Biomedical subjects

Dar-Ren Chen

Publications and source records attributed to Dar-Ren Chen.

25 records · Page 2Linked to original sources

PubMed2002

Use of the bootstrap technique with small training sets for computer-aided diagnosis in breast ultrasound.

The purpose of this study was to test the efficacy of using small training sets in computer-aided diagnostic systems (CAD) and to increase the capabilities of ultrasound (US) technology in the differential diagnosis of solid breast tumors. A total of 263 sonographic images of solid breast nodules, including 129 malignancies and 134 benign nodules, were evaluated by using a bootstrap technique with 10 original training samples. Texture parameters of a region-of-interest (ROI) were resampled with a bootstrap technique and a decision-tree model was used to classify the tumor as benign or malignant. The accuracy was 87.07% (229 of 263 tumors), the sensitivity was 95.35% (123 of 129), the specificity was 79.10% (106 of 134), the positive predictive value was 81.46% (123 of 151), and the negative predictive value was 94.64% (106 of 112). This analysis method provides a second opinion for physicians with high accuracy. The new method shows a potential to be useful in future application of CAD, especially when a large database cannot be obtained for training or a newly developed ultrasonic system has smaller sets of samples.

Adolescent↗

PubMed2002

Retrieval technique for the diagnosis of solid breast tumors on sonogram.

We evaluated a series of pathologically proven breast tumors using an image-retrieval technique for classifying benign and malignant lesions. A total of 263 breast tumors (129 malignant and 134 benign) were retrospectively evaluated. The physician located regions-of-interest (ROI) of ultrasonic images and texture parameters (contrast, covariance and dissimilarity) were used in the process of the content-based image-retrieval technique. The accuracy of using the retrieval technique for classifying malignancies was 92.55% (236 of 255), the sensitivity was 94.44% (119 of 126), the specificity was 90.70% (117 of 129), the positive predictive value was 90.84% (119 of 131), and negative predictive value was 94.35% (117 of 124) for the proposed computer-aided diagnostic system. This computer-aided diagnosis system can provide a useful tool and its high negative predictive value could potentially help avert benign biopsies. It is unnecessary to perform any training procedures. This computer-aided diagnosis system can provide a second opinion for a sonographic interpreter; the main advantage in this proposed system is that we do not need any training. Historical cases can be directly added into the database and training of the diagnosis system again is not needed. With the growth of the database, more and more information can be collected and used as reference cases while performing diagnoses. This increases the flexibility of our diagnostic system.

Adolescent↗

PubMed2002

Diagnosis of breast tumors with sonographic texture analysis using wavelet transform and neural networks.

To increase the ability of ultrasonographic technology for the differential diagnosis of solid breast tumors, we describe a novel computer-aided diagnosis (CADx) system using neural networks for classification of breast tumors. Tumor regions and surrounding tissues are segmented from the physician-located region-of-interest (ROI) images by applying our proposed segmentation algorithm. Cooperating with the segmentation algorithm, three feasible features, including variance contrast, autocorrelation contrast and distribution distortion of wavelet coefficients, were extracted from the ROI images for further classification. A multilayered perceptron (MLP) neural network trained using error back-propagation algorithm with momentum was then used for the differential diagnosis of breast tumors on sonograms. In the experiment, 242 cases (including benign breast tumors from 161 patients and carcinomas from 82 patients) were sampled with k-fold cross-validation (k = 10) to evaluate the performance. The receiver operating characteristic (ROC) area index for the proposed CADx system is 0.9396 +/- 0.0183, the sensitivity is 98.77%, the specificity is 81.37%, the positive predictive value is 72.73% and the negative predictive value is 99.24%. Experimental results showed that our diagnosis model performed very well for breast tumor diagnosis.

Breast Diseases↗

PubMed2002

Computer-aided diagnosis of breast tumors with different US systems.

RATIONALE AND OBJECTIVES: The authors performed this study to determine whether a computer-aided diagnostic (CAD) system was suitable from one ultrasound (US) unit to another after parameters were adjusted by using intelligent selection algorithms. MATERIALS AND METHODS: The authors used texture analysis and data mining with a decision tree model to classify breast tumors with different US systems. The databases of training cases from one unit and testing cases from another were collected from different countries. Regions of interest on US scans and co-variance texture parameters were used in the diagnosis system. Proposed adjustment schemes for different US systems were used to transform the information needed for a differential diagnosis. RESULTS: Comparison of the diagnostic system with and without adjustment, respectively, yielded the following results: accuracy, 89.9% and 82.2%; sensitivity, 94.6% and 92.2%; specificity, 85.4% and 72.3%; positive predictive value, 86.5% and 76.8%; and negative predictive value, 94.1% and 90.4%. The improvement in accuracy, specificity, and positive predictive value was statistically significant. Diagnostic performance was improved after the adjustment. CONCLUSION: After parameters were adjusted by using intelligent selection algorithms, the performance of the proposed CAD system was better both with the same and with different systems. Different resolutions, different setting conditions, and different scanner ages are no longer obstacles to the application of such a CAD system.

Algorithms↗

PubMed2002

Detecting breast cancer in mammographically dense breasts: comparing technetium-99m tetrofosmin mammoscintigraphy and ultrasonography.

The aim of our study was to compare the usefulness of technetium-99m tetrofosmin (Tc-TF) mammoscintigraphy and ultrasonography for detecting breast cancer in mammographically dense breasts. This study included 32 female Taiwanese patients with indeterminate mammographic probability of malignancy due to mammographically dense breasts. Both Tc-TF mammoscintigraphy and ultrasonography were performed on each patient. Then, all of the 32 breast masses underwent biopsies or operations to obtain final pathological diagnoses. Based on the final pathological diagnoses, 24 women had breast cancer and eight had benign breast tumors among the 32 patients. Twenty cancers and one benign tumor had positive Tc-TF mammoscintigraphic findings. The sensitivity, specificity, and accuracy were 83, 88, and 84%, respectively. Twenty-two cancers and five benign tumors had positive ultrasonographic findings. The sensitivity, specificity, and accuracy were 92, 38, and 78%, respectively. To detect breast cancer in patients with nondiagnostic mammograms because of mammographically dense breasts, ultrasonography with its higher sensitivity is more suitable for screening breast masses. However, due to its higher specificity, Tc-TF mammoscintigraphy is useful to confirm the ultrasonographic findings.

Adenocarcinoma↗

PubMedDate not supplied

Support vector machines in sonography: application to decision making in the diagnosis of breast cancer.

We evaluated a series of pathologically proven breast tumors using the support vector machine (SVM) in the differential diagnosis of solid breast tumors. This study evaluated two ultrasonic image databases, i.e., DB1 and DB2. The DB1 contained 140 ultrasonic images of solid breast nodules (52 malignant and 88 benign). The DB2 contained 250 ultrasonic images of solid breast nodules (35 malignant and 215 benign). The physician-located regions of interest (ROI) of sonography and textual features were utilized to classify breast tumors. An SVM classifier using interpixel textual features classified the tumor as benign or malignant. The receiver operating characteristic (ROC) area index for the proposed system on the DB1 and the DB2 are 0.9695+/-0.0150 and 0.9552+/-0.0161, respectively. The proposed system differentiates solid breast nodules with a relatively high accuracy and helps inexperienced operators avoid misdiagnosis. The main advantage in the proposed system is that the training procedure of SVM was very fast and stable. The training and diagnosis procedure of the proposed system is almost 700 times faster than that of multilayer perception neural networks (MLPs). With the growth of the database, new ultrasonic images can be collected and used as reference cases while performing diagnoses. This study reduces the training and diagnosis time dramatically.

Breast Neoplasms↗

PubMedDate not supplied

Classification of breast ultrasound images using fractal feature.

Fractal analyses have been applied successfully for the image compression, texture analysis, and texture image segmentation. The fractal dimension could be used to quantify the texture information. In this study, the differences of gray value of neighboring pixels are used to estimate the fractal dimension of an ultrasound image of breast lesion by using the fractal Brownian motion. Furthermore, a computer-aided diagnosis (CAD) system based on the fractal analysis is proposed to classify the breast lesions into two classes: benign and malignant. To improve the classification performances, the ultrasound images are preprocessed by using morphology operations and histogram equalization. Finally, the k-means classification method is used to classify benign tumors from malignant ones. The US breast image databases include only histologically confirmed cases: 110 malignant and 140 benign tumors, which were recorded. All the digital images were obtained prior to biopsy using by an ATL HDI 3000 system. The receiver operator characteristic (ROC) area index AZ is 0.9218, which represents the diagnostic performance.

Adolescent↗