comparative studies concerning the use different methods of image resampling on the accuracy of the reconstruction of oral and maxillofacial geometry. The study was conducted on 14 different patients. In order to extract the oral and maxillofacial models from DICOM data, a region-growing algorithm was used. Thresholds were set above 200 HU to select only craniofacial tissue. After an oral and maxillofacial tissue was segmented from DICOM data, a marching cubes algorithm was used for computing isosurfaces. Model with [Formula: see text][Formula: see text]mm voxel was chosen as the gold standard for models of [Formula: see text][Formula: see text]mm structure and improved with image resampling filters. In the study 7 different kernels were used allowing for filtration. The image resampling filters minimize maximum positive deviations, especially in the occipital, mandible and zygomatic bone area, and maximum negative deviations in the area of the maxilla and nasal bone. Lanczos filtering is the best method of interpolation as compared to other used methods, due to significantly increased visibility of the edges of the segmented structures. As a result of applying this method, partial volume effect artifact was minimized. The distributions and statistical parameters of resampled DICOM data prove that on this stage of data editing, it is possible to increase the accuracy of segmentation and reconstruction of the geometry.