In the medical domain, digital images are produced in ever-increasing quantities, which offer great opportunities for
diagnostics, therapy and training. So how to manage these data and utilize them effectively and efficiently possess
significant technical challenges. Thus, the technique of Content-based Medical Image Retrieval (CBMIR) emerges as the
times require. However, current CBMIR is not sufficient to capture the semantic content of images. Accordingly, in this
paper, an innovative approach for medical image knowledge representation and retrieval is proposed by focusing on the
mapping modeling between visual feature and semantic concept. Firstly, the low-level fusion visual features are
extracted based on statistical features. Secondly, a set of disjoint semantic tokens with appearance in medical images is
selected to define a Visual and Medical Vocabulary. Thirdly, to narrow down the semantic gap and increase the retrieval
efficiency, we investigate support vector machine (SVM) to associate low-level visual image features with their highlevel
semantic. Experiments are conducted with a medical image DB consisting of 300 diverse medical images obtained
from the Hei Longjiang Province Hospital. And the comparison of the retrieval results shows that the approach proposed
in this paper is effective.
Robust real-time tracking of non-rigid objects is a challenging task. The difficulty in visual tracking is how to match
targets from frame to frame quickly and reliably. Mean shift algorithm (MSA) is a typical nonparametric evaluation
algorithm that needs great computation. Some scholars join Kalman filter to perform state prediction in the mean shift
algorithm for reducing the computing of template matching. However, traditional Kalman filter sometimes can't track
human movement very accurately because of the particularity of human joint. While wavelet moment has the
multiresolution properties in addition to the invariant to the translation, scaling and rotation, so it is suitable for
differentiating the details of the motion objects. Therefore, Kalman-mean shift tracking algorithm based on wavelet
moment (W-K-MSA) is proposed in this paper. In this algorithm, a Kalman filter algorithm, which is used to estimate the
motion parameters of targets, is improved based on wavelet moment features in the searching process. And searching
window is adaptively changed, as a result, searching scope is reduced greatly, and the processing velocity and veracity is
improved during model matching. The experimental results demonstrate that the proposed tracking algorithm is robust
and practical.
Smoke control is one of the important aspects in atrium fire. For an efficient smoke control strategy, it is very important to identify the smoke and fire source in a very short period of time. However, traditional methods such as point type detectors are not effective for smoke and fire detection in large space such as atrium. Therefore, video smoke and fire detection systems are proposed. For the development of the system, automatic extraction and tracking of flame are two important problems needed to be solved. Based on entropy theory, region growing and Otsu method, a new automatic integrated algorithm, which is used to track flame from video images, is proposed in this paper. It can successfully identify flames from different environment, different background and in different form. The experimental results show that this integrated algorithm has stronger robustness and wider adaptability. In addition, because of the low computational demand of this algorithm, it is also possible to be used as part of a robust, real-time smoke and fire detection system.
KEYWORDS: Volume rendering, 3D image processing, 3D scanning, Opacity, 3D modeling, Reconstruction algorithms, Image segmentation, Transparency, 3D image reconstruction, Binary data
Several problems related to the three-dimensional volume rendering from sector scanning images have been deeply studied in this paper. To counter the key problem that the points on the two adjacent images are not same interval, the recursive interpolation algorithm for the unequal interval, by which the data changes continuously and gradually, has been put forward in this paper. Therefore, the problem, how to make interpolation amid the unequal interval in the volume rendering from sector scanning images, has been well solved. Besides, the formation of outer curved layer volume is another key problem in the volume rendering from sector scanning images. The algorithm based on cubic parameter spline function for forming the outer curved layer volume has been proposed in this paper as well. Using this algorithm, the smooth outer surface can be obtained. On the basis of unparalleled two-dimensional section images, the three-dimension reconstruction from sector scanning has been firstly realized using volume rendering. And, several tested images of irregular model from sector scanning are presented. The tested results indicate that the algorithms presented in this paper are correct and practicable. The effect of reconstructed images is satisfactory.
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