Lumbar vertebral fracture seriously endangers the health of people, which has a higher mortality. Due to the tiny difference among various fracture features in CT images, multiple vertebral fractures classification has a great challenge for computer-aided diagnosis system. To solve this problem, this paper proposes a multiclass PSVM ensemble method with multi-feature selection to recognize lumbar vertebral fractures from spine CT images. In the proposed method, firstly, the active contour model is utilized to segment lumbar vertebral bodies. It is helpful for the subsequent feature extraction. Secondly, different image features are extracted, including 3 geometric shape features, 3 texture features, and 5 height ratios. The importance of these features is analyzed and ranked by using infinite feature selection method, thus selecting different feature subsets. Finally, three multiclass probability SVMs with binary tree structure are trained on three datasets. The weighted voting strategy is used for the final decision fusion. To validate the effectiveness of the proposed method, probability SVM, K-nearest neighbor, and decision tree as base classifiers are compared with or without feature selection. Experimental results on 25 spine CT volumes demonstrate that the advantage of the proposed method compared to other classifiers, both in terms of the classification accuracy and Cohen’s kappa coefficient.
Spine curvature disorders have been found relevant as the nervous system diseases and may produce serious disturbances of the whole body. The ability to automatically segment and locate the spinal vertebrae is, therefore, an important task for modern studies of the spinal curvature disorders detection. In this work, we devise a modern, simple and automated human spinal vertebrae segmentation and localization method using transfer learning, that works on CT and MRI acquisitions. We exploit pre-trained models to spinal vertebrae segmentation and localization problem. We first explore and evaluate different medical imaging architectures and choose the deep dilated convolutions as the initialization for our spinal vertebrae segmentation and localization task. Then we conduct the pre-trained model from spinal cord gray matter dataset to our spinal vertebrae segmentation task with supervised fine-tuning. The vertebral centroid coordinate can be computed from the segmented result, and the centroid localization error is used as the feedback for fine-tuning. We evaluate our method against traditional method on medical image segmentation and localization task and report the comparison of evaluation metrics. We show the qualitative and quantitative evaluation on spine CT images which are from spine CT volumes on the publicity platform SpineWeb. The evaluation results show that our approach was able to capture many properties of the spinal vertebrae, and provided good segmentation and localization performance. From our research we show that the deep dilated convolutions pre-trained on MRI spinal cord gray matter images can be transfer to process CT spinal vertebrae images.
Considering the weak edges in pancreas segmentation, this paper proposes a new solution which integrates more features of CT images by combining SLIC superpixels and interactive region merging. In the proposed method, Mahalanobis distance is first utilized in SLIC method to generate better superpixel images. By extracting five texture features and one gray feature, the similarity measure between two superpixels becomes more reliable in interactive region merging. Furthermore, object edge blocks are accurately addressed by re-segmentation merging process. Applying the proposed method to four cases of abdominal CT images, we segment pancreatic tissues to verify the feasibility and effectiveness. The experimental results show that the proposed method can make segmentation accuracy increase to 92% on average. This study will boost the application process of pancreas segmentation for computer-aided diagnosis system.
Endoscopy is widely used in clinical application, and surgical navigation system is an extremely important way to enhance the safety of endoscopy. The key to improve the accuracy of the navigation system is to solve the positional relationship between camera and tracking marker precisely. The problem can be solved by the hand-eye calibration method based on dual quaternions. However, because of the tracking error and the limited motion of the endoscope, the sample motions may contain some incomplete motion samples. Those motions will cause the algorithm unstable and inaccurate. An advanced selection rule for sample motions is proposed in this paper to improve the stability and accuracy of the methods based on dual quaternion. By setting the motion filter to filter out the incomplete motion samples, finally, high precision and robust result is achieved. The experimental results show that the accuracy and stability of camera registration have been effectively improved by selecting sample motion data automatically.
The precise annotation of vessel is desired in computer-assisted systems to help surgeons identify each vessel branch. A method has been reported that annotates vessels on volume rendered images by rendering their names on them using a two-pass rendering process. In the reported method, however, cylinder surface models of the vessels should be generated for writing vessels names. In fact, vessels are not actual cylinders, so the surfaces of the vessels cannot be simulated by such models accurately. This paper presents a model-free method for annotating vessels on volume rendered images by rendering their names on them using the two-pass rendering process: surface rendering and volume rendering. In the surface rendering process, docking points of vessel names are estimated by using such properties as centerlines, running directions, and vessel regions which are obtained in preprocess. Then the vessel names are pasted on the vessel surfaces at the docking points. In the volume rendering process, volume image is rendered using a fast volume rendering algorithm with depth buffer of image rendered in the surface rendering process. Finally, those rendered images are blended into an image as a result. In order to confirm the proposed method, a visualizing system for the automated annotation of abdominal arteries is performed. The experimental results show that vessel names can be drawn on the corresponding vessel in the volume rendered images correctly. The proposed method has enormous potential to be adopted to annotate other organs which cannot be modeled using regular geometrical surface.
Virtual Endoscope is a method to emulate cavity checking visually combined row volume data obtained from CT and
MR with three-dimensional image technologies in virtue of navigation, flythrough and pseudo-color technologies. The
application on virtual endoscope is developed in recent several years, and the software realization needs the support of multiple complicated algorithms, including internal surface reconstruction, center path automatic extraction, lens setting-up, multi-case processing, collision detection, and corresponding algorithm computation and realization, which cause the application software development for Virtual Endoscope is rather complex and difficult. It puts forward volume rendering for rapid three-dimensional reconstruction, introduces highly active path planning algorithm of three-dimensional space path algorithm, improved path smooth algorithm and lens entry point auto-detecting algorithm, illustrates three-dimensional scene establishment by VTK development toolkit, and discusses the key technologies in virtual endoscope realization in this paper, based on which the virtual endoscope system has graceful performance.
Proc. SPIE. 6625, International Symposium on Photoelectronic Detection and Imaging 2007: Related Technologies and Applications
KEYWORDS: Image processing algorithms and systems, 3D image reconstruction, Detection and tracking algorithms, Image segmentation, Image processing, Medical image reconstruction, Medical imaging, Volume rendering, Reconstruction algorithms, 3D image processing
Three-dimensional image reconstruction by volume rendering has two problems: time-consuming and low precision. During the diagnosis procedure, some detailed organ tissue is the interest to doctors, so the reconstructed two-dimensional images are pre-processed before three-dimensional reconstruction including disturbance removing and
precise segmentation, to obtain Region-Of-Interest (ROI) based on which three-dimensional reconstruction carries through, that can decrease the complexity of time and space. By this, Live Wire segmentation algorithm model for medical image is improved to gain exact edge coordinate for the image segmentation with interior details by improved filling algorithm. Segmented images with object details only are regarded as input to realize volume rendering by ray
casting tracking algorithm. Because the needless organs have been filtered, the disturbance on interested objects for doctors is reduced. Meanwhile, generally speaking, these needed organs left are less proportion in images. So it reduces data amount of volume rendering, and improves the speed of three-dimensional reconstruction.
Theoretical study and simulation research on atmospheric effect in airplane-ground laser communication are developed in this paper, which establishes refraction, attenuation and turbulence models of laser atmospheric transmission, uses communication speed and error rate as objective function to do digital simulation research for the whole communication process based on theoretical model of laser communication system. In simulation, various picture tools are used for observing the changing of laser energy with external environment effects, such as atmospheric effect, free-space loss, background radiation; as well as observing laser beam position and energy distribution when laser arrives at the satellite receiver. Moreover, local simulation model is established for analyzing thoroughly effects of various external factors to the laser communication performance.