Search

As a new research focus in biometrics, palm vein recognition has attracted people's attention because of high security, liveness‐detection, user acceptability and convenience. Palm vein recognition exhibits high security, as it uses the network of blood vessels underneath palm skin for recognition. As palm vein is interior biological information of the body, vein patterns are much harder for intruders to copy compared to other biometric features. Palm vein is mostly invisible to the human eyes; they are commonly captured under near‐infrared light. Under its natural state, the veins of the palm are concealed naturally and, for most of time people's hands are in the gesture of half fist. This study presents a comprehensive overview of recent research progress of palm vein recognition, from the basic background knowledge to data acquisition, public database, preprocessing, feature extraction and matching. In addition, the study focuses on palm vein‐related fusion. Ultimately, the authors discuss the challenges and future perspectives in palm vein recognition for further works. [ABSTRACT FROM AUTHOR]

Computed aided diagnosis of lung CT data is a new quantitative analysis technique to distinguish malignant nodules from benign ones. Nodule growth rate is a key indicator to discriminate between benign and malignant nodules. Accurate nodule segmentation is the essential for calculating the nodule growth rate. However, it is difficult to segment juxta-vascular nodules, due to the similar gray levels in nodule and attached blood vessels. To distinguish the nodule region from the adjacent vessel region, a flowing direction feature, referred to as the direction of the normal vector for a pixel, is introduced. Since blood is flowing in one single direction through a vessel, the normal vectors of pixels in the vessel region typically point in similar orientations while the directions of those in the nodule region can be viewed as disorganized. The entropy value of the flowing direction features in a neighboring region for a vessel pixel is smaller than that for a nodule pixel. Moreover, vessel pixels typically have a larger geodesic distance to the nodule center than nodule pixels. Based on k-means clustering method, the flow entropy, combined with the geodesic distance, is used to segment vessel attached nodules. The validation of the proposed segmentation algorithm was carried out on juxta-vascular nodules, identified in the Chinalung-CT screening trial and on Lung Image Database Consortium (LIDC) dataset. In fully automated mode, accuracies of 92.9% (26/28), 87.5%(7/8), and 94.9% (149/157) are reached for the outlining of juxta-vascular nodules in the Chinalung-CT, and the first and second datasets of LIDC, respectively. Furthermore, it is demonstrated that the proposed method has low time complexity and high accuracies. [ABSTRACT FROM PUBLISHER]

This paper proposes a new fractional order total variation (TV) denoising method, which provides a much more elegant and effective way of treating problems of the algorithm implementation, ill-posed inverse, regularization parameter selection and blocky effect. Two fractional order TV-L models are constructed for image denoising. The majorization-minimization (MM) algorithm is used to decompose these two complex fractional TV optimization problems into a set of linear optimization problems which can be solved by the conjugate gradient algorithm. The final adaptive numerical procedure is given. Finally, we report experimental results which show that the proposed methodology avoids the blocky effect and achieves state-of-the-art performance. In addition, two medical image processing experiments are presented to demonstrate the validity of the proposed methodology. [ABSTRACT FROM AUTHOR]

Lung segmentation is the premise of the computer aided diagnosis of lung cancer.The traditional segmentation method based on local low-level features can not get the correct result when a tumor is connected with pleura due to their similar computed tomography(CT)values.Moreover,because the big size of tumor leads to the loss of a large part of lung area,the traditional segmentation methods of lung with juxta-pleural nodule whose diameter is less than 3cm are not suitable.Acitve shape model(ASM)combined with prior shape and low level features might be appropriate.But the search steps in conventional ASM is an optimization method based on the least square,which is sensitive to outlier marker points,and it makes profile update to the transition area of normal lung tissue and tumor rather than a true lung contour.To solve the problem,we proposed an improved ASM algorithm.Firstly,we identified these outlier marker points by distance,and then gave the different searching functions to the abnormal and normal marker points.And the search processing should be limited in volume of interesting(VOI).We selected 30 lung images with juxta-pleural tumors,and got the overlap rate with the gold standard as 93.6%.The experimental results showed that the improved ASM could get good segmentation results for the lungs with juxta-pleural tumors,and the running time of the algorithm could be tolerated in clinical.