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|Title:||Improving the process of making rapid prototyping models from medical ultrasound images||Authors:||Vaezi, Mohammad
Chua, Chee Kai
Chou, Siaw Meng
|Issue Date:||2012||Source:||Vaezi, M., Chua, C. K., & Chou, S. M. (2012). Improving the process of making rapid prototyping models from medical ultrasound images. Rapid Prototyping Journal, 18(4), 287-298.||Series/Report no.:||Rapid prototyping journal||Abstract:||Purpose – Today, medical models can be made by the use of medical imaging systems through modern image processing methods and rapid prototyping (RP) technology. In ultrasound imaging systems, as images are not layered and are of lower quality as compared to those of computerized tomography (CT) and magnetic resonance imaging (MRI), the process for making physical models requires a series of intermediate processes and it is a challenge to fabricate a model using ultrasound images due to the inherent limitations of the ultrasound imaging process. The purpose of this paper is to make high quality, physical models from medical ultrasound images by combining modern image processing methods and RP technology. Design/methodology/approach – A novel and effective semi-automatic method was developed to improve the quality of 2D image segmentation process. In this new method, a partial histogram of 2D images was used and ideal boundaries were obtained. A 3D model was achieved using the exact boundaries and then the 3D model was converted into the stereolithography (STL) format, suitable for RP fabrication. As a case study, the foetus was chosen for this application since ultrasonic imaging is commonly used for foetus imaging so as not to harm the baby. Finally, the 3D Printing (3DP) and PolyJet processes, two types of RP technique, were used to fabricate the 3D physical models. Findings – The physical models made in this way proved to have sufficient quality and shortened the process time considerably. Originality/value – It is still a challenge to fabricate an exact physical model using ultrasound images. Current commercial histogram-based segmentation method is time-consuming and results in a less than optimum 3D model quality. In this research work, a novel and effective semi-automatic method was developed to select the threshold optimum value easily.||URI:||https://hdl.handle.net/10356/97254
|ISSN:||1355-2546||DOI:||http://dx.doi.org/10.1108/13552541211231716||Rights:||© 2012 Emerald Group Publishing Limited.||Fulltext Permission:||none||Fulltext Availability:||No Fulltext|
|Appears in Collections:||MAE Journal Articles|
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