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Literature DB >> 20480383

Online mammographic images database for development and comparison of CAD schemes.

Bruno Roberto Nepomuceno Matheus¹, Homero Schiabel.

Abstract

Considering the difficulties in finding good-quality images for the development and test of computer-aided diagnosis (CAD), this paper presents a public online mammographic images database free for all interested viewers and aimed to help develop and evaluate CAD schemes. The digitalization of the mammographic images is made with suitable contrast and spatial resolution for processing purposes. The broad recuperation system allows the user to search for different images, exams, or patient characteristics. Comparison with other databases currently available has shown that the presented database has a sufficient number of images, is of high quality, and is the only one to include a functional search system.

Entities: Species

Mesh：

Year: 2011 PMID： 20480383 PMCID： PMC3092049 DOI： 10.1007/s10278-010-9297-2

Source DB: PubMed Journal: J Digit Imaging ISSN： 0897-1889 Impact factor: 4.056

6 in total

1. Maximum likelihood fitting of FROC curves under an initial-detection-and-candidate-analysis model.

Authors: Darrin C Edwards; Matthew A Kupinski; Charles E Metz; Robert M Nishikawa
Journal: Med Phys Date: 2002-12 Impact factor: 4.071

2. Improvement in radiologists' characterization of malignant and benign breast masses on serial mammograms with computer-aided diagnosis: an ROC study.

Authors: Lubomir Hadjiiski; Heang-Ping Chan; Berkman Sahiner; Mark A Helvie; Marilyn A Roubidoux; Caroline Blane; Chintana Paramagul; Nicholas Petrick; Janet Bailey; Katherine Klein; Michelle Foster; Stephanie Patterson; Dorit Adler; Alexis Nees; Joseph Shen
Journal: Radiology Date: 2004-08-18 Impact factor: 11.105

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Authors: R M Nishikawa
Journal: Breast Dis Date: 1998-08

4. An approach to automated detection of tumors in mammograms.

Authors: D Brzakovic; X M Luo; P Brzakovic
Journal: IEEE Trans Med Imaging Date: 1990 Impact factor: 10.048

5. Effect of case selection on the performance of computer-aided detection schemes.

Authors: R M Nishikawa; M L Giger; K Doi; C E Metz; F F Yin; C J Vyborny; R A Schmidt
Journal: Med Phys Date: 1994-02 Impact factor: 4.071

6. Computer-aided diagnosis of mammographic microcalcification clusters.

Authors: Maria Kallergi
Journal: Med Phys Date: 2004-02 Impact factor: 4.071

6 in total

8 in total

1. Development of a Reference Image Collection Library for Histopathology Image Processing, Analysis and Decision Support Systems Research.

Authors: Spiros Kostopoulos; Panagiota Ravazoula; Pantelis Asvestas; Ioannis Kalatzis; George Xenogiannopoulos; Dionisis Cavouras; Dimitris Glotsos
Journal: J Digit Imaging Date: 2017-06 Impact factor: 4.056

2. A modified undecimated discrete wavelet transform based approach to mammographic image denoising.

Authors: Eri Matsuyama; Du-Yih Tsai; Yongbum Lee; Masaki Tsurumaki; Noriyuki Takahashi; Haruyuki Watanabe; Hsian-Min Chen
Journal: J Digit Imaging Date: 2013-08 Impact factor: 4.056

3. Mammographic image denoising and enhancement using the Anscombe transformation, adaptive wiener filtering, and the modulation transfer function.

Authors: Larissa C S Romualdo; Marcelo A C Vieira; Homero Schiabel; Nelson D A Mascarenhas; Lucas R Borges
Journal: J Digit Imaging Date: 2013-04 Impact factor: 4.056

Online mammographic images database for development and comparison of CAD schemes.

1. Maximum likelihood fitting of FROC curves under an initial-detection-and-candidate-analysis model.

2. Improvement in radiologists' characterization of malignant and benign breast masses on serial mammograms with computer-aided diagnosis: an ROC study.

3. Mammographic databases.

4. An approach to automated detection of tumors in mammograms.

5. Effect of case selection on the performance of computer-aided detection schemes.

6. Computer-aided diagnosis of mammographic microcalcification clusters.

1. Development of a Reference Image Collection Library for Histopathology Image Processing, Analysis and Decision Support Systems Research.

2. A modified undecimated discrete wavelet transform based approach to mammographic image denoising.

3. Mammographic image denoising and enhancement using the Anscombe transformation, adaptive wiener filtering, and the modulation transfer function.

4. Integrative blockwise sparse analysis for tissue characterization and classification.

5. An evaluation of image descriptors combined with clinical data for breast cancer diagnosis.

6. Spatially localized sparse representations for breast lesion characterization.

7. Developing a comprehensive database management system for organization and evaluation of mammography datasets.

Review 8. A Comprehensive Survey on Deep-Learning-Based Breast Cancer Diagnosis.