(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 8, No. 8, 2017 83 | Page www.ijacsa.thesai.org Improvement of Radial basis Function Interpolation Performance on Cranial Implant Design Ferhat Atasoy Computer Engineering Department Karabuk University, Karabuk, Turkey Baha Sen Computer Engineering Department Ankara Yildirim Beyazit University, Ankara, Turkey Fatih Nar Computer Engineering Department Konya Food and Agriculture University, Konya, Turkey Ismail Bozkurt Neurosurgery Department Cankiri State Hospital, Cankiri, Turkey Abstract—Cranioplasty is a neurosurgical operation for repairing cranial defects that have occurred in a previous operation or trauma. Various methods have been presented for cranioplasty from past to present. In computer-aided design based methods, quality of an implant depends on operator’s talent. In mathematical model based methods, such as curve- fitting and various interpolations, healthy parts of a skull are used to generate implant model. Researchers have studied to improve performance of mathematical models which are independent from operators’ talent. In this study, improvement of radial basis function (RBF) interpolation performance using symmetrical data is presented. Since we focused on the improvement of RBF interpolation performance on cranial implant design, results were compared with previous studies involving the same technique. In comparison with previously presented results, difference between the computed implant model and the original skull was reduced from 7 mm to 2 mm using newly proposed approach. Keywords—Cranioplasty; interpolation on medical images; radial basis function interpolation; symmetrical data I. INTRODUCTION Cranioplasty is a neurosurgical operation for repairing cranial defects that have occurred in a previous operation or trauma. This operation is important for both aesthetics and health [1]. Encephalitis, cerebritis, trauma, malignancy, hydrocephalus, epilepsy, mental or psychological disorders are associated with cranial bone defects [2], [3]. The main goals of cranioplasty are protection of intracranial contents and providing normal development and growth of the brain in children [4]. Various metals, ceramics, synthetic materials can be used for cranioplasty. The task is to complete the damaged skull bone with the selected material. Cranioplasty operations are performed on frontal bone, parietal bone, occipital bone, sphenoid bone, and portion of the temporal bone [1]. The oldest cranial operations dates back to 7000 B.C. in ancient Egypt [1], [5]. Archaeological finds indicate that inorganic materials have been used much earlier than organic materials. Bones were used for cranioplasty from a wide population of donor groups such as rib bone and tibia, in the 19th century. Although many different materials and methods have been described up to now, there is no consensus on which method is better [1]. An ideal implant material must have following features for cranioplasty applications [1], [5]:  It must close and fit the defected part of the skull completely  Not dilated with heat  Resistance to infections  Radiolucency  Lightweight and compatible with tissues (biocompatibility)  Easy to shape  Ready to use  Not expensive  Resistant to biomechanical procedures  Easily sterilized  Non-inflammatory and non-carcinogenic Thickness of implant varies according to the material. Therefore, implant mold should be specially created for implant material. While surface interpolation may be a good choice to manufacture titanium implant, it may not be right choice for cement-based materials such as methacrylate. Computer-aided manufacturing of cranial implants have come into use with increasing processing speed of computers and development on imaging and modeling. In previous studies, implants were created with mathematical model or using solid modeling software. Carr et al. designed cranial implants with radial basis function (RBF) based surface interpolation method on computed tomography (CT) images. In the study, they began with detection of defected part of skull and a height map was created for the defected part and nearby. Unknown areas (greater values on height map) were computed with RBF by This study was supported by Ankara Yildirim Beyazit University as pre- scientific research project with 587 project number.