Online First Articles; Ahead of issue publication
Calculation of Talocrural Joint Axis Motion by Approximating Trochlea Tali with Conical Side Surface
1Kenta Nomura, 2Shinichi Kosugi, 3Yasuhito TANAKA, 1Hiroshi Takemura
1Department of Mechanical Engineering, Tokyo University of Science, Japan;
2Department of Orthopedic Surgery, Nara Prefectural Seiwa Medical Center, Japan;
3Department of Orthopedic Surgery, Nara Medical University, Japan;
The motion of the talus, the most complex and important bone in ankle motion, is determined by the geometric characteristics of the articular surface of the talocrural joint, known as the trochlea tali. Therefore, modeling the geometric features of the trochlea tali is important for various fields.
The purpose of this study is to approximate the rotation axis of the talocrural joint, which is important in the motion of the ankle foot with a conical model. In this experiment, a foot in four types of postures was photographed using computerized tomography (CT). An approximate cone was generated from point cloud data of the trochlea tali, obtained in this CT imaging experiment. In addition, the relationship between the rotation axis of the talus obtained by this experiment and the
approximated cone was confirmed by this study. The results show that the axis of rotation of the talocrural joint moves along the side surface of the approximated cone, formed by two protruded shapes of trochlea tali. This suggests that the proposed method can be used to model the rotation axis of the talocrural joint with the side surface of the cone.
Keywords: Trochlea tali; Modeling with infinite cone side surface; Rotation axis of the talocrural joint; Computerized tomography; Globally optimal iterative closest point.
Full Text Article
Real and Complex Valued Ripplet-I Transform for Medical Image Denoising and Analysis of Thresholding Constants and Scales Effects
1Hüseyin Yaşar, 2Murat Ceylan
1Ministry of Health of the Republic of Turkey, Ankara, Turkey;
2Department of Electrical and Electronics Engineering, Selcuk University, Konya, Turkey;
Medical image processing is an important diagnostic tool in the field of medical. Medical images might be affected by the noises that manipulate the resolution negatively during screening or transmission. These images need to be eliminated so as not to affect the diagnosis success negatively. In medical image denoising studies, using the multi-resolution analysis coefficients is a widely appreciated method. This study tested the success rate of real and complex valued ripplet-I transform for medical image denoising. Thanks to this study, the complex version of the newly suggested ripplet-I transform whose real version was used formerly in various studies was used in a medical image denoising application the first time. In the study tested with 40 liver images, 40 retinal images and 322 mammographic images, peak signal-to-noise ratio (PSNR), mean structural similarity index (MSSIM) and feature similarity index (FSIM) were utilized to compare the successes of image denoising. In the wake of study, it was seen that the complex valued ripplet-I (CVR-I) transform gave better results than the real valued ripplet-I (RVR-I) transform when used in the same image denoising algorithm. This study also examined the effects that the changes in scale and thresholding constant values have on the medical image denoising results, thus making this study appear as a guideline.
Keywords: Real and complex valued ripplet-I transform; Medical image denoising; Thresholding constant; Peak signal-to-noise ratio (PSNR); Mean structural similarity index (MSSIM); Feature similarity index (FSIM).
Full Text Article