The Experts below are selected from a list of 1341 Experts worldwide ranked by ideXlab platform

Jin Young Kwak - One of the best experts on this subject based on the ideXlab platform.

  • Application of Texture Analysis in the Differential Diagnosis of Benign and Malignant Thyroid Nodules: Comparison With Gray-Scale Ultrasound and Elastography
    American Journal of Roentgenology, 2015
    Co-Authors: Soo-yeon Kim, Eun-kyung Kim, Hee Jung Moon, Jung Hyun Yoon, Jin Young Kwak
    Abstract:

    OBJECTIVE. The purposes of this study were to investigate the optimal subset for texture analysis by use of a histogram and Cooccurrence Matrix in the differential diagnosis of benign and malignant thyroid nodules and to compare the results with those of gray-scale ultrasound and elastography. MATERIALS AND METHODS. From a retrospective search of an institutional database between June and November 2009, 633 solid nodules 5 mm or larger from 613 patients who underwent gray-scale ultrasound and elastography and subsequent ultrasound-guided fine-needle aspiration were included in this study. Each nodule was categorized as probably benign or suspicious of being malignant according to findings at gray-scale ultrasound and elastography. Histogram parameters (mean, SD, skewness, kurtosis, and entropy) and Cooccurrence Matrix parameters (contrast, correlation, uniformity, homogeneity, and entropy) were extracted from gray-scale ultrasound and elastographic images. The diagnostic performances of gray-scale ultraso...

  • Application of Texture Analysis in the Differential Diagnosis of Benign and Malignant Thyroid Nodules: Comparison With Gray-Scale Ultrasound and Elastography.
    AJR. American journal of roentgenology, 2015
    Co-Authors: Soo-yeon Kim, Eun-kyung Kim, Hee Jung Moon, Jung Hyun Yoon, Jin Young Kwak
    Abstract:

    The purposes of this study were to investigate the optimal subset for texture analysis by use of a histogram and Cooccurrence Matrix in the differential diagnosis of benign and malignant thyroid nodules and to compare the results with those of gray-scale ultrasound and elastography. From a retrospective search of an institutional database between June and November 2009, 633 solid nodules 5 mm or larger from 613 patients who underwent gray-scale ultrasound and elastography and subsequent ultrasound-guided fine-needle aspiration were included in this study. Each nodule was categorized as probably benign or suspicious of being malignant according to findings at gray-scale ultrasound and elastography. Histogram parameters (mean, SD, skewness, kurtosis, and entropy) and Cooccurrence Matrix parameters (contrast, correlation, uniformity, homogeneity, and entropy) were extracted from gray-scale ultrasound and elastographic images. The diagnostic performances of gray-scale ultrasound, elastography, and texture analysis for differentiating thyroid nodules were evaluated. Gray-scale ultrasound had the best diagnostic performance with an ROC AUC (Az) of 0.809 among all parameters. Elastography had significantly poorer performance (Az = 0.646) than gray-scale ultrasound (p < 0.001). Mean extracted from gray-scale ultrasound had the highest Az (0.675) among all histogram and Cooccurrence Matrix parameters extracted from gray-scale ultrasound and elastographic images. However, mean and the combination of mean and gray-scale ultrasound had poorer performance than gray-scale ultrasound alone. Using texture analysis does not improve diagnostic performance in the evaluation of thyroid cancers.

Soo-yeon Kim - One of the best experts on this subject based on the ideXlab platform.

  • Application of Texture Analysis in the Differential Diagnosis of Benign and Malignant Thyroid Nodules: Comparison With Gray-Scale Ultrasound and Elastography
    American Journal of Roentgenology, 2015
    Co-Authors: Soo-yeon Kim, Eun-kyung Kim, Hee Jung Moon, Jung Hyun Yoon, Jin Young Kwak
    Abstract:

    OBJECTIVE. The purposes of this study were to investigate the optimal subset for texture analysis by use of a histogram and Cooccurrence Matrix in the differential diagnosis of benign and malignant thyroid nodules and to compare the results with those of gray-scale ultrasound and elastography. MATERIALS AND METHODS. From a retrospective search of an institutional database between June and November 2009, 633 solid nodules 5 mm or larger from 613 patients who underwent gray-scale ultrasound and elastography and subsequent ultrasound-guided fine-needle aspiration were included in this study. Each nodule was categorized as probably benign or suspicious of being malignant according to findings at gray-scale ultrasound and elastography. Histogram parameters (mean, SD, skewness, kurtosis, and entropy) and Cooccurrence Matrix parameters (contrast, correlation, uniformity, homogeneity, and entropy) were extracted from gray-scale ultrasound and elastographic images. The diagnostic performances of gray-scale ultraso...

  • Application of Texture Analysis in the Differential Diagnosis of Benign and Malignant Thyroid Nodules: Comparison With Gray-Scale Ultrasound and Elastography.
    AJR. American journal of roentgenology, 2015
    Co-Authors: Soo-yeon Kim, Eun-kyung Kim, Hee Jung Moon, Jung Hyun Yoon, Jin Young Kwak
    Abstract:

    The purposes of this study were to investigate the optimal subset for texture analysis by use of a histogram and Cooccurrence Matrix in the differential diagnosis of benign and malignant thyroid nodules and to compare the results with those of gray-scale ultrasound and elastography. From a retrospective search of an institutional database between June and November 2009, 633 solid nodules 5 mm or larger from 613 patients who underwent gray-scale ultrasound and elastography and subsequent ultrasound-guided fine-needle aspiration were included in this study. Each nodule was categorized as probably benign or suspicious of being malignant according to findings at gray-scale ultrasound and elastography. Histogram parameters (mean, SD, skewness, kurtosis, and entropy) and Cooccurrence Matrix parameters (contrast, correlation, uniformity, homogeneity, and entropy) were extracted from gray-scale ultrasound and elastographic images. The diagnostic performances of gray-scale ultrasound, elastography, and texture analysis for differentiating thyroid nodules were evaluated. Gray-scale ultrasound had the best diagnostic performance with an ROC AUC (Az) of 0.809 among all parameters. Elastography had significantly poorer performance (Az = 0.646) than gray-scale ultrasound (p < 0.001). Mean extracted from gray-scale ultrasound had the highest Az (0.675) among all histogram and Cooccurrence Matrix parameters extracted from gray-scale ultrasound and elastographic images. However, mean and the combination of mean and gray-scale ultrasound had poorer performance than gray-scale ultrasound alone. Using texture analysis does not improve diagnostic performance in the evaluation of thyroid cancers.

Selin Aviyente - One of the best experts on this subject based on the ideXlab platform.

  • image denoising based on the wavelet Cooccurrence Matrix
    International Conference on Acoustics Speech and Signal Processing, 2005
    Co-Authors: Zeyong Shan, Selin Aviyente
    Abstract:

    Image denoising is a well-known problem in signal processing. Wavelet decomposition based approaches have been applied successfully to the image denoising problem. The majority of wavelet thresholding methods do not take the spatial correlation between wavelet coefficients into account. A new image denoising approach is presented; it incorporates the intra-scale dependencies between the wavelet coefficients into the thresholding algorithm. The Cooccurrence Matrix of the wavelet coefficients and their neighbors is constructed to represent the spatial dependencies. An information-theoretic criterion, the 2D joint entropy of the wavelet Cooccurrence Matrix, is used as the cost function to determine the optimal threshold. Experimental results indicate that the proposed approach yields significant improvement over universal thresholding, both in visual quality and mean square error.

  • ICASSP (2) - Image denoising based on the wavelet Cooccurrence Matrix
    Proceedings. (ICASSP '05). IEEE International Conference on Acoustics Speech and Signal Processing 2005., 1
    Co-Authors: Zeyong Shan, Selin Aviyente
    Abstract:

    Image denoising is a well-known problem in signal processing. Wavelet decomposition based approaches have been applied successfully to the image denoising problem. The majority of wavelet thresholding methods do not take the spatial correlation between wavelet coefficients into account. A new image denoising approach is presented; it incorporates the intra-scale dependencies between the wavelet coefficients into the thresholding algorithm. The Cooccurrence Matrix of the wavelet coefficients and their neighbors is constructed to represent the spatial dependencies. An information-theoretic criterion, the 2D joint entropy of the wavelet Cooccurrence Matrix, is used as the cost function to determine the optimal threshold. Experimental results indicate that the proposed approach yields significant improvement over universal thresholding, both in visual quality and mean square error.

Zhu Hongqing - One of the best experts on this subject based on the ideXlab platform.

  • segmentation of blood vessels in retinal images using 2d entropies of gray level gradient Cooccurrence Matrix
    International Conference on Acoustics Speech and Signal Processing, 2004
    Co-Authors: Zhu Hongqing
    Abstract:

    A novel automated method for the segmentation of blood vessels in retinal images based upon enhancement and maximum entropy thresholding is proposed. Blood vessels usually have poor local contrast. Before thresholding fundus images, several matched filters are employed to enhance the contrast of blood vessels. The matched-filter-response (MFR) image is processed by a thresholding scheme in order to extract blood vessels from the background. Then, the proposed thresholding approach evaluates two-dimensional entropies based on the gray level-gradient Cooccurrence Matrix. The 2D threshold vector that maximizes the edge class entropies is selected. This thresholding method utilizes the information of gray level and gradient in the MFR image. It is found that the proposed algorithm works well in normal or abnormal retinal images.

  • ICASSP (3) - Segmentation of blood vessels in retinal images using 2D entropies of gray level-gradient Cooccurrence Matrix
    2004 IEEE International Conference on Acoustics Speech and Signal Processing, 1
    Co-Authors: Zhu Hongqing
    Abstract:

    A novel automated method for the segmentation of blood vessels in retinal images based upon enhancement and maximum entropy thresholding is proposed. Blood vessels usually have poor local contrast. Before thresholding fundus images, several matched filters are employed to enhance the contrast of blood vessels. The matched-filter-response (MFR) image is processed by a thresholding scheme in order to extract blood vessels from the background. Then, the proposed thresholding approach evaluates two-dimensional entropies based on the gray level-gradient Cooccurrence Matrix. The 2D threshold vector that maximizes the edge class entropies is selected. This thresholding method utilizes the information of gray level and gradient in the MFR image. It is found that the proposed algorithm works well in normal or abnormal retinal images.

Jung Hyun Yoon - One of the best experts on this subject based on the ideXlab platform.

  • Application of Texture Analysis in the Differential Diagnosis of Benign and Malignant Thyroid Nodules: Comparison With Gray-Scale Ultrasound and Elastography
    American Journal of Roentgenology, 2015
    Co-Authors: Soo-yeon Kim, Eun-kyung Kim, Hee Jung Moon, Jung Hyun Yoon, Jin Young Kwak
    Abstract:

    OBJECTIVE. The purposes of this study were to investigate the optimal subset for texture analysis by use of a histogram and Cooccurrence Matrix in the differential diagnosis of benign and malignant thyroid nodules and to compare the results with those of gray-scale ultrasound and elastography. MATERIALS AND METHODS. From a retrospective search of an institutional database between June and November 2009, 633 solid nodules 5 mm or larger from 613 patients who underwent gray-scale ultrasound and elastography and subsequent ultrasound-guided fine-needle aspiration were included in this study. Each nodule was categorized as probably benign or suspicious of being malignant according to findings at gray-scale ultrasound and elastography. Histogram parameters (mean, SD, skewness, kurtosis, and entropy) and Cooccurrence Matrix parameters (contrast, correlation, uniformity, homogeneity, and entropy) were extracted from gray-scale ultrasound and elastographic images. The diagnostic performances of gray-scale ultraso...

  • Application of Texture Analysis in the Differential Diagnosis of Benign and Malignant Thyroid Nodules: Comparison With Gray-Scale Ultrasound and Elastography.
    AJR. American journal of roentgenology, 2015
    Co-Authors: Soo-yeon Kim, Eun-kyung Kim, Hee Jung Moon, Jung Hyun Yoon, Jin Young Kwak
    Abstract:

    The purposes of this study were to investigate the optimal subset for texture analysis by use of a histogram and Cooccurrence Matrix in the differential diagnosis of benign and malignant thyroid nodules and to compare the results with those of gray-scale ultrasound and elastography. From a retrospective search of an institutional database between June and November 2009, 633 solid nodules 5 mm or larger from 613 patients who underwent gray-scale ultrasound and elastography and subsequent ultrasound-guided fine-needle aspiration were included in this study. Each nodule was categorized as probably benign or suspicious of being malignant according to findings at gray-scale ultrasound and elastography. Histogram parameters (mean, SD, skewness, kurtosis, and entropy) and Cooccurrence Matrix parameters (contrast, correlation, uniformity, homogeneity, and entropy) were extracted from gray-scale ultrasound and elastographic images. The diagnostic performances of gray-scale ultrasound, elastography, and texture analysis for differentiating thyroid nodules were evaluated. Gray-scale ultrasound had the best diagnostic performance with an ROC AUC (Az) of 0.809 among all parameters. Elastography had significantly poorer performance (Az = 0.646) than gray-scale ultrasound (p < 0.001). Mean extracted from gray-scale ultrasound had the highest Az (0.675) among all histogram and Cooccurrence Matrix parameters extracted from gray-scale ultrasound and elastographic images. However, mean and the combination of mean and gray-scale ultrasound had poorer performance than gray-scale ultrasound alone. Using texture analysis does not improve diagnostic performance in the evaluation of thyroid cancers.