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330 results on '"Digital mammogram"'

52. Classification of Individual and Clustered Microcalcifications in Digital Mammograms Using Evolutionary Neural Networks

Author

Hernández-Cisneros, Rolando R., Terashima-Marín, Hugo, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Gelbukh, Alexander, editor, and Reyes-Garcia, Carlos Alberto, editor

Published
2006
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53. A Comparison of Breast Tissue Classification Techniques

Author

Oliver, Arnau, Freixenet, Jordi, Martí, Robert, Zwiggelaar, Reyer, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Larsen, Rasmus, editor, Nielsen, Mads, editor, and Sporring, Jon, editor

Published
2006
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54. Capturing Microcalcification Patterns in Dense Parenchyma with Wavelet-Based Eigenimages

Author

Arikidis, Nikolaos, Skiadopoulos, Spyros, Sakellaropoulos, Filippos, Panayiotakis, George, Costaridou, Lena, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Astley, Susan M., editor, Brady, Michael, editor, Rose, Chris, editor, and Zwiggelaar, Reyer, editor

Published
2006
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55. Contrast Threshold of 4 Full Field Digital Mammography Systems Using Different Measurement Methods

Author

Carton, A. -K., Bosmans, H., Vanongeval, C., Souverijns, G., Marchal, G., Jacobs, J., Vandenbroucke, D., Pauwels, H., Nijs, K., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Astley, Susan M., editor, Brady, Michael, editor, Rose, Chris, editor, and Zwiggelaar, Reyer, editor

Published
2006
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56. Comparison Between CRT and LCD Displays for Full-Field-Digital-Mammography (FFDM) Interpretation

Author

Del Frate, Chiara, Bestagno, Alexia, Londero, Viviana, Mucelli, Raffaella Pozzi, Salomoni, Valerio, Bazzocchi, Massimo, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Astley, Susan M., editor, Brady, Michael, editor, Rose, Chris, editor, and Zwiggelaar, Reyer, editor

Published
2006
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57. Using Wavelet-Based Features to Identify Masses in Dense Breast Parenchyma

Author

Sakellaropoulos, Filippos, Skiadopoulos, Spyros, Karahaliou, Anna, Costaridou, Lena, Panayiotakis, George, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Astley, Susan M., editor, Brady, Michael, editor, Rose, Chris, editor, and Zwiggelaar, Reyer, editor

Published
2006
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58. Evaluating the Effect of Dose on Reconstructed Image Quality in Digital Tomosynthesis

Author

Kempston, Michael P., Mainprize, James G., Yaffe, Martin J., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Astley, Susan M., editor, Brady, Michael, editor, Rose, Chris, editor, and Zwiggelaar, Reyer, editor

Published
2006
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59. Impact of Textured Background on Scoring of Simulated CDMAM Phantom

Author

Grosjean, Bénédicte, Muller, Serge, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Astley, Susan M., editor, Brady, Michael, editor, Rose, Chris, editor, and Zwiggelaar, Reyer, editor

Published
2006
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60. Web Services for the DDSM and Digital Mammography Research

Author

Rose, Chris, Turi, Daniele, Williams, Alan, Wolstencroft, Katy, Taylor, Chris, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Astley, Susan M., editor, Brady, Michael, editor, Rose, Chris, editor, and Zwiggelaar, Reyer, editor

Published
2006
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61. Detection of Microcalcifications in Digital Mammograms Based on Dual-Threshold

Author

Wu, Yuan, Huang, Qian, Peng, YongHong, Situ, Wuchao, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Astley, Susan M., editor, Brady, Michael, editor, Rose, Chris, editor, and Zwiggelaar, Reyer, editor

Published
2006
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62. Image Similarity and Asymmetry to Improve Computer-Aided Detection of Breast Cancer

Author

Tahmoush, Dave, Samet, Hanan, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Astley, Susan M., editor, Brady, Michael, editor, Rose, Chris, editor, and Zwiggelaar, Reyer, editor

Published
2006
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64. Digital Mammography

Author

Yaffe, Martin J., Dreyer, Keith J., editor, Thrall, James H., editor, Hirschorn, David S., editor, and Mehta, Amit, editor

Published
2006
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65. Detection of Microcalcification Clusters in Mammograms Using a Difference of Optimized Gaussian Filters

Author

Oporto-Díaz, Samuel, Hernández-Cisneros, Rolando, Terashima-Marín, Hugo, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Kamel, Mohamed, editor, and Campilho, Aurélio, editor

Published
2005
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66. Texture Granularities

Author

Southam, Paul, Harvey, Richard, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Roli, Fabio, editor, and Vitulano, Sergio, editor

Published
2005
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67. The Spiral Method Applied to the Study of the Microcalcifications in Mammograms

Author

Vitulano, Sergio, Casanova, Andrea, Savona, Valentina, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Roli, Fabio, editor, and Vitulano, Sergio, editor

Published
2005
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68. Mammogram Analysis Using Two-Dimensional Autoregressive Models: Sufficient or Not?

Author

Lee, Sarah, Stathaki, Tania, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Roli, Fabio, editor, and Vitulano, Sergio, editor

Published
2005
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69. A Hybrid Classifier for Mass Classification with Different Kinds of Features in Mammography

Author

Zhang, Ping, Kumar, Kuldeep, Verma, Brijesh, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Wang, Lipo, editor, and Jin, Yaochu, editor

Published
2005
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70. Problems Related to Automatic Nipple Extraction

Author

Olsén, Christina, Georgsson, Fredrik, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Kalviainen, Heikki, editor, Parkkinen, Jussi, editor, and Kaarna, Arto, editor

Published
2005
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71. Classification of Microcalcification Clusters in Digital Mammograms Using a Stack Generalization Based Classifier

Author

Nashid Alam, Erika R. E. Denton, and Reyer Zwiggelaar

Subjects
digital mammogram, microcalcification, stack generalization, classification, morphological features, Photography, TR1-1050, Computer applications to medicine. Medical informatics, R858-859.7, Electronic computers. Computer science, QA75.5-76.95
Abstract

This paper presents a machine learning based approach for the discrimination of malignant and benign microcalcification (MC) clusters in digital mammograms. A series of morphological operations was carried out to facilitate the feature extraction from segmented microcalcification. A combination of morphological, texture, and distribution features from individual MC components and MC clusters were extracted and a correlation-based feature selection technique was used. The clinical relevance of the selected features is discussed. The proposed method was evaluated using three different databases: Optimam Mammography Image Database (OMI-DB), Digital Database for Screening Mammography (DDSM), and Mammographic Image Analysis Society (MIAS) database. The best classification accuracy ( 95.00 ± 0.57 %) was achieved for OPTIMAM using a stack generalization classifier with 10-fold cross validation obtaining an A z value equal to 0.97 ± 0.01 .

Published
2019
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72. Filtering of Mammograms Based on Convolution with Directional Fractal Masks to Enhance Microcalcifications

Author

Rocio Sanchez-Montero, Juan-Antonio Martinez-Rojas, Pablo-Luis Lopez-Espi, Luis Nuñez-Martin, and Efren Diez-Jimenez

Subjects
mask convolution enhancement, microcalcification, digital mammogram, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999
Abstract

The image processing of mammograms is very important for the early detection of breast pathologies, including cancer. This paper proposes a new technique based on directional fractal filtering for detecting microcalcification clusters or irregularly shaped microcalcifications. The proposed algorithm has two parts: a preprocessing step for detecting and locating microcalcification; and a second zooming, enhancement, and segmentation step. Detection is performed by image convolution using a set of masks with interesting fractal properties. Combined with other simple mathematical operations, remarkable contrast enhancement and segmentation are produced. The final result permits the clear delineation of the shape of individual microcalcifications. A comparison is made with other microcalcification enhancement techniques described in the literature.

Published
2019
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73. Fractal-Based Approach for Segmentation of Address Block in Postal Envelopes

Author

Eiterer, Luiz Felipe, Facon, Jacques, Menoti, David, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Sanfeliu, Alberto, editor, Martínez Trinidad, José Francisco, editor, and Carrasco Ochoa, Jesús Ariel, editor

Published
2004
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74. Comparing Study for Detecting Microcalcifications in Digital Mammogram Using Wavelets

Author

Yang, Ju Cheng, Shin, Jin Wook, Park, Dong Sun, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Yang, Zheng Rong, editor, Yin, Hujun, editor, and Everson, Richard M., editor

Published
2004
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75. Combination of SVM Knowledge for Microcalcification Detection in Digital Mammograms

Author

Li, Ying, Jiang, Jianmin, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Yang, Zheng Rong, editor, Yin, Hujun, editor, and Everson, Richard M., editor

Published
2004
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76. Inorganic Phosphates Investigation by Support Vector Machine

Author

Pierro, Cinzia, Capitelli, Francesco, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Laganá, Antonio, editor, Gavrilova, Marina L., editor, Kumar, Vipin, editor, Mun, Youngsong, editor, Tan, C. J. Kenneth, editor, and Gervasi, Osvaldo, editor

Published
2004
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78. Associative Classifiers for Medical Images

Author

Antonie, Maria-Luiza, Zaïane, Osmar R., Coman, Alexandru, Goos, Gerhard, editor, Hartmanis, Juris, editor, van Leeuwen, Jan, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Zaïane, Osmar R., editor, Simoff, Simeon J., editor, and Djeraba, Chabane, editor

Published
2003
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93. Scalable Mammogram Retrieval Using Composite Anchor Graph Hashing With Iterative Quantization.

Author

Liu, Jingjing, Zhang, Shaoting, Liu, Wei, Deng, Cheng, Zheng, Yuanjie, and Metaxas, Dimitris N.

Subjects
*MAMMOGRAMS, *GRAPHIC methods, *IMAGE retrieval, *IMAGE processing, *DATA analysis
Abstract

Content-based image retrieval (CBIR) shows great significance in clinical decision-making, which explores the visual content of medical images rather than keywords, tags, or descriptions. It provides doctors an image-guided approach to explore relevant cases that could offer doctors instructive reference. Mammogram screening has been known to be widely used in the early stage diagnosis of breast cancer and could reduce its morbidity and mortality. In this paper, we aim to develop a scalable CBIR method for a large repository of mammogram. To this end, we extend the original Anchor Graph Hashing (AGH) and propose a new unsupervised hashing algorithm, named as composite AGH with iterative quantization (C-AGH-ITQ), which compresses mammographic regions of interest (ROIs) into compact binary codes and enables real-time searching in Hamming space. Multimodal features and different distance metrics are integrated, performing upon a composite Anchor Graph. To improve the effectiveness of the hash code, quantization error is further iteratively minimized by introducing an orthogonal rotation matrix. We evaluate the presented C-AGH-ITQ algorithm on a data set of 11 533 mammographic ROIs obtained from the Digital Database for Screening Mammography. Our method obtains more than 84% retrieval precision and 93% classification accuracy (using $k$ NN prediction), which demonstrates that hash codes produced by C-AGH-ITQ well capture the visual similarities between mammographic images. In addition, since C-AGH-ITQ ensures linear complexity of the training procedure and constant time for query, our system is readily applicable to large-scale mammogram databases and has the potential to provide abundant clinical cases as reference. [ABSTRACT FROM AUTHOR]

Published
2017
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94. Can Occult Invasive Disease in Ductal Carcinoma In Situ Be Predicted Using Computer-extracted Mammographic Features?

Author

Shi, Bibo, Grimm, Lars J., Mazurowski, Maciej A., Baker, Jay A., Marks, Jeffrey R., King, Lorraine M., Maley, Carlo C., Hwang, E. Shelley, and Lo, Joseph Y.

Abstract

Rationale and Objectives: This study aimed to determine whether mammographic features assessed by radiologists and using computer algorithms are prognostic of occult invasive disease for patients showing ductal carcinoma in situ (DCIS) only in core biopsy.Materials and Methods: In this retrospective study, we analyzed data from 99 subjects with DCIS (74 pure DCIS, 25 DCIS with occult invasion). We developed a computer-vision algorithm capable of extracting 113 features from magnification views in mammograms and combining these features to predict whether a DCIS case will be upstaged to invasive cancer at the time of definitive surgery. In comparison, we also built predictive models based on physician-interpreted features, which included histologic features extracted from biopsy reports and Breast Imaging Reporting and Data System-related mammographic features assessed by two radiologists. The generalization performance was assessed using leave-one-out cross validation with the receiver operating characteristic curve analysis.Results: Using the computer-extracted mammographic features, the multivariate classifier was able to distinguish DCIS with occult invasion from pure DCIS, with an area under the curve for receiver operating characteristic equal to 0.70 (95% confidence interval: 0.59-0.81). The physician-interpreted features including histologic features and Breast Imaging Reporting and Data System-related mammographic features assessed by two radiologists showed mixed results, and only one radiologist's subjective assessment was predictive, with an area under the curve for receiver operating characteristic equal to 0.68 (95% confidence interval: 0.57-0.81).Conclusions: Predicting upstaging for DCIS based upon mammograms is challenging, and there exists significant interobserver variability among radiologists. However, the proposed computer-extracted mammographic features are promising for the prediction of occult invasion in DCIS. [ABSTRACT FROM AUTHOR]

Published
2017
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96. Contrast-Enhanced Mammography Implementation, Performance, and Use for Supplemental Breast Cancer Screening

Author

Matthew F. Covington

Subjects
medicine.medical_specialty, Breast imaging, viruses, media_common.quotation_subject, Contrast Media, Breast Neoplasms, Sensitivity and Specificity, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, Breast cancer screening, 0302 clinical medicine, immune system diseases, hemic and lymphatic diseases, Humans, Contrast (vision), Medicine, Mammography, Radiology, Nuclear Medicine and imaging, Breast, skin and connective tissue diseases, media_common, Breast tissue, medicine.diagnostic_test, business.industry, hemic and immune systems, General Medicine, Digital mammogram, Radiographic Image Enhancement, 030220 oncology & carcinogenesis, Female, Radiology, business
Abstract

Contrast-enhanced mammography (CEM) is an emerging breast imaging technology that provides recombined contrast-enhanced images of the breast in addition to low-energy images analogous to a 2-dimensional full-field digital mammogram. Because most breast imaging centers do not use CEM at this time, a detailed overview of CEM implementation and performance is presented. Thereafter, the potential use of CEM for supplemental screening is discussed in detail, given the importance of this topic for the future of the CEM community. Diagnostic performance, safety, and cost considerations of CEM for dense breast tissue supplemental screening are discussed.

Published
2021
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98. Benefits of digital breast tomosynthesis: A lesion-level analysis

Author

Susan C. Harvey, Eniola Oluyemi, Mary Kate Jones, Emily B. Ambinder, Tali Amir, Lisa A. Mullen, Evan L Honig, and Matthew D. Alvin

Subjects
medicine.medical_specialty, Breast Neoplasms, 030218 nuclear medicine & medical imaging, Lesion, 03 medical and health sciences, Breast cancer screening, 0302 clinical medicine, medicine, Humans, Mass Screening, Mammography, Breast, Early Detection of Cancer, Retrospective Studies, medicine.diagnostic_test, business.industry, Health Policy, Public Health, Environmental and Occupational Health, Digital Breast Tomosynthesis, Digital mammogram, 030220 oncology & carcinogenesis, Female, Radiology, medicine.symptom, business
Abstract

Objective To compare outcome metrics of digital breast tomosynthesis (DBT) breast cancer screening with full-field digital mammogram (FFDM); specifically, to compare recall rates by the type of recalled finding, and to assess if screening with DBT versus FFDM changes biopsy recommendations and if the likelihood of malignancy varied by lesion type, if detected on DBT or FFDM screening mammogram. Methods The outcomes of 22,055 FFDM and DBT screening mammograms were retrospectively reviewed. The exams were performed at an academic institution between August 2015 and September 2016. Performance of screening with FFDM versus DBT was compared in terms of recall rate and percentage of recalled lesions resulting in a cancer diagnosis, with subset analyses performed for specific mammographic findings. Results The recall rate was 10.6% for FFDM and 8.0% for DBT ( p Conclusions DBT screening confers an advantage in detection of architectural distortion representing malignancy. Recall rate of asymmetries are reduced with screening DBT, probably due to reduction of tissue superimposition. Calcifications pose a particularly difficult diagnostic challenge for breast imagers, regardless of screening mammogram type.

Published
2020
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99. Classification of masses in digital mammograms using Biogeography-based optimization technique

Author

Ranjana Ingolikar and Shankar Thawkar

Subjects
Artificial neural network, 0209 industrial biotechnology, Adaptive neuro-fuzzy inference system, Digital mammography, General Computer Science, Computer science, Feature vector, Inference system, Feature selection, 02 engineering and technology, Machine learning, computer.software_genre, Biogeography-based optimization, lcsh:QA75.5-76.95, 020901 industrial engineering & automation, 0202 electrical engineering, electronic engineering, information engineering, Effective treatment, business.industry, Classification, Digital mammogram, ROC curve, ComputingMethodologies_PATTERNRECOGNITION, 020201 artificial intelligence & image processing, Artificial intelligence, lcsh:Electronic computers. Computer science, business, computer, Classifier (UML)
Abstract

Breast cancer is a leading cause of death in women in both developed and developing countries. Design and development of computer-based systems can assist radiologists in the effective treatment of breast cancer. For the design of an efficient classification system, efficient feature selection techniques must be used to reduce complexity of feature space in digital mammogram classification. The proposed methodology aims to explore use of Biogeography-based optimization to select a subset of features. Adaptive neuro-fuzzy inference system and artificial neural network are employed to evaluate fitness of the selected features. The features selected are used to train and test adaptive neuro-fuzzy inference system and artificial neural network classifiers. The experiment employed over 651 mammograms. The classification results shows that Biogeography-based optimization with adaptive neuro-fuzzy inference system is superior to Biogeography-based optimization with artificial neural network. Adaptive neuro-fuzzy inference system classifier achieve an accuracy of 98.92% with sensitivity of 99.10%, specificity of 98.72% and area under curve AZ = 0.999 ± 0.000. Outcomes achieved with the proposed Biogeography-based optimization with adaptive neuro-fuzzy inference system are far better as compared to some recent work.

Published
2020

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