The Experts below are selected from a list of 16248 Experts worldwide ranked by ideXlab platform
Rueyfeng Chang - One of the best experts on this subject based on the ideXlab platform.
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Tumor Detection in Automated Breast Ultrasound Using 3-D CNN and Prioritized Candidate Aggregation
IEEE Transactions on Medical Imaging, 2019Co-Authors: Tsung-chen Chiang, Chiunsheng Huang, Rong Tai Chen, Yao-sian Huang, Rueyfeng ChangAbstract:Automated whole Breast Ultrasound (ABUS) has been widely used as a screening modality for examination of Breast abnormalities. Reviewing hundreds of slices produced by ABUS, however, is time consuming. Therefore, in this paper, a fast and effective computer-aided detection system based on 3-D convolutional neural networks (CNNs) and prioritized candidate aggregation is proposed to accelerate this reviewing. First, an efficient sliding window method is used to extract volumes of interest (VOIs). Then, each VOI is estimated the tumor probability with a 3-D CNN, and VOIs with higher estimated probability are selected as tumor candidates. Since the candidates may overlap each other, a novel scheme is designed to aggregate the overlapped candidates. During the aggregation, candidates are prioritized based on estimated tumor probability to alleviate over-aggregation issue. The relationship between the sizes of VOI and target tumor is optimally exploited to effectively perform each stage of our detection algorithm. On evaluation with a test set of 171 tumors, our method achieved sensitivities of 95% (162/171), 90% (154/171), 85% (145/171), and 80% (137/171) with 14.03, 6.92, 4.91, and 3.62 false positives per patient (with six passes), respectively. In summary, our method is more general and much faster than preliminary works and demonstrates promising results.
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multi dimensional tumor detection in automated whole Breast Ultrasound using topographic watershed
IEEE Transactions on Medical Imaging, 2014Co-Authors: Rong Tai Chen, Chiunsheng Huang, Yeunchung Chang, Yawen Yang, Ming Jen Hung, Rueyfeng ChangAbstract:Automated whole Breast Ultrasound (ABUS) is becoming a popular screening modality for whole Breast examination. Compared to conventional handheld Ultrasound, ABUS achieves operator-independent and is feasible for mass screening. However, reviewing hundreds of slices in an ABUS image volume is time-consuming. A computer-aided detection (CADe) system based on watershed transform was proposed in this study to accelerate the reviewing. The watershed transform was applied to gather similar tissues around local minima to be homogeneous regions. The likelihoods of being tumors of the regions were estimated using the quantitative morphology, intensity, and texture features in the 2-D/3-D false positive reduction (FPR). The collected database comprised 68 benign and 65 malignant tumors. As a result, the proposed system achieved sensitivities of 100% (133/133), 90% (121/133), and 80% (107/133) with FPs/pass of 9.44, 5.42, and 3.33, respectively. The figure of merit of the combination of three feature sets is 0.46 which is significantly better than that of other feature sets ( [Formula: see text]). In summary, the proposed CADe system based on the multi-dimensional FPR using the integrated feature set is promising in detecting tumors in ABUS images.
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computer aided tumor detection based on multi scale blob detection algorithm in automated Breast Ultrasound images
IEEE Transactions on Medical Imaging, 2013Co-Authors: Woo Kyung Moon, Yi Wei Shen, Chiunsheng Huang, Min Sun Bae, Jeonhor Chen, Rueyfeng ChangAbstract:Automated whole Breast Ultrasound (ABUS) is an emerging screening tool for detecting Breast abnormalities. In this study, a computer-aided detection (CADe) system based on multi-scale blob detection was developed for analyzing ABUS images. The performance of the proposed CADe system was tested using a database composed of 136 Breast lesions (58 benign lesions and 78 malignant lesions) and 37 normal cases. After speckle noise reduction, Hessian analysis with multi-scale blob detection was applied for the detection of tumors. This method detected every tumor, but some nontumors were also detected. The tumor likelihoods for the remaining candidates were estimated using a logistic regression model based on blobness, internal echo, and morphology features. The tumor candidates with tumor likelihoods higher than a specific threshold (0.4) were considered tumors. By using the combination of blobness, internal echo, and morphology features with 10-fold cross-validation, the proposed CAD system showed sensitivities of 100%, 90%, and 70% with false positives per pass of 17.4, 8.8, and 2.7, respectively. Our results suggest that CADe systems based on multi-scale blob detection can be used to detect Breast tumors in ABUS images.
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computer aided diagnosis for the classification of Breast masses in automated whole Breast Ultrasound images
Ultrasound in Medicine and Biology, 2011Co-Authors: Woo Kyung Moon, Yi Wei Shen, Chiunsheng Huang, Li Ren Chiang, Rueyfeng ChangAbstract:Abstract New automated whole Breast Ultrasound (ABUS) machines have recently been developed and the Ultrasound (US) volume dataset of the whole Breast can be acquired in a standard manner. The purpose of this study was to develop a novel computer-aided diagnosis system for classification of Breast masses in ABUS images. One hundred forty-seven cases (76 benign and 71 malignant Breast masses) were obtained by a commercially available ABUS system. Because the distance of neighboring slices in ABUS images is fixed and small, these continuous slices were used for reconstruction as three-dimensional (3-D) US images. The 3-D tumor contour was segmented using the level-set segmentation method. Then, the 3-D features, including the texture, shape and ellipsoid fitting were extracted based on the segmented 3-D tumor contour to classify benign and malignant tumors based on the logistic regression model. The Student’s t test, Mann-Whitney U test and receiver operating characteristic (ROC) curve analysis were used for statistical analysis. From the Az values of ROC curves, the shape features (0.9138) are better than the texture features (0.8603) and the ellipsoid fitting features (0.8496) for classification. The difference was significant between shape and ellipsoid fitting features ( p = 0.0382). However, combination of ellipsoid fitting features and shape features can achieve a best performance with accuracy of 85.0% (125/147), sensitivity of 84.5% (60/71), specificity of 85.5% (65/76) and the area under the ROC curve Az of 0.9466. The results showed that ABUS images could be used for computer-aided feature extraction and classification of Breast tumors. (E-mail: rfchang@csie.ntu.edu.tw )
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rapid image stitching and computer aided detection for multipass automated Breast Ultrasound
Medical Physics, 2010Co-Authors: Rueyfeng Chang, Chiunsheng Huang, Yihong Chou, Kuangche Changchien, Etsuo Takada, Jeonhor ChenAbstract:Purpose: Breast Ultrasound(US) is recently becoming more and more popular for detecting Breast lesions. However, screening results in hundreds of USimages for each subject. This magnitude of images can lead to fatigue in radiologist, causing failure in the detection of lesions of a subtle nature. In this study, an image stitching technique is proposed for combining multipass images of the whole Breast into a series of full-view images, and a fully automatic screening system that works off these images is also presented. Methods: Using the registration technique based on the simple sum of absolute block-mean difference (SBMD) measure, three-pass images were merged into full-view USimages. An automatic screening system was then developed for detecting tumors from these full-view images. The preprocessing step was used to reduce the tumor detection time of the system and to improve image quality. The gray-level slicing method was then used to divide images into numerous regions. Finally, seven computerized features—darkness, uniformity, width-height ratio, area size, nonpersistence, coronal area size, and region continuity—were defined and used to determine whether or not each region was a part of a tumor. Results: In the experiment, there was a total of 25 experimental cases with 26 lesions, and each case was composed of 252 images (three passes, 84 images/pass). The processing time of the proposed stitching procedure for each case was within 30 s with a Pentium IV 2.0 processor, and the detection sensitivity of the proposed CADsystem was 92.3% with 1.76 false positives per case. Conclusions: The proposed automatic screening system can be applied to the whole Breast images stitched together via SBMD-based registration in order to detect tumors.
Maryellen L Giger - One of the best experts on this subject based on the ideXlab platform.
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mo de 207b 06 computer aided diagnosis of Breast Ultrasound images using transfer learning from deep convolutional neural networks
Medical Physics, 2016Co-Authors: Benjamin Q Huynh, Karen Drukker, Maryellen L GigerAbstract:Purpose: To assess the performance of using transferred features from pre-trained deep convolutional networks (CNNs) in the task of classifying cancer in Breast Ultrasound images, and to compare this method of transfer learning with previous methods involving human-designed features. Methods: A Breast Ultrasound dataset consisting of 1125 cases and 2393 regions of interest (ROIs) was used. Each ROI was labeled as cystic, benign, or malignant. Features were extracted from each ROI using pre-trained CNNs and used to train support vector machine (SVM) classifiers in the tasks of distinguishing non-malignant (benign+cystic) vs malignant lesions and benign vs malignant lesions. For a baseline comparison, classifiers were also trained on prior analytically-extracted tumor features. Five-fold cross-validation (by case) was conducted with the area under the receiver operating characteristic curve (AUC) as the performance metric. Results: Classifiers trained on CNN-extracted features were comparable to classifiers trained on human-designed features. In the non-malignant vs malignant task, both the SVM trained on CNN-extracted features and the SVM trained on human-designed features obtained an AUC of 0.90. In the task of determining benign vs malignant, the SVM trained on CNN-extracted features obtained an AUC of 0.88, compared to the AUC of 0.85 obtained by the SVM trained on human-designed features. Conclusion: We obtained strong results using transfer learning to characterize Ultrasound Breast cancer images. This method allows us to directly classify a small dataset of lesions in a computationally inexpensive fashion without any manual input. Modern deep learning methods in computer vision are contingent on large datasets and vast computational resources, which are often inaccessible for clinical applications. Consequently, we believe transfer learning methods will be important for computer-aided diagnosis schemes in order to utilize advancements in deep learning and computer vision without the associated costs. This work was partially funded by NIH grant U01 CA195564 and the University of Chicago Metcalf program. M.L.G. is a stockholder in R2/Hologic, co-founder and equity holder in Quantitative Insights, and receives royalties from Hologic, GE Medical Systems, MEDIAN Technologies, Riverain Medical, Mitsubishi, and Toshiba. K.D. received royalties from Hologic.
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automated Breast Ultrasound in Breast cancer screening of women with dense Breasts reader study of mammography negative and mammography positive cancers
American Journal of Roentgenology, 2016Co-Authors: Maryellen L Giger, Karen Drukker, Alexandra Edwards, Rachel F Brem, Yulei Jiang, Marc Inciardi, John Papaioannou, Jeremy Bancroft BrownAbstract:OBJECTIVE. The objective of our study was to assess and compare, in a reader study, radiologists' performance in the detection of Breast cancer using full-field digital mammography (FFDM) alone and using FFDM with 3D automated Breast Ultrasound (ABUS). MATERIALS AND METHODS. In this multireader, multicase, sequential-design reader study, 17 Mammography Quality Standards Act–qualified radiologists interpreted a cancer-enriched set of FFDM and ABUS examinations. All imaging studies were of asymptomatic women with BI-RADS C or D Breast density. Readers first interpreted FFDM alone and subsequently interpreted FFDM combined with ABUS. The analysis included 185 cases: 133 noncancers and 52 biopsy-proven cancers. Of the 52 cancer cases, the screening FFDM images were interpreted as showing BI-RADS 1 or 2 findings in 31 cases and BI-RADS 0 findings in 21 cases. For the cases interpreted as BI-RADS 0, a forced BI-RADS score was also given. Reader performance was compared in terms of AUC under the ROC curve, sensi...
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segmentation of Breast masses on dedicated Breast computed tomography and three dimensional Breast Ultrasound images
Journal of medical imaging, 2014Co-Authors: Karen Drukker, Maryellen L Giger, Hsienchi Kuo, Ingrid Reiser, John M Boone, Karen K Lindfors, Kai Yang, Alexandra Edwards, Charlene A SennettAbstract:We present and evaluate a method for the three-dimensional (3-D) segmentation of Breast masses on dedicated Breast computed tomography (bCT) and automated 3-D Breast Ultrasound images. The segmentation method, refined from our previous segmentation method for masses on contrast-enhanced bCT, includes two steps: (1) initial contour estimation and (2) active contour-based segmentation to further evolve and refine the initial contour by adding a local energy term to the level-set equation. Segmentation performance was assessed in terms of Dice coefficients (DICE) for 129 lesions on noncontrast bCT, 38 lesions on contrast-enhanced bCT, and 98 lesions on 3-D Breast Ultrasound (US) images. For bCT, DICE values of 0.82 and 0.80 were obtained on contrast-enhanced and noncontrast images, respectively. The improvement in segmentation performance with respect to that of our previous method was statistically significant (p ¼ 0.002). Moreover, segmentation appeared robust with respect to the presence of glandular tissue. For 3-D Breast US, the DICE value was 0.71. Hence, our method obtained promising results for both 3-D imaging modalities, laying a solid foundation for further quantitative image analysis and potential future expansion to other 3-D imaging modalities.©2014Society
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computerized analysis of shadowing on Breast Ultrasound for improved lesion detection
Medical Physics, 2003Co-Authors: Karen Drukker, Maryellen L Giger, Ellen B MendelsonAbstract:Sonography is being considered for the screening of women at high risk for Breast cancer. We are developing computerized detection methods to aid in the localization of lesions on Breast Ultrasound images. The detection scheme presented here is based on the analysis of posterior acoustic shadowing, since posterior acoustic shadowing is observed for many malignant lesions. The method uses a nonlinear filtering technique based on the skewness of the gray level distribution within a kernel of image data. The database used in this study included 400 Breast Ultrasound cases (757 images) consisting of complicated cysts, solid benign lesions, and malignant lesions. At a false-positive rate of 0.25 false positives per image, a detection sensitivity of 80% by case (66% by image) was achieved for malignant lesions. The performance for the overall database (at 0.25 false positives per image) was less at 42% sensitivity by case (30% by image) due to the more limited presence of posterior acoustic shadowing for benign solid lesions and the presence of posterior acoustic enhancement for cysts. Our computerized method for the detection of lesion shadows alerts radiologists to lesions that exhibit posterior acoustic shadowing. While this is not a characterization method, its performance is best for lesions that exhibit posterior acoustic shadowing such as malignant and, to a lesser extent, benign solid lesions. This method, in combination with other computerized sonographic detection methods, may ultimately help facilitate the use of Ultrasound for Breast cancer screening.
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computerized lesion detection on Breast Ultrasound
Medical Physics, 2002Co-Authors: Karen Drukker, Maryellen L Giger, Karla Horsch, Matthew A Kupinski, Carl J Vyborny, Ellen B MendelsonAbstract:We investigated the use of a radial gradient index (RGI) filtering technique to automatically detect lesions on Breast Ultrasound. After initial RGI filtering, a sensitivity of 87% at 0.76 false-positive detections per image was obtained on a database of 400 patients (757 images). Next, lesion candidates were segmented from the background by maximizing an average radial gradient (ARD) index for regions grown from the detected points. At an overlap of 0.4 with a radiologist lesion outline, 75% of the lesions were correctly detected. Subsequently, round robin analysis was used to assess the quality of the classification of lesion candidates into actual lesions and false-positives by a Bayesian neural network. The round robin analysis yielded an Az value of 0.84, and an overall performance by case of 94% sensitivity at 0.48 false-positives per image. Use of computerized analysis of Breast sonograms may ultimately facilitate the use of sonography in Breast cancer screening programs.
Karen Drukker - One of the best experts on this subject based on the ideXlab platform.
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mo de 207b 06 computer aided diagnosis of Breast Ultrasound images using transfer learning from deep convolutional neural networks
Medical Physics, 2016Co-Authors: Benjamin Q Huynh, Karen Drukker, Maryellen L GigerAbstract:Purpose: To assess the performance of using transferred features from pre-trained deep convolutional networks (CNNs) in the task of classifying cancer in Breast Ultrasound images, and to compare this method of transfer learning with previous methods involving human-designed features. Methods: A Breast Ultrasound dataset consisting of 1125 cases and 2393 regions of interest (ROIs) was used. Each ROI was labeled as cystic, benign, or malignant. Features were extracted from each ROI using pre-trained CNNs and used to train support vector machine (SVM) classifiers in the tasks of distinguishing non-malignant (benign+cystic) vs malignant lesions and benign vs malignant lesions. For a baseline comparison, classifiers were also trained on prior analytically-extracted tumor features. Five-fold cross-validation (by case) was conducted with the area under the receiver operating characteristic curve (AUC) as the performance metric. Results: Classifiers trained on CNN-extracted features were comparable to classifiers trained on human-designed features. In the non-malignant vs malignant task, both the SVM trained on CNN-extracted features and the SVM trained on human-designed features obtained an AUC of 0.90. In the task of determining benign vs malignant, the SVM trained on CNN-extracted features obtained an AUC of 0.88, compared to the AUC of 0.85 obtained by the SVM trained on human-designed features. Conclusion: We obtained strong results using transfer learning to characterize Ultrasound Breast cancer images. This method allows us to directly classify a small dataset of lesions in a computationally inexpensive fashion without any manual input. Modern deep learning methods in computer vision are contingent on large datasets and vast computational resources, which are often inaccessible for clinical applications. Consequently, we believe transfer learning methods will be important for computer-aided diagnosis schemes in order to utilize advancements in deep learning and computer vision without the associated costs. This work was partially funded by NIH grant U01 CA195564 and the University of Chicago Metcalf program. M.L.G. is a stockholder in R2/Hologic, co-founder and equity holder in Quantitative Insights, and receives royalties from Hologic, GE Medical Systems, MEDIAN Technologies, Riverain Medical, Mitsubishi, and Toshiba. K.D. received royalties from Hologic.
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automated Breast Ultrasound in Breast cancer screening of women with dense Breasts reader study of mammography negative and mammography positive cancers
American Journal of Roentgenology, 2016Co-Authors: Maryellen L Giger, Karen Drukker, Alexandra Edwards, Rachel F Brem, Yulei Jiang, Marc Inciardi, John Papaioannou, Jeremy Bancroft BrownAbstract:OBJECTIVE. The objective of our study was to assess and compare, in a reader study, radiologists' performance in the detection of Breast cancer using full-field digital mammography (FFDM) alone and using FFDM with 3D automated Breast Ultrasound (ABUS). MATERIALS AND METHODS. In this multireader, multicase, sequential-design reader study, 17 Mammography Quality Standards Act–qualified radiologists interpreted a cancer-enriched set of FFDM and ABUS examinations. All imaging studies were of asymptomatic women with BI-RADS C or D Breast density. Readers first interpreted FFDM alone and subsequently interpreted FFDM combined with ABUS. The analysis included 185 cases: 133 noncancers and 52 biopsy-proven cancers. Of the 52 cancer cases, the screening FFDM images were interpreted as showing BI-RADS 1 or 2 findings in 31 cases and BI-RADS 0 findings in 21 cases. For the cases interpreted as BI-RADS 0, a forced BI-RADS score was also given. Reader performance was compared in terms of AUC under the ROC curve, sensi...
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segmentation of Breast masses on dedicated Breast computed tomography and three dimensional Breast Ultrasound images
Journal of medical imaging, 2014Co-Authors: Karen Drukker, Maryellen L Giger, Hsienchi Kuo, Ingrid Reiser, John M Boone, Karen K Lindfors, Kai Yang, Alexandra Edwards, Charlene A SennettAbstract:We present and evaluate a method for the three-dimensional (3-D) segmentation of Breast masses on dedicated Breast computed tomography (bCT) and automated 3-D Breast Ultrasound images. The segmentation method, refined from our previous segmentation method for masses on contrast-enhanced bCT, includes two steps: (1) initial contour estimation and (2) active contour-based segmentation to further evolve and refine the initial contour by adding a local energy term to the level-set equation. Segmentation performance was assessed in terms of Dice coefficients (DICE) for 129 lesions on noncontrast bCT, 38 lesions on contrast-enhanced bCT, and 98 lesions on 3-D Breast Ultrasound (US) images. For bCT, DICE values of 0.82 and 0.80 were obtained on contrast-enhanced and noncontrast images, respectively. The improvement in segmentation performance with respect to that of our previous method was statistically significant (p ¼ 0.002). Moreover, segmentation appeared robust with respect to the presence of glandular tissue. For 3-D Breast US, the DICE value was 0.71. Hence, our method obtained promising results for both 3-D imaging modalities, laying a solid foundation for further quantitative image analysis and potential future expansion to other 3-D imaging modalities.©2014Society
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computerized analysis of shadowing on Breast Ultrasound for improved lesion detection
Medical Physics, 2003Co-Authors: Karen Drukker, Maryellen L Giger, Ellen B MendelsonAbstract:Sonography is being considered for the screening of women at high risk for Breast cancer. We are developing computerized detection methods to aid in the localization of lesions on Breast Ultrasound images. The detection scheme presented here is based on the analysis of posterior acoustic shadowing, since posterior acoustic shadowing is observed for many malignant lesions. The method uses a nonlinear filtering technique based on the skewness of the gray level distribution within a kernel of image data. The database used in this study included 400 Breast Ultrasound cases (757 images) consisting of complicated cysts, solid benign lesions, and malignant lesions. At a false-positive rate of 0.25 false positives per image, a detection sensitivity of 80% by case (66% by image) was achieved for malignant lesions. The performance for the overall database (at 0.25 false positives per image) was less at 42% sensitivity by case (30% by image) due to the more limited presence of posterior acoustic shadowing for benign solid lesions and the presence of posterior acoustic enhancement for cysts. Our computerized method for the detection of lesion shadows alerts radiologists to lesions that exhibit posterior acoustic shadowing. While this is not a characterization method, its performance is best for lesions that exhibit posterior acoustic shadowing such as malignant and, to a lesser extent, benign solid lesions. This method, in combination with other computerized sonographic detection methods, may ultimately help facilitate the use of Ultrasound for Breast cancer screening.
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computerized lesion detection on Breast Ultrasound
Medical Physics, 2002Co-Authors: Karen Drukker, Maryellen L Giger, Karla Horsch, Matthew A Kupinski, Carl J Vyborny, Ellen B MendelsonAbstract:We investigated the use of a radial gradient index (RGI) filtering technique to automatically detect lesions on Breast Ultrasound. After initial RGI filtering, a sensitivity of 87% at 0.76 false-positive detections per image was obtained on a database of 400 patients (757 images). Next, lesion candidates were segmented from the background by maximizing an average radial gradient (ARD) index for regions grown from the detected points. At an overlap of 0.4 with a radiologist lesion outline, 75% of the lesions were correctly detected. Subsequently, round robin analysis was used to assess the quality of the classification of lesion candidates into actual lesions and false-positives by a Bayesian neural network. The round robin analysis yielded an Az value of 0.84, and an overall performance by case of 94% sensitivity at 0.48 false-positives per image. Use of computerized analysis of Breast sonograms may ultimately facilitate the use of sonography in Breast cancer screening programs.
Chiunsheng Huang - One of the best experts on this subject based on the ideXlab platform.
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Tumor Detection in Automated Breast Ultrasound Using 3-D CNN and Prioritized Candidate Aggregation
IEEE Transactions on Medical Imaging, 2019Co-Authors: Tsung-chen Chiang, Chiunsheng Huang, Rong Tai Chen, Yao-sian Huang, Rueyfeng ChangAbstract:Automated whole Breast Ultrasound (ABUS) has been widely used as a screening modality for examination of Breast abnormalities. Reviewing hundreds of slices produced by ABUS, however, is time consuming. Therefore, in this paper, a fast and effective computer-aided detection system based on 3-D convolutional neural networks (CNNs) and prioritized candidate aggregation is proposed to accelerate this reviewing. First, an efficient sliding window method is used to extract volumes of interest (VOIs). Then, each VOI is estimated the tumor probability with a 3-D CNN, and VOIs with higher estimated probability are selected as tumor candidates. Since the candidates may overlap each other, a novel scheme is designed to aggregate the overlapped candidates. During the aggregation, candidates are prioritized based on estimated tumor probability to alleviate over-aggregation issue. The relationship between the sizes of VOI and target tumor is optimally exploited to effectively perform each stage of our detection algorithm. On evaluation with a test set of 171 tumors, our method achieved sensitivities of 95% (162/171), 90% (154/171), 85% (145/171), and 80% (137/171) with 14.03, 6.92, 4.91, and 3.62 false positives per patient (with six passes), respectively. In summary, our method is more general and much faster than preliminary works and demonstrates promising results.
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multi dimensional tumor detection in automated whole Breast Ultrasound using topographic watershed
IEEE Transactions on Medical Imaging, 2014Co-Authors: Rong Tai Chen, Chiunsheng Huang, Yeunchung Chang, Yawen Yang, Ming Jen Hung, Rueyfeng ChangAbstract:Automated whole Breast Ultrasound (ABUS) is becoming a popular screening modality for whole Breast examination. Compared to conventional handheld Ultrasound, ABUS achieves operator-independent and is feasible for mass screening. However, reviewing hundreds of slices in an ABUS image volume is time-consuming. A computer-aided detection (CADe) system based on watershed transform was proposed in this study to accelerate the reviewing. The watershed transform was applied to gather similar tissues around local minima to be homogeneous regions. The likelihoods of being tumors of the regions were estimated using the quantitative morphology, intensity, and texture features in the 2-D/3-D false positive reduction (FPR). The collected database comprised 68 benign and 65 malignant tumors. As a result, the proposed system achieved sensitivities of 100% (133/133), 90% (121/133), and 80% (107/133) with FPs/pass of 9.44, 5.42, and 3.33, respectively. The figure of merit of the combination of three feature sets is 0.46 which is significantly better than that of other feature sets ( [Formula: see text]). In summary, the proposed CADe system based on the multi-dimensional FPR using the integrated feature set is promising in detecting tumors in ABUS images.
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computer aided tumor detection based on multi scale blob detection algorithm in automated Breast Ultrasound images
IEEE Transactions on Medical Imaging, 2013Co-Authors: Woo Kyung Moon, Yi Wei Shen, Chiunsheng Huang, Min Sun Bae, Jeonhor Chen, Rueyfeng ChangAbstract:Automated whole Breast Ultrasound (ABUS) is an emerging screening tool for detecting Breast abnormalities. In this study, a computer-aided detection (CADe) system based on multi-scale blob detection was developed for analyzing ABUS images. The performance of the proposed CADe system was tested using a database composed of 136 Breast lesions (58 benign lesions and 78 malignant lesions) and 37 normal cases. After speckle noise reduction, Hessian analysis with multi-scale blob detection was applied for the detection of tumors. This method detected every tumor, but some nontumors were also detected. The tumor likelihoods for the remaining candidates were estimated using a logistic regression model based on blobness, internal echo, and morphology features. The tumor candidates with tumor likelihoods higher than a specific threshold (0.4) were considered tumors. By using the combination of blobness, internal echo, and morphology features with 10-fold cross-validation, the proposed CAD system showed sensitivities of 100%, 90%, and 70% with false positives per pass of 17.4, 8.8, and 2.7, respectively. Our results suggest that CADe systems based on multi-scale blob detection can be used to detect Breast tumors in ABUS images.
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computer aided diagnosis for the classification of Breast masses in automated whole Breast Ultrasound images
Ultrasound in Medicine and Biology, 2011Co-Authors: Woo Kyung Moon, Yi Wei Shen, Chiunsheng Huang, Li Ren Chiang, Rueyfeng ChangAbstract:Abstract New automated whole Breast Ultrasound (ABUS) machines have recently been developed and the Ultrasound (US) volume dataset of the whole Breast can be acquired in a standard manner. The purpose of this study was to develop a novel computer-aided diagnosis system for classification of Breast masses in ABUS images. One hundred forty-seven cases (76 benign and 71 malignant Breast masses) were obtained by a commercially available ABUS system. Because the distance of neighboring slices in ABUS images is fixed and small, these continuous slices were used for reconstruction as three-dimensional (3-D) US images. The 3-D tumor contour was segmented using the level-set segmentation method. Then, the 3-D features, including the texture, shape and ellipsoid fitting were extracted based on the segmented 3-D tumor contour to classify benign and malignant tumors based on the logistic regression model. The Student’s t test, Mann-Whitney U test and receiver operating characteristic (ROC) curve analysis were used for statistical analysis. From the Az values of ROC curves, the shape features (0.9138) are better than the texture features (0.8603) and the ellipsoid fitting features (0.8496) for classification. The difference was significant between shape and ellipsoid fitting features ( p = 0.0382). However, combination of ellipsoid fitting features and shape features can achieve a best performance with accuracy of 85.0% (125/147), sensitivity of 84.5% (60/71), specificity of 85.5% (65/76) and the area under the ROC curve Az of 0.9466. The results showed that ABUS images could be used for computer-aided feature extraction and classification of Breast tumors. (E-mail: rfchang@csie.ntu.edu.tw )
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rapid image stitching and computer aided detection for multipass automated Breast Ultrasound
Medical Physics, 2010Co-Authors: Rueyfeng Chang, Chiunsheng Huang, Yihong Chou, Kuangche Changchien, Etsuo Takada, Jeonhor ChenAbstract:Purpose: Breast Ultrasound(US) is recently becoming more and more popular for detecting Breast lesions. However, screening results in hundreds of USimages for each subject. This magnitude of images can lead to fatigue in radiologist, causing failure in the detection of lesions of a subtle nature. In this study, an image stitching technique is proposed for combining multipass images of the whole Breast into a series of full-view images, and a fully automatic screening system that works off these images is also presented. Methods: Using the registration technique based on the simple sum of absolute block-mean difference (SBMD) measure, three-pass images were merged into full-view USimages. An automatic screening system was then developed for detecting tumors from these full-view images. The preprocessing step was used to reduce the tumor detection time of the system and to improve image quality. The gray-level slicing method was then used to divide images into numerous regions. Finally, seven computerized features—darkness, uniformity, width-height ratio, area size, nonpersistence, coronal area size, and region continuity—were defined and used to determine whether or not each region was a part of a tumor. Results: In the experiment, there was a total of 25 experimental cases with 26 lesions, and each case was composed of 252 images (three passes, 84 images/pass). The processing time of the proposed stitching procedure for each case was within 30 s with a Pentium IV 2.0 processor, and the detection sensitivity of the proposed CADsystem was 92.3% with 1.76 false positives per case. Conclusions: The proposed automatic screening system can be applied to the whole Breast images stitched together via SBMD-based registration in order to detect tumors.
Nico Karssemeijer - One of the best experts on this subject based on the ideXlab platform.
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validation of radiologists findings by computer aided detection cad software in Breast cancer detection with automated 3d Breast Ultrasound a concept study in implementation of artificial intelligence software
Acta Radiologica, 2020Co-Authors: Jan Van Zelst, Tao Tan, Ritse M Mann, Nico KarssemeijerAbstract:BackgroundComputer-aided detection software for automated Breast Ultrasound has been shown to have potential in improving the accuracy of radiologists. Alternative ways of implementing computer-aid...
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Computer-Aided Detection of Cancer in Automated 3-D Breast Ultrasound
IEEE Transactions on Medical Imaging, 2013Co-Authors: Bram Platel, Ritse M Mann, László Tabár, Nico KarssemeijerAbstract:Automated 3-D Breast Ultrasound (ABUS) has gained a lot of interest and may become widely used in screening of dense Breasts, where sensitivity of mammography is poor. However, reading ABUS images is time consuming, and subtle abnormalities may be missed. Therefore, we are developing a computer aided detection (CAD) system to help reduce reading time and prevent errors. In the multi-stage system we propose, segmentations of the Breast, the nipple and the chestwall are performed, providing landmarks for the detection algorithm. Subsequently, voxel features characterizing coronal spiculation patterns, blobness, contrast, and depth are extracted. Using an ensemble of neural-network classifiers, a likelihood map indicating potential abnormality is computed. Local maxima in the likelihood map are determined and form a set of candidates in each image. These candidates are further processed in a second detection stage, which includes region segmentation, feature extraction and a final classification. On region level, classification experiments were performed using different classifiers including an ensemble of neural networks, a support vector machine, a k-nearest neighbors, a linear discriminant, and a gentle boost classifier. Performance was determined using a dataset of 238 patients with 348 images (views), including 169 malignant and 154 benign lesions. Using free response receiver operating characteristic (FROC) analysis, the system obtains a view-based sensitivity of 64% at 1 false positives per image using an ensemble of neural-network classifiers.
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Computer-Aided Lesion Diagnosis in Automated 3-D Breast Ultrasound Using Coronal Spiculation
IEEE Transactions on Medical Imaging, 2012Co-Authors: Bram Platel, Henkjan Huisman, Clara I. Sanchez, Nico KarssemeijerAbstract:A computer-aided diagnosis (CAD) system for the classification of lesions as malignant or benign in automated 3-D Breast Ultrasound (ABUS) images, is presented. Lesions are automatically segmented when a seed point is provided, using dynamic programming in combination with a spiral scanning technique. A novel aspect of ABUS imaging is the presence of spiculation patterns in coronal planes perpendicular to the transducer. Spiculation patterns are characteristic for malignant lesions. Therefore, we compute spiculation features and combine them with features related to echotexture, echogenicity, shape, posterior acoustic behavior and margins. Classification experiments were performed using a support vector machine classifier and evaluation was done with leave-one-patient-out cross-validation. Receiver operator characteristic (ROC) analysis was used to determine performance of the system on a dataset of 201 lesions. We found that spiculation was among the most discriminative features. Using all features, the area under the ROC curve (Az) was 0.93, which was significantly higher than the performance without spiculation features (Az=0.90, p=0.02). On a subset of 88 cases, classification performance of CAD (Az=0.90) was comparable to the average performance of 10 readers (Az=0.87).