Fuzzy Hybrid Method for the Reconstruction of 3D Models Based on CT/MRI Data

Image analysis plays a vital role in modern computeraided systems. Images can be obtained from different modalities, such as cone beam computed tomography (CBCT), magnetic resonance imaging (MRI), positron emission tomography (PET), singlephoton emission computed tomography (SPECT), ultrasound, etc. These can provide three-dimensional (3D) image datasets that contain accurate information for the generation of surface 3D models, even when compared to optical 3D digitizing methods [1]. Surface 3D models are a very useful resource for accurate diagnosis, but also for further action such as preparation of surgeries, designing different types of implants, etc. The most critical step for the generation of a surface 3D model is the accurate segmentation for extracting objects of interest from the surroundings, thus enabling 3D surface reconstruction [2] and [3]. Information acquired from medical images has a significant impact on proper diagnosis and treatment. For this purpose, the segmentation of medical images is performed, which can be either manual or automatic [4]. Nowadays, due to the large amount of data obtained using medical imaging systems, methods used for semi-automatic or fully automatic segmentation are more favourable but still refer to manual results for verification and training purposes [5]. When a 2D image is acquired, some information may be lost, and this information loss degrades the image quality, and more importantly affects the accuracy of segmentation and geometry reconstruction, eventually endangering proper diagnosis. Therefore, accurate reconstruction of geometry is required and depends on several factors, including spatial resolution, which is determined by the layer thickness [6], and slice thickness, which affects loss of resolution quality on the reconstructed data [7]. Without some form of image enhancement, segmentation of medical images becomes very difficult and sometimes does not provide accurate results. This occurs as a result of the vague structures in poorly displayed medical images, or with the presence of homogenous surrounding structures. Thus, to improve the segmentation accuracy, it is necessary to preprocess image and to enhance its quality. With the breakthrough of additive manufacturing (AM) technologies in the medical field, it enabled physical fabrication of anatomical structures, which strongly Fuzzy Hybrid Method for the Reconstruction of 3D Models Based on CT/MRI Data Sokac, M – Vukelic, D. – Jakovljevic, Z. – Santosi, Z. – Hadzistevic, M. – Budak, I. Mario Sokac1 – Djordje Vukelic1,* – Zivana Jakovljevic2 – Zeljko Santosi1 – Miodrag Hadzistevic1 – Igor Budak1 1University of Novi Sad, Faculty of Technical Sciences, Serbia 2University of Belgrade, Faculty of Mechanical Engineering, Serbia