The three-dimensional strain map is useful to elucidate the relationships between microstructures and locally caused deformation and fracture. However, a robust tracking method, which enables error-free tracking in synchrotoron radiation computed tomography (SR-CT) images with more than ten-thousand microstructural features, is not currently available. In this study, a model sample was subjected to a tensile test and scanned by the SR-CT technique in order to develop a new tracking method. The developed tracking methods indicated a high tracking ratio and tracking success ratio of nearly 100% in a wide strain range, which included the assumed strain in a practical experiment. It was confirmed that tracking errors produce an incorrect strain distribution in three-dimensional strain mapping. This study verified the validity of the developed tracking method. The application of this method to high-resolution SR-CT images will make measurement and visualization of the strain distribution possible in three dimensions in bulk materials.
We compared the results ten years after an inverted V-shaped high tibial osteotomy with those of a historical series of conventional closing-wedge osteotomies. The closing-wedge series consisted of 56 knees in 51 patients with a mean follow-up of 11 years (10 to 15). The inverted V-shaped osteotomy was evaluated in 48 knees in 43 patients at a mean follow-up of 14 years (10 to 19). All the patients were scored using the Japanese Orthopaedic Association rating scale for osteoarthritis of the knee and radiological assessment. The pre-operative grade of osteoarthritis was similar in both groups. Post-operatively, the knee function score was graded as satisfactory in 63% (35) of the closing-wedge group but in 89% (43) of the inverted V-shaped osteotomy group. Post-operative radiological examination showed that delayed union and loss of correction occurred more often after a closing-wedge osteotomy than after an inverted V-shaped procedure. Our study suggests that the inverted V-shaped osteotomy may offer more dependable long-term results than traditional closing-wedge osteotomy.
We assessed lip and nose shapes, which played an important role in performance evaluations, before and after cleft lip and nose surgery. We used a noncontact-type semiconductor laser 3-dimensional measurement system on normal Japanese children to obtain 3-dimensional images of noses and lips, which were accurate enough to measure facial shapes. We could rotate these images on the computer, therefore we measured the following points: the distance between the peaks of the Cupid's bow and the width of the labial fissure (frontal view), and the width of the nose and the nasal tip protrusion (basal view). Lip and nose shapes were evaluated for each gender. Additionally, nasolabial angles (NLA) were measured on the lateral views of faces. We classified the morphology of the philtral columns into four types; (1) triangular type, (2) parallel type, (3) concave type, and (4) flat type. We also classified nostril shapes into four types: (1) teardrop type, (2) heart shaped type, (3) round type, and (4) triangular type. We calculated the average of the 3-dimensional coordinate values for each landmark, and created standard facial models of normal Japanese children. Moreover, we invented a new morphological evaluation method before and after cleft lip and nose surgery, using the 3-dimensional data converting and editing software. The method was more feasible to evaluate the assessment cleft lip and nose surgery, by quantifying the surface areas of right and left nostrils and the surface areas of upper and lower vermilions, even now by measuring with the eyes and comparing them.
Building and road detection from aerial imagery has many applications in a wide range of areas including urban design, real-estate management, and disaster relief. The extracting buildings and roads from aerial imagery has been performed by human experts manually, so that it has been very costly and time-consuming process. Our goal is to develop a system for automatically detecting buildings and roads directly from aerial imagery. Many attempts at automatic aerial imagery interpretation have been proposed in remote sensing literature, but much of early works use local features to classify each pixel or segment to an object label, so that these kind of approach needs some prior knowledge on object appearance or class-conditional distribution of pixel values. Furthermore, some works also need a segmentation step as pre-processing. Therefore, we use Convolutional Neural Networks(CNN) to learn mapping from raw pixel values in aerial imagery to three object labels (buildings, roads, and others), in other words, we generate three-channel maps from raw aerial imagery input. We take a patch-based semantic segmentation approach, so we firstly divide large aerial imagery into small patches and then train the CNN with those patches and corresponding three-channel map patches. Finally, we evaluate our system on a large-scale road and building detection datasets that is publicly available.
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