OBJECTIVE. The objectives of our study were to compare MR elastography (MRE) based on gradient-recalled echo (GRE) imaging with spin-echo echo-planar imaging (SEEPI) and rapid fractional (RF)–GRE MRE sequences at 3 T in terms of liver stiffness (LS) and image quality and to evaluate the effect of liver R2* on image quality. MATERIALS AND METHODS. Eighty-one patients underwent 3-T liver MRE with GRE, SE-EPI, and RF-GRE sequences performed in variable order in this study. LS and ROI areas on the LS 95% confidence maps were compared among the three sequences. The relationship between liver R2* and ROI area was investigated. RESULTS. There was no significant difference in mean LS among the three sequences (p = 0.49). Mean ROI area was significantly larger for RF-GRE (18,213 ± 9292 [SD] mm2) than for GRE (13,196 ± 8149 mm2) and SE-EPI (12,896 ± 8656 mm2) (p < 0.0001). Liver R2* was significantly higher among patients with one or more failed sequences (mean ± SD, 116 ± 76 s−1) than for patients with no failed s...

Ancient stelae are considered important historical sources. However, it is a challenge to recognize the inscriptions carved on stelae that have rough surfaces due to prolonged weathering. In this paper, we propose a deep learning-based method to extract engraved regions from the 3D scanned mesh of a stela. First, the uneven distribution of vertices in the mesh is transformed using a mesh subdivision method such that the vertices in the mesh are uniformly distributed. Then, surface features (depth, concave features, and local surface features) are extracted from the subdivided mesh. The depth represents the basic shape of the mesh and is obtained from the aligned mesh. The concave features effectively represent concave regions by using a Frangi filter, and the local surface features have the spin image technique applied to describe the fine shapes of neighboring vertices relative to a vertex. The mesh and the surface features are rasterized into feature images, and engraved regions are segmented from the feature images by using a FC-DenseNet. Our experiments confirm that the proposed method effectively extracts engraved regions of the inscriptions from the rough surface of a stela and it shows robustness to noisy and extremely abraded characters. The proposed method outperformed the second-best method, obtaining an F1 score, IoU, and SIRI of approximately 2.95%, 3.65%, and 7.53%, respectively.

Since consumer devices have different resolution sizes of display panels, digital images are frequently required to be resized for each display adaptation. In this paper, we define DCT-based distortion measures to quantitatively evaluate both scaling distortion and shape distortion generated by downsizing. By utilizing the distortion measures, we propose a novel content-aware image retargeting algorithm. The proposed algorithm can not only satisfy demands of users without the loss of semantic information in images but also is computationally efficient since the digital images are typically stored in DCT-based compression format. Experimental results show that the proposed method can achieve higher subjective quality with significantly reduced computational loads as compared with existing methods.

Archaeological steles having a rough surface due to long periods of weathering make recognizing the inscription difficult. In this paper, we propose a machine learning-based method to extract reliefs for the inscription from a rough stele surface. Relief candidate segments are initially obtained by using a curvature-based method, which include not only actual reliefs but also noises such as dents and scratches. Then, relief segments are selected using a support vector machine classifier that is trained with various features extracted from relief candidate segments. While conventional methods using a single geometric feature easily fail to detect reliefs from the rough surface, the proposed method utilizes 79-dimensional features consisting of appearance-based, cross section-based, and local extrema-based characteristics of each candidate segment to determine whether the segment is relief or not. Using the proposed method, the inscription of the stele Musul-ojakbi made during the Silla Dynasty AD578 were completely recognized. The experimental results demonstrate that the proposed method accurately extracts reliefs and achieves the highest performance on the rough stele data. The performance of the proposed method is about 8.95% and 10.4% higher than the best of the conventional methods in terms of the F1-score and the SIRI, respectively.