The Experts below are selected from a list of 1980 Experts worldwide ranked by ideXlab platform

Yulen Huang - One of the best experts on this subject based on the ideXlab platform.

  • breast cancer diagnosis using self organizing map for sonography
    Ultrasound in Medicine and Biology, 2000
    Co-Authors: Darren Chen, Rueyfeng Chang, Yulen Huang
    Abstract:

    The purpose of this study was to evaluate the performance of neural network model self-organizing maps (SOM) in the classification of benign and malignant sonographic breast lesions. A total of 243 breast tumors (82 malignant and 161 benign) were retrospectively evaluated. When a sonogram was performed, the Analog Video Signal was captured to obtain a digitized sonographic image. The physician selected the region of interest in the sonography. An SOM model using 24 autocorrelation texture features classified the tumor as benign or malignant. In the experiment, cases were sampled with k-fold cross-validation (k = 10) to evaluate the performance using receiver operating characteristic (ROC) curves. The ROC area index for the proposed SOM system is 0.9357 ± 0.0152, the accuracy is 85.6%, the sensitivity is 97.6%, the specificity is 79.5%, the positive predictive value is 70.8%, and the negative predictive value is 98.5%. This computer-aided diagnosis system can provide a useful tool and its high negative predictive value could potentially help avert benign biopsies.

  • computer aided diagnosis applied to us of solid breast nodules by using neural networks
    Radiology, 1999
    Co-Authors: Darren Chen, Rueyfeng Chang, Yulen Huang
    Abstract:

    PURPOSE: To increase the capabilities of ultrasonographic (US) technology for the differential diagnosis of solid breast tumors by using a neural network. MATERIALS AND METHODS: One hundred forty US images of solid breast nodules were evaluated. When a sonogram was obtained, an Analog Video Signal from the VCR output of the scanner was transmitted to a notebook computer. A frame grabber connected to the printer port of the computer was then used to digitize the data. The suspicious tumor region on the digitized US image was manually selected. The texture information of the subimage was extracted, and a neural network classifier with autocorrelation features was used to classify the tumor as benign or malignant. In this experiment, 140 pathologically proved tumors (52 malignant and 88 benign tumors) were sampled with k-fold cross-validation (k = 10) to evaluate the performance with receiver operating characteristic curves. RESULTS: The accuracy of neural networks for classifying malignancies was 95.0% (133...

Darren Chen - One of the best experts on this subject based on the ideXlab platform.

  • breast cancer diagnosis using self organizing map for sonography
    Ultrasound in Medicine and Biology, 2000
    Co-Authors: Darren Chen, Rueyfeng Chang, Yulen Huang
    Abstract:

    The purpose of this study was to evaluate the performance of neural network model self-organizing maps (SOM) in the classification of benign and malignant sonographic breast lesions. A total of 243 breast tumors (82 malignant and 161 benign) were retrospectively evaluated. When a sonogram was performed, the Analog Video Signal was captured to obtain a digitized sonographic image. The physician selected the region of interest in the sonography. An SOM model using 24 autocorrelation texture features classified the tumor as benign or malignant. In the experiment, cases were sampled with k-fold cross-validation (k = 10) to evaluate the performance using receiver operating characteristic (ROC) curves. The ROC area index for the proposed SOM system is 0.9357 ± 0.0152, the accuracy is 85.6%, the sensitivity is 97.6%, the specificity is 79.5%, the positive predictive value is 70.8%, and the negative predictive value is 98.5%. This computer-aided diagnosis system can provide a useful tool and its high negative predictive value could potentially help avert benign biopsies.

  • computer aided diagnosis applied to us of solid breast nodules by using neural networks
    Radiology, 1999
    Co-Authors: Darren Chen, Rueyfeng Chang, Yulen Huang
    Abstract:

    PURPOSE: To increase the capabilities of ultrasonographic (US) technology for the differential diagnosis of solid breast tumors by using a neural network. MATERIALS AND METHODS: One hundred forty US images of solid breast nodules were evaluated. When a sonogram was obtained, an Analog Video Signal from the VCR output of the scanner was transmitted to a notebook computer. A frame grabber connected to the printer port of the computer was then used to digitize the data. The suspicious tumor region on the digitized US image was manually selected. The texture information of the subimage was extracted, and a neural network classifier with autocorrelation features was used to classify the tumor as benign or malignant. In this experiment, 140 pathologically proved tumors (52 malignant and 88 benign tumors) were sampled with k-fold cross-validation (k = 10) to evaluate the performance with receiver operating characteristic curves. RESULTS: The accuracy of neural networks for classifying malignancies was 95.0% (133...

Rueyfeng Chang - One of the best experts on this subject based on the ideXlab platform.

  • breast cancer diagnosis using self organizing map for sonography
    Ultrasound in Medicine and Biology, 2000
    Co-Authors: Darren Chen, Rueyfeng Chang, Yulen Huang
    Abstract:

    The purpose of this study was to evaluate the performance of neural network model self-organizing maps (SOM) in the classification of benign and malignant sonographic breast lesions. A total of 243 breast tumors (82 malignant and 161 benign) were retrospectively evaluated. When a sonogram was performed, the Analog Video Signal was captured to obtain a digitized sonographic image. The physician selected the region of interest in the sonography. An SOM model using 24 autocorrelation texture features classified the tumor as benign or malignant. In the experiment, cases were sampled with k-fold cross-validation (k = 10) to evaluate the performance using receiver operating characteristic (ROC) curves. The ROC area index for the proposed SOM system is 0.9357 ± 0.0152, the accuracy is 85.6%, the sensitivity is 97.6%, the specificity is 79.5%, the positive predictive value is 70.8%, and the negative predictive value is 98.5%. This computer-aided diagnosis system can provide a useful tool and its high negative predictive value could potentially help avert benign biopsies.

  • computer aided diagnosis applied to us of solid breast nodules by using neural networks
    Radiology, 1999
    Co-Authors: Darren Chen, Rueyfeng Chang, Yulen Huang
    Abstract:

    PURPOSE: To increase the capabilities of ultrasonographic (US) technology for the differential diagnosis of solid breast tumors by using a neural network. MATERIALS AND METHODS: One hundred forty US images of solid breast nodules were evaluated. When a sonogram was obtained, an Analog Video Signal from the VCR output of the scanner was transmitted to a notebook computer. A frame grabber connected to the printer port of the computer was then used to digitize the data. The suspicious tumor region on the digitized US image was manually selected. The texture information of the subimage was extracted, and a neural network classifier with autocorrelation features was used to classify the tumor as benign or malignant. In this experiment, 140 pathologically proved tumors (52 malignant and 88 benign tumors) were sampled with k-fold cross-validation (k = 10) to evaluate the performance with receiver operating characteristic curves. RESULTS: The accuracy of neural networks for classifying malignancies was 95.0% (133...

Ying Yiche - One of the best experts on this subject based on the ideXlab platform.

  • image processing system based on Analog Video Signal differential and projective distortion rectification
    Computer and Modernization, 2013
    Co-Authors: Ying Yiche
    Abstract:

    The Video camera is widely applied to embedded systems. Before further processed by the embedded systems,the ana- log Video Signal is usually digitized first. The large amount of data has made the difference of the processing capability much sig- nificant,and that limits the widespread use of vision sensor. An Analog Video Signal processing system with differential and image rectification is proposed,in which the differential between pixel in Analog Signal is used to detect the edge. Meanwhile,the pro- jective distortion is corrected according to lookup table. It is easier for further processing after the rectification of projective distor- tion. The solution improves the real time of Signal processing and reduces the dependence on processing capability.

David A. Miller - One of the best experts on this subject based on the ideXlab platform.

  • the aapm rsna physics tutorial for residents digital fluoroscopy
    Radiographics, 2001
    Co-Authors: Robert A. Pooley, J. Mark Mckinney, David A. Miller
    Abstract:

    A digital fluoroscopy system is most commonly configured as a conventional fluoroscopy system (tube, table, image intensifier, Video system) in which the Analog Video Signal is converted to and stored as digital data. Other methods of acquiring the digital data (eg, digital or charge-coupled device Video and flat-panel detectors) will become more prevalent in the future. Fundamental concepts related to digital imaging in general include binary numbers, pixels, and gray levels. Digital image data allow the convenient use of several image processing techniques including last image hold, gray-scale processing, temporal frame averaging, and edge enhancement. Real-time subtraction of digital fluoroscopic images after injection of contrast material has led to widespread use of digital subtraction angiography (DSA). Additional image processing techniques used with DSA include road mapping, image fade, mask pixel shift, frame summation, and vessel size measurement. Peripheral angiography performed with an automatic moving table allows imaging of the peripheral vasculature with a single contrast material injection.