A Robotic System for Warped Stitching Based Compressive Strength Prediction of Marbles

The amount, distribution, and morphology of the impurities in a marble block determine both its aesthetic quality and compressive strength (CS). Although the former property has been studied extensively, CS prediction is rarely investigated. The existing approaches either use expensive and tedious laboratory tests or employ image processing to individual surface images, which are shown to achieve limited performance. In this paper, a new electromechanical system is designed for full automatic prediction of CS of a marble block on a conveyor belt using all visible surface images, which are acquired by a three-dimensional (3-D) printed robotic arm. The images are used to generate unique reconstructions, which can represent the 3-D structure of the marbles in two-dimensional (2-D) via developed warped stitching based visualizations. Moreover, a novel feature set is proposed for taking advantage of these reconstructions. A total of 157 cubic marble blocks are collected to test the performance of the system using both conventional (neural networks) and emerging (deep) machine learning tools. Adverse effects of small sample size are compensated with data augmentation and transfer learning. It is shown that the system achieves the state-of-the-art prediction results

Design and Configuration of a Medical Imaging Systems Computer Laboratory Syllabus

Medical imaging systems (MIS) constitute an important emergent subdiscipline of engineering studies. In the context of electrical and electronics engineering (EEE) education, MIS courses cover physics, instrumentation, data acquisition, image formation, modeling, and quality assessment of various modalities. Many well-structured MIS courses are available for EEE curricula, providing introduction to all modern diagnostic imaging systems. However, in these courses the laboratory component is limited to image formation and analysis. This paper proposes a wide range of experiments that incorporate various disciplines of EEE education into MIS courses. These experiments are designed to integrate knowledge that students have acquired previously from key EEE courses (such as circuit theory, differential equations, wave theory, energy conversion, control theory, and signal processing) into their new MIS knowledge. The proposed laboratory was adapted to a senior-year MIS class in the EEE Department of Dokuz Eylul Univesity, Turkey. This paper presents the application of these new laboratory experiments, along with the assessment results

Exploring Brushlet Based 3D Textures in Transfer Function Specification for Direct Volume Rendering of Abdominal Organs

Intuitive and differentiating domains for transfer function (TF) specification for direct volume rendering is an important research area for producing informative and useful 3D images. One of the emerging branches of this research is the texture based transfer functions. Although several studies in two, three, and four dimensional image processing show the importance of using texture information, these studies generally focus on segmentation. However, TFs can also be built effectively using appropriate texture information. To accomplish this, methods should be developed to collect wide variety of shape, orientation, and texture of biological tissues and organs. In this study, volumetric data (i.e., domain of a TF) is enhanced using brushlet expansion, which represents both low and high frequency textured structures at different quadrants in transform domain. Three methods (i.e., expert based manual, atlas and machine learning based automatic) are proposed for selection of the quadrants. Non-linear manipulation of the complex brushlet coefficients is also used prior to the tiling of selected quadrants and reconstruction of the volume. Applications to abdominal data sets acquired with CT, MR, and PET show that the proposed volume enhancement effectively improves the quality of 3D rendering using well-known TF specification techniques