Biomedical subjects

Chii-Jen Chen

Publications and source records attributed to Chii-Jen Chen.

4 recordsLinked to original sources

PubMed2006

2-D ultrasound strain images for breast cancer diagnosis using nonrigid subregion registration.

Tissue elasticity of a lesion is a useful criterion for the diagnosis of breast ultrasound (US). Elastograms are created by comparing ultrasonic radio-frequency waveforms before and after a light-tissue compression. In this study, we evaluate the accuracy of continuous US strain image in the classification of benign from malignant breast tumors. A series of B-mode US images is applied and each case involves 60 continuous images obtained by using the steady artificial pressure of the US probe. In general, after compression by the US probe, a soft benign tumor will become flatter than a stiffened malignant tumor. We proposed a computer-aided diagnostic (CAD) system by utilizing the nonrigid image registration modality on the analysis of tumor deformation. Furthermore, we used some image preprocessing methods, which included the level set segmentation, to improve the performance. One-hundred pathology-proven cases, including 60 benign breast tumors and 40 malignant tumors, were used in the experiments to test the classification accuracy of the proposed method. Four characteristic values--normalized slope of metric value (NSM), normalized area difference (NAD), normalized standard deviation (NSD) and normalized center translation (NCT)--were computed for all cases. By using the support vector machine, the accuracy, sensitivity, specificity and positive and negative predictive values of the classification of continuous US strain images were satisfactory. The A(z) value of the support vector machine based on the four characteristic values used for the classification of solid breast tumors was 0.9358.

Adult↗

PubMed2005

Solid breast masses: classification with computer-aided analysis of continuous US images obtained with probe compression.

PURPOSE: To prospectively evaluate the accuracy of continuous ultrasonographic (US) images obtained during probe compression and computer-aided analysis for classification of biopsy-proved (reference standard) benign and malignant breast tumors. MATERIALS AND METHODS: This study was approved by the local ethics committee, and informed consent was obtained from all included patients. Serial US images of 100 solid breast masses (60 benign and 40 malignant tumors) were obtained with US probe compression in 86 patients (mean age, 45 years; range, 20-67 years). After segmentation of tumor contours with the level-set method, three features of strain on tissue from probe compression--contour difference, shift distance, area difference--and one feature of shape--solidity-were computed. A maximum margin classifier was used to classify the tumors by using these four features. The Student t test and receiver operating characteristic curve analysis were used for statistical analysis. RESULTS: The mean values of contour difference, shift distance, area difference, and solidity were 3.52% +/- 2.12 (standard deviation), 2.62 +/- 1.31, 1.08% +/- 0.85, and 1.70 +/- 1.85 in malignant tumors and 9.72% +/- 4.54, 5.04 +/- 2.79, 3.17% +/- 2.86, and 0.53 +/- 0.63 in benign tumors, respectively. Differences with P < .001 were statistically significant for all four features. Area under the receiver operating characteristic curve (A(Z)) values for contour difference, shift distance, area difference, and solidity were 0.88, 0.85, 0.86, and 0.79, respectively. The A(Z) value of three features of strain was significantly higher than that of the feature of shape (P < .01). The accuracy, sensitivity, specificity, and positive and negative predictive values of US classifications that were based on values for these four features were 87.0% (87 of 100), 85% (34 of 40), 88% (53 of 60), 83% (34 of 41), and 90% (53 of 59), respectively, with an A(Z) value of 0.91. CONCLUSION: Continuous US images obtained with probe compression and computer-aided analysis can aid in classification of benign and malignant breast tumors.

Adult↗

PubMed2004

Three-dimensional ultrasound in margin evaluation for breast tumor excision using Mammotome.

Sonographic evidence of tumor removal by Mammotome excision does not confirm histological clearance. The operator finds it hard to determine if a malignant tumor has been fully removed, leaving a safe margin in the direction of each border; that is, the spatial orientation during tumor retrieval is not well-established by naked eye under sonographic guidance. We propose a computational imaging process to extract reasonable tumor contour in pre- and postoperative data sets for sonographic guidance so that Mammotome excision can help the operator to evaluate the surgical outcome. There were five tumors in the study, including three benign and two malignant. The lesion of interest was delineated after 2-D examination was completed, then it was analyzed with 3-D breast ultrasound (US). To give a reference point for correlations between pre- and postoperative images, we used a marker tape pasted on the skin within the transducer scanning area and then the preoperative 3-D US images were obtained. Subsequently, 2-D breast US was applied during Mammotome operation. After the Mammotome procedures were finished, the postoperative 3-D US images were obtained; thus, we gained two different data sets of 3-D US images that were used for later analysis for evaluating the extension of postoperative margin status. From the results, the safe margin was not satisfactory in all directions, because the minimum differences measured by the proposed algorithm were not large enough in all five cases, and this was proved from two malignant mastectomy specimens. The experimental results representing this inadequate Mammotome excision can be visualized through the computer aid. The comparison of tumor contour and excision margin may possibly be used for small malignant tumors in the future to improve the breast-conserving surgery.

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↗