Weili Shi

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.

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.

Develop and validate a finite element method model for deformation matching of laparoscopic gastrectomy navigation

Experimental surgical navigation systems have been reported being used in laparoscopic surgery, however, accurate registration in the surgical navigation is very challenging due to vessel deformation. We aim to build a deformable model based on the preoperative CT images to improve the matching accuracy by using the finite element method (FEM). Enhanced CT scans before and after the left gastric artery (LGA) pulled up were performed for FEM model and ground-truth generating in a pig experiment, respectively. An ANSYS software was used to simulate the FEM model of the vessel after pulled up according to the need for the laparoscopic gastrectomy. The central line (Line B) of the FEM model and central line (Line A) of the ground-truth were drawn and compared with each other. On the basis of material and parameters acquired from the animal experiment, we built a perigastric vessels FEM model of the patient with gastric cancer and evaluated its accuracy in surgical scene of laparoscopic gastrectomy. In animal experiment, the average distance between the two central lines is 6.467mm while the average distance between the closest points of them is 3.751mm. In surgical scene of laparoscopic gastrectomy, superimposing the FEM model onto the 2D laparoscopic image demonstrated a good coincidence. In this study, we built a deformable vessel model based on the preoperative CT images which may improve the matching accuracy and supply a referable way for further research of the deformation matching in the laparoscopic gastrectomy navigation.

An improved method for pancreas segmentation using SLIC and interactive region merging

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.

A Study of Multilevel Banded Graph Cuts for Three-Dimensional Colon Tissue Segmentation

Graph cuts is an image segmentation method by which the region and boundary information of objects can be revolved comprehensively. Because of the complex spatial characteristics of high-dimensional images, time complexity and segmentation accuracy of graph cuts methods for high-dimensional images need to be improved. This paper proposes a new three-dimensional multilevel banded graph cuts model to increase its accuracy and reduce its complexity. Firstly, three-dimensional image is viewed as a high-dimensional space to construct three-dimensional network graphs. A pyramid image sequence is created by Gaussian pyramid downsampling procedure. Then, a new energy function is built according to the spatial characteristics of the three-dimensional image, in which the adjacent points are expressed by using a 26-connected system. At last, the banded graph is constructed on a narrow band around the object/background. The graph cuts method is performed on the banded graph layer by layer to obtain the object region sequentially. In order to verify the proposed method, we have performed an experiment on a set of three-dimensional colon CT images, and compared the results with local region active contour and Chan–Vese model. The experimental results demonstrate that the proposed method can segment colon tissues from three-dimensional abdominal CT images accurately. The segmentation accuracy can be increased to 95.1% and the time complexity is reduced by about 30% of the other two methods.

An MR image segmentation algorithm based on bias field correction

Bias field which existed in MR image had an unfavorable influence on analyzing medical image, especially for medical image segmentation. To solve it, this paper proposed an approach which is based on energy minimization to estimate bias field where exists in the MR image, finish brain tissue segmentation, meanwhile. This method fully utilize the properties of factual information and bias information in the MR image, and by means of effective and stabilized matrix iterative calculation we can obtain optimal value of bias field and every tissues. Furthermore, this method also reflects excellent, robustness and accuracy.

An improved robust hand-eye calibration for endoscopy navigation 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.

A study on CT aorta segmentation using vessel enhancement diffusion filter and region growing

Medical imaging in clinical diagnoses and treatment has been increasingly important, and CT scan has been applied more widely. It is important to extract blood vessels structure for improving planning and navigating in interventional procedures. However, the complexity of vascular structures and the limitation of angiography equipment make vessel segmentation from CT images more challenging. A method for extracting abdominal aorta from CT images is proposed based on vessel enhancing diffusion filters and three-dimensional region growing algorithm. In order to certify the proposed method, a group of clinical CT image series is used and the results show that the proposed method is an effective way for improving the accuracy of abdominal aorta segmentation.