Bone suppression of chest radiographs (CXRs) is potentially useful for diagnosing lung diseases for radiologists and computer-aided diagnosis. This paper presents a cascaded convolutional network model in wavelet domain (Wavelet-CCN) for bone suppression in single conventional CXR. Wavelet coefficients are sparse and suitable as the output of convolutional network. The convolutional networks are trained to predict the wavelet coefficients of bone images from the wavelet coefficients of CXRs, using real two-exposure dual energy subtraction (DES) CXRs as training data. By combining the multilevel wavelet decomposition and a cascaded refinement framework, the Wavelet-CCN model can work automatically with a multi-scale approach and progressively refine the prediction in terms of accuracy and spatial resolution. Compared with previous work of CamsNet model which preforms bone prediction in gradient domain, the Wavelet-CCN model predicts the wavelet coefficients to reconstruct bone images and can avoid the inconsistent background intensity caused by 2D integration of gradients. The predicted bone image is subtracted from the original CXR to produce a soft-tissue image. The Wavelet-CCN model and its variants with different wavelet basis are evaluated on a dataset that consists of 504 cases of real two-exposure DES CXRs (404 cases for training and 100 cases for test). Experimental results show that among all the variants and different wavelet bases, the Wavelet-CCN model with Haar wavelet performs best. The average peak signal-to-noise ratio and structural similarity index of the soft-tissue images produced by the proposed Wavelet-CCN model are both higher than those of the previous CamsNet model in gradient domain, reaching values of 39.4 (±0.94) dB and 0.977 (±0.004), respectively. The results also demonstrate that the Wavelet-CCN model can process the CXRs acquired by four types of X-ray machines. INDEX TERMS Bone suppression, convolutional networks, chest radiographs, wavelet transform.
Mosaicking of retinal images is potentially useful for ophthalmologists and computer-aided diagnostic schemes. Vascular bifurcations can be used as features for matching and stitching of retinal images. A fully convolutional network model is employed to segment vascular structures in retinal images to detect vascular bifurcations. Then, bifurcations are extracted as feature points on the vascular mask by a robust and efficient approach. Transformation parameters for stitching can be estimated from the correspondence of vascular bifurcations. The proposed feature detection and mosaic method is evaluated on retinal images of 14 different eyes, 62 retinal images. The proposed method achieves a considerably higher average recall rate of matching for paired images compared with speeded-up robust features and scale-invariant feature transform. The running time of our method was also lower than other methods. Results produced by the proposed method superior to that of AutoStitch, photomerge function in Photoshop cs6 and ICE, demonstrate that accurate matching of detected vascular bifurcations could lead to high-quality mosaic of retinal images.
The main differences between American and Chinese higher education are displayed in administrative system, teaching methods, curriculum, assessment and moral education. However, the two countries also have enlightened each other for the past periods in educational idea and other aspects of higher education. The two countries both have advantages and shortcomings, so they open to each other and take into some advanced ideas and measures that will make great contributions to the rapid development of higher education
Automatic segmentation of ulna and radius (UR) in forearm radiographs is a necessary step for single X-ray absorptiometry bone mineral density measurement and diagnosis of osteoporosis. Accurate and robust segmentation of UR is difficult, given the variation in forearms between patients and the nonuniformity intensity in forearm radiographs. In this work, we proposed a practical automatic UR segmentation method through the dynamic programming (DP) algorithm to trace UR contours. Four seed points along four UR diaphysis edges are automatically located in the preprocessed radiographs. Then, the minimum cost paths in a cost map are traced from the seed points through the DP algorithm as UR edges and are merged as the UR contours. The proposed method is quantitatively evaluated using 37 forearm radiographs with manual segmentation results, including 22 normal-exposure and 15 low-exposure radiographs. The average Dice similarity coefficient of our method reached 0.945. The average mean absolute distance between the contours extracted by our method and a radiologist is only 5.04 pixels. The segmentation performance of our method between the normal- and low-exposure radiographs was insignificantly different. Our method was also validated on 105 forearm radiographs acquired under various imaging conditions from several hospitals. The results demonstrated that our method was fairly robust for forearm radiographs of various qualities.
There are two steps in using 3D computer vision to extract metric information from 2D images. Camera calibration is the first step, and this paper analyses and compares the three popular calibration methods: typical linear calibration, Tsai's two-stage calibration and Zhang's planar pattern calibration by measuring the position of space points. It is concluded that Zhang's method has the best practicability and validity, and this has been verified by experiment. In the structured light vision system, the accuracy of center position of the stripe is of great importance for that of the whole measuring system. Processing the image of stripe is the second important step. We adopted an improved arithmetic based on Harris to extract the points of the planar pattern. At last, using it to obtain a cylinder's radius and prove it simple and available.
Abstract-Catalytic hydrolytic reaction of ammonia borane (AB) is regarded as safe and efficient way to produce hydrogen. However, the development of heterogeneous catalysts with both high catalytic performance and low cost for this hydrolytic reaction is still a great challenge. In this work, we have developed a novel catalyst for the hydrolysis of ammonia borane, Ca 0.5 Mg 0.5 Co 2 O 4 nanosheets composed of nanoparticles, which was characterized by X-ray powder diffractometer, field emission scanning electron microscope, transmission electron microscope, and volumetric analyzer. In the AB hydrolysis, the hydrogen production rate will increase as the increase the NaOH dosage. At NaOH dosage of 1.6 g, the turnover frequency is 4.8 mol hydrogen min -1 mol cat -1 . It is also found that a high catalyst dosage and a high reaction temperature are favorable for the fast hydrogen release from AB solution.
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