||General speaking, steps of noise removing, segmentation, and interpolation are often needed for the reconstruction of three-dimensional (3D) medical image. This study focuses on steps of segmentation and interpolation of our oral magnetic resonance (MR) images.|
The main purpose of this study is to reconstruct a 3D tongue from our oral MR images. We used snake algorithm to segment tongue tissues from oral MR images. Then we proposed morphology-based interpolation methods, namely, (1) morphology-based grey level interpolation method, (2) morphology based non-correctional erosion grey level interpolation, and (3) morphology-based correctional erosion grey level interpolation for the interpolated new images.
Our method adopted grey-level interpolation to lift up 2D images to 3D images, and mark the overlap, non-overlap, and background regions. Then morphological inflation and erosion was adopted to interpolate the shape of objects (included mb, mbnc and mbc). After we finished distance code operation, the 3D images were collapsed to 2D images to complete image interpolation. In addition, we utilized mean squared difference (msd), number of site of disagreement (nsd), largest difference of grey level (ldiff), and the location displacement (dispmt) which is sum of false positive and false negative to compare our proposed methods with the linear grey level and shape-based grey level interpolation methods.
Our results showed that mb methods have mixed performance in terms of values of msd, nsd, ldiff, and dispmt. Although the values of msd (185.61), nsd (393638), ldiff (120.67) of our method (mbnc) were slightly larger than those of the shape-based grey level interpolation (181.75, 373661, and 112.67, respectively), the objects which were interpolated by mbnc (which had the lowest dispmt value) had the best smoother contours among all methods. The smoothness of contours of the interpolated objects is the major concern of our application which is the construction of a tongue model that is based on the reconstruction tongue of our MRI data.
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