Zhengang Jiang

Automated nomenclature of upper abdominal arteries for displaying anatomical names on virtual laparoscopic images

This paper presents a method for automated nomenclature of abdominal arteries that are extracted from 3D CT images based on the combination optimization approach for the displaying anatomical names on virtual laparoscopic images. It is important to understand the blood vessel network of a patient. Our proposed method recognizes the anatomical names of each arterial branch extracted from contrasted 3D images based on geometric features. We employ a combination optimization approach for treating the variations of branching patterns and overlay recognized anatomical names on virtual laparoscopic views for assisting the recognition of patient anatomy for surgeons. Experimental results using 89 cases of 3D CT images showed that the nomenclature accuracy for uncorrected blood vessel tree and corrected blood vessel tree were about 84.2% and 88.8% in average respectively and demonstrated anatomical name overlay on virtual laparoscopic images.

Anatomical annotation on vascular structure in volume rendered images

The precise annotation of vascular structure is desired in computer-assisted systems to help surgeons identify each vessel branch. This paper proposes a method that annotates vessels on volume rendered images by rendering their names on them using a two-pass rendering process. In the first rendering pass, vessel surface models are generated using such properties as centerlines, radii, and running directions. Then the vessel names are drawn on the vessel surfaces. Finally, the vessel name images and the corresponding depth buffer are generated by a virtual camera at the viewpoint. In the second rendering pass, volume rendered images are generated by a ray casting volume rendering algorithm that considers the depth buffer generated in the first rendering pass. After the two-pass rendering is finished, an annotated image is generated by blending the volume rendered image with the surface rendered image. To confirm the effectiveness of our proposed method, we performed a computer-assisted system for the automated annotation of abdominal arteries. The experimental results show that vessel names can be drawn on the corresponding vessel surface in the volume rendered images at a computing cost that is nearly the same as that by volume rendering only. The proposed method has enormous potential to be adopted to annotate the vessels in the 3D medical images in clinical applications, such as image-guided surgery.

A study on automated anatomical labeling to arteries concerning with colon from 3D abdominal CT images

This paper presents an automated anatomical labeling method of arteries extracted from contrasted 3D CT images based on multi-class AdaBoost. In abdominal surgery, understanding of vasculature related to a target organ such as the colon is very important. Therefore, the anatomical structure of blood vessels needs to be understood by computers in a system supporting abdominal surgery. There are several researches on automated anatomical labeling, but there is no research on automated anatomical labeling to arteries concerning with the colon. The proposed method obtains a tree structure of arteries from the artery region and calculates features values of each branch. These feature values are thickness, curvature, direction, and running vectors of branch. Then, candidate arterial names are computed by classifiers that are trained to output artery names. Finally, a global optimization process is applied to the candidate arterial names to determine final names. Target arteries of this paper are nine lower abdominal arteries (AO, LCIA, RCIA, LEIA, REIA, SMA, IMA, LIIA, RIIA). We applied the proposed method to 14 cases of 3D abdominal contrasted CT images, and evaluated the results by leave-one-out scheme. The average precision and recall rates of the proposed method were 87.9% and 93.3%, respectively. The results of this method are applicable for anatomical name display of surgical simulation and computer aided surgery.

An improved method for compensating ultra-tiny electromagnetic tracker utilizing position and orientation information and its application to a flexible neuroendoscopic surgery navigation system

This paper presents an improved method for compensating ultra-tiny electromagnetic tracker (UEMT) outputs and its application to a flexible neuroendoscopic surgery navigation system. Recently, UEMT is widely used in a surgical navigation system using a flexible endoscope to obtain the position and the orientation of an endoscopic camera.However, due to the distortion of the electromagnetic field, the accuracy of such UEMT system becomes low. Several research groups have presented methods for compensating UEMT outputs that are deteriorated by ferromagnetic objects existing around the UEMT. These compensation methods firstly acquired positions and orientations (sample data) by sweeping a special tool (hybrid tool) having a UEMT and an optical tracker (OT) in free-hand. Then a polynomial compensating UEMT outputs is computed from both outputs. However, these methods have following problems: 1) Compensation function is obtained as a function of position, and orientation information is not used in compensation. 2) Although we need to slowly move the hybrid tool to obtain better compensation results, this leads increase of time. To overcome such problems, this paper presents a UEMT-output compensation function that is a function of not only position but also orientation. Also, a new sweeping method of the hybrid tool is proposed in order to reduce the sweeping time required for obtaining sample data. We evaluated the accuracy and feasibility of the proposed method by experiments in an OpenMR operating room. According to the result of experiments, the accuracy of the compensation method is improved about 20% than that of the previous method. We implemented the proposed method in a navigation system for flexible neuroendoscopic surgery and performed a phantom test and several clinical application tests. The result showed the proposed method is efficient for UEMT output compensation and improves accuracy of a flexible neuroendoscopic surgery system.

Key technologies and system development method of virtual endoscope

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.

Computer-aided diagnosis of mammographic masses using geometric verification-based image retrieval

Computer-Aided Diagnosis of masses in mammograms is an important indicator of breast cancer. The use of retrieval systems in breast examination is increasing gradually. In this respect, the method of exploiting the vocabulary tree framework and the inverted file in the mammographic masse retrieval have been proved high accuracy and excellent scalability. However it just considered the features in each image as a visual word and had ignored the spatial configurations of features. It greatly affect the retrieval performance. To overcome this drawback, we introduce the geometric verification method to retrieval in mammographic masses. First of all, we obtain corresponding match features based on the vocabulary tree framework and the inverted file. After that, we grasps the main point of local similarity characteristic of deformations in the local regions by constructing the circle regions of corresponding pairs. Meanwhile we segment the circle to express the geometric relationship of local matches in the area and generate the spatial encoding strictly. Finally we judge whether the matched features are correct or not, based on verifying the all spatial encoding are whether satisfied the geometric consistency. Experiments show the promising results of our approach.

A hand-eye calibration method for computer assisted endoscopy

Endoscopy has been more and more widely used in clinical application. The surgical navigation system is an important way to improve the safety of endoscopy. To get the position relationship between camera and tracking marker is a critical work for improving the precision of the navigation system. This problem can be solved by the hand-eye calibration approach using dual quaternion. However, because of the output error of tracking system and the limited motion of the endoscope, this algorithm becomes unstable and the system accuracy is low. Therefore, this paper proposes a method to avoid these problems by advancing the selection rule of sample motions. The experimental results show that the stability and the accuracy of algorithm have been improved by selecting sample motion data automatically.

A rapid method for compensating registration error between tracker and endoscope in flexible neuroendoscopic surgery navigation system

This paper proposes a rapid method for compensating registration error between the tracker and the endoscope in a flexible neuroendoscopic surgery navigation system, as well as evaluates the accuracy of the proposed method. Recently, flexible neuroendoscopic surgery navigation systems have been developed utilizing an electromagnetic tracker (EMT). In such systems, an electromagnetic tracker sensor is fixed at the tip of a flexible endoscope to get the position and the orientation of the endoscope camera by using the relationship between the camera and the sensor. Usually, the relationship is estimated by a registration method using a calibration chart. Then, virtual images corresponding to real endoscopic views are generated by using the position and orientation of the camera. However, in the clinical application, the sensor has to be re-fixed before or during the surgery due to its disinfection or breakage. Although the sensor can be re-fixed at the same position as the registered position, it is difficult to ensure the roll of sensor in the same because the senor is a cylinder. Furthermore, the sensor can also be rotated by the operation of tools during surgery. As a result, the virtual images will be rotated and become greatly different from the real endoscopic views. In this case, the relationship between camera and sensor has to be re-estimated by a registration method or manually, which makes the operation of endoscope complicated and nonfeasible. In order to overcome this problem, we proposed a rapid method for compensating the rotational error between real and virtual cameras using the epipolar geometry. In this study, various experiments of the method are performed in order to evaluate and to improve its accuracy. Experimental results suggested estimation accuracy can be improved by reducing the relative error of EMT outputs, and it is necessary to ensure the quality of images which are used in the estimation.

An Improved Weighted ELM with Hierarchical Feature Representation for Imbalanced Biomedical Datasets

In medical intelligent diagnosis, most of the real-world datasets have the class-imbalance problem and some strong correlation features. In this paper, a novel classification model with hierarchical feature representation is proposed to tackle small and imbalanced biomedicine datasets. The main idea of the proposed method is to integrate extreme learning machine-autoencoder (ELM-AE) into the weighted ELM (W-ELM) model. ELM-AE with norm optimization is utilized to extract more effective information from raw data, thereby forming a hierarchical and compact feature representation. Afterwards, random projections of learned feature results view as inputs of the W-ELM. An adaptive weighting scheme is designed to reduce the misclassified rate of the minority class by assigning a larger weight to minority samples. The classification performance of the proposed method is evaluated on two biomedical datasets from the UCI repository. The experimental results show that the proposed method cannot only effectively solve the class-imbalanced problem with small biomedical datasets, but also obtain a higher and more stable performance than other state-of-the-art classification methods.

Application of the CLAHE Algorithm Based on Optimized Bilinear Interpolation in Near Infrared Vein Image Enhancement

In1 general, there are some features in the near-infrared superficial vein images such as high noise and low contrast. The edges of veins in the image are blurred. And the vascular lines are not obvious. It has high algorithm complexity and low processing efficiency in the CLAHE (Contrast Limited Adaptive Histogram Equalization) based on bilinear interpolation. In order to solve these problems, this paper proposed an CLAHE algorithm based on optimized binliner interpolation, which adds the parameter T to the interpolation function. T speeds up the interpolation speed so that the entire algorithm runs faster. Experiments show that the running speed of the algorithm is better than that of the CLAHE algorithm based on bilinear interpolation. Simultaneously, the enhancement effect of the algorithm on the image is the same as that of the CLAHE algorithm based on bilinear interpolation.