Biomedical subjects

Wen-Jie Wu

Publications and source records attributed to Wen-Jie Wu.

7 recordsLinked to original sources

PubMed2005

Automatic ultrasound segmentation and morphology based diagnosis of solid breast tumors.

Ultrasound (US) is a useful diagnostic tool to distinguish benign from malignant masses of the breast. It is a very convenient and safe diagnostic method. However, there is a considerable overlap benignancy and malignancy in ultrasonic images and interpretation is subjective. A high performance breast tumors computer-aided diagnosis (CAD) system can provide an accurate and reliable diagnostic second opinion for physicians to distinguish benign breast lesions from malignant ones. The potential of sonographic texture analysis to improve breast tumor classifications has been demonstrated. However, the texture analysis is system-dependent. The disadvantages of these systems which use texture analysis to classify tumors are they usually perform well only in one specific ultrasound system. While Morphological based US diagnosis of breast tumor will take the advantage of nearly independent to either the setting of US system and different US machines. In this study, the tumors are segmented using the newly developed level set method at first and then six morphologic features are used to distinguish the benign and malignant cases. The support vector machine (SVM) is used to classify the tumors. There are 210 ultrasonic images of pathologically proven benign breast tumors from 120 patients and carcinomas from 90 patients in the ultrasonic image database. The database contains only one image from each patient. The ultrasonic images are captured at the largest diameter of the tumor. The images are collected consecutively from August 1, 1999 to May 31, 2000; the patients' ages ranged from 18 to 64 years. Sonography is performed using an ATL HDI 3000 system with a L10-5 small part transducer. In the experiment, the accuracy of SVM with shape information for classifying malignancies is 90.95% (191/210), the sensitivity is 88.89% (80/90), the specificity is 92.5% (111/120), the positive predictive value is 89.89% (80/89), and the negative predictive value is 91.74% (111/121).

Adolescent↗

PubMed2003

Improvement in breast tumor discrimination by support vector machines and speckle-emphasis texture analysis.

Recent statistics show that breast cancer is a major cause of death among women in developed countries. Hence, finding an accurate and effective diagnostic method is very important. In this paper, we propose a high precision computer-aided diagnosis (CAD) system for sonography. We utilize a support vector machine (SVM) to classify breast tumors according to their texture information surrounding speckle pixels. We test our system with 250 pathologically-proven breast tumors including 140 benign and 110 malignant ones. Also we compare the diagnostic performances of three texture features, i.e., speckle-emphasis texture feature, nonspeckle-emphasis texture feature and conventional all pixels texture feature, applied to breast sonography using SVM. In our experiment, the accuracy of SVM with speckle information for classifying malignancies is 93.2% (233/250), the sensitivity is 95.45% (105/110), the specificity is 91.43% (128/140), the positive predictive value is 89.74% (105/117) and the negative predictive value is 96.24% (128/133). Based on the experimental results, speckle phenomenon is a useful tool to be used in computer-aided diagnosis; its performance is better than those of the other two features. Speckle phenomenon, which is considered as noise in sonography, can intrude into judgments of a physician using naked eyes but it is another story for application in a computer-aided diagnosis algorithm.

Algorithms↗

PubMed2003

3-D US frame positioning using speckle decorrelation and image registration.

In this paper, a new positioning system is proposed for the 3-D ultrasound (US). This system combines the image registration technique and speckle decorrelation algorithm to accurately position sequential ultrasonic images without any additional positioning hardware. The speckle decorrelation algorithm estimates the relative distance of two neighboring frames and the image registration technique gets the range of the whole 3-D ultrasonic data set and makes slight modification on each frame's position. The image registration technique is based on the reference image, which is perpendicular to the 3-D ultrasonic data set. This reference image intersects each frame of the 3-D ultrasonic data set in a line. For each frame, the intersectional line is first found and then the location in the reference image can be used to estimate the position of this frame. This system uses the data set of consecutive 2-D freehand-scanned US B-mode images to construct the 3-D US volume data, and it can be integrated into the 3-D US volume rendering system.

Algorithms↗

PubMed2003

3-D breast ultrasound segmentation using active contour model.

In this study, we made use of the discrete active contour model to overcome the natural properties of ultrasound (US) images, speckle, noise and tissue-related textures, to segment the breast tumors precisely. Determination of the real tumor boundary with the snake-deformation process requires an initial contour estimate. However, the manual way to sketch an initial contour is very time-consuming. Thus, we propose an automatic initial contour-finding method that not only maintains the tumor shape, but also is close to the tumor boundary and inside the tumor. During the deformation process, to prevent the snake trapping into the false position caused by tissue-related texture or speckle, we added the edge information as an image feature to define the external force. In addition, because the 3-D volume of a tumor is essentially constructed by a sequence of 2-D images, our method for finding boundaries of a tumor can be extended to 3-D cases. By precisely counting the volume of the 3-D images, we can get the volume of tumor. Finally, we will show that the proposed techniques have rather good performance and lead to a satisfactory result in comparison with the estimated volume and physician's estimate.

Breast Neoplasms↗

PubMed2003

Support vector machines for diagnosis of breast tumors on US images.

RATIONALE AND OBJECTIVES: Breast cancer has become the leading cause of cancer deaths among women in developed countries. To decrease the related mortality, disease must be treated as early as possible, but it is hard to detect and diagnose tumors at an early stage. A well-designed computer-aided diagnostic system can help physicians avoid misdiagnosis and avoid unnecessary biopsy without missing cancers. In this study, the authors tested one such system to determine its effectiveness. MATERIALS AND METHODS: Many computer-aided diagnostic systems for ultrasonography are based on the neural network model and classify breast tumors according to texture features. The authors tested a refinement of this model, an advanced support vector machine (SVM), in 250 cases of pathologically proved breast tumors (140 benign and 110 malignant), and compared its performance with that of a multilayer propagation neural network. RESULTS: The accuracy of the SVM for classifying malignancies was 85.6% (214 of 250); the sensitivity, 95.45% (105 of 110); the specificity, 77.86% (109 of 140); the positive predictive value, 77.21% (105 of 136); and the negative predictive value, 95.61% (109 of 114). CONCLUSION: The SVM proved helpful in the imaging diagnosis of breast cancer. The classification ability of the SVM is nearly equal to that of the neural network model, and the SVM has a much shorter training time (1 vs 189 seconds). Given the increasing size and complexity of data sets, the SVM is therefore preferable for computer-aided diagnosis.

Breast Neoplasms↗

PubMed2003

3-D snake for US in margin evaluation for malignant breast tumor excision using Mammotome.

The goal of this paper is to use the three-dimensional (3-D) snake technique in 3-D ultrasound to obtain the tumor contour for the pre- and the post-operative malignant breast excision by the vacuum assisted biopsy instrument Mammotome. This technique of assessing the margin of two can help the physician to evaluate the effect of the surgery. By using the anisotropic diffusion filter, the noise and speckles can be reduced. Then the stick detection is adopted for enhancing the edge. Finally, the gradient vector flow (GVF) snake is used to obtain the tumor contour. These techniques are extended to the 3-D techniques to increase the accuracy and robust of segmentation results. We hope that this study can help physicians to improve the minimal invasive operation for a breast tumor.

Anatomy, Cross-Sectional↗

PubMed2002

[Sal I, Nru I and Mse I restriction fragment length polymorphisms of factor IX gene in Chinese Han people].

The purpose of this study is to investigate the Sal I, Nru I and Mse I restriction fragment length polymorphisms (RFLPs) of factor IX gene in Chinese Han people. The frequencies of FIX-192 and FIX-793 for A and G, and FIX-698 for T and C were analyzed by polymerase chain reaction (PCR) in unrelated normal Chinese Han people. A sample of 214, 210 and 206 unrelated X chromosomes were analyzed for FIX-192 and FIX-793 and FIX-698, respectively. The results showed that the frequencies for FIX-192 were 0.878 for A and 0.122 for G, with a heterozygosity rate of 0.213, and the frequencies for FIX-793 were 0.552 for A and 0.448 for G, with a heterozygosity rate of 0.494, the frequencies for FIX-698 were 0.311 for T and 0.689 for C, with a heterozygosity rate of 0.429. It was concluded that the SalIand NruI and MseI RFLPs of FIX gene may be useful markers for carrier detection and prenatal diagnosis in Chinese families with hemophilia B patients.

China↗