Literature DB >> 11865987

Computerized diagnosis of breast lesions on ultrasound.

Karla Horsch1, Maryellen L Giger, Luz A Venta, Carl J Vyborny.   

Abstract

We present a computer-aided diagnosis (CAD) method for breast lesions on ultrasound that is based on the automatic segmentation of lesions and the automatic extraction of four features related to the lesion shape, margin, texture, and posterior acoustic behavior. Using a database of 400 cases (94 malignant lesions, 124 complex cysts, and 182 benign solid lesions), we investigate the marginal benefit of each feature in our CAD method and the performance of our CAD method in distinguishing malignant lesions from various classes of benign lesions. Finally, independent validation is performed on our CAD method. Eleven independent trials yielded an average Az value of 0.87 in the task of distinguishing malignant from benign lesions.

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Year:  2002        PMID: 11865987     DOI: 10.1118/1.1429239

Source DB:  PubMed          Journal:  Med Phys        ISSN: 0094-2405            Impact factor:   4.071


  37 in total

1.  An effective approach of lesion segmentation within the breast ultrasound image based on the cellular automata principle.

Authors:  Yan Liu; H D Cheng; Jianhua Huang; Yingtao Zhang; Xianglong Tang
Journal:  J Digit Imaging       Date:  2012-10       Impact factor: 4.056

2.  Classification of benign and malignant breast masses based on shape and texture features in sonography images.

Authors:  Fahimeh Sadat Zakeri; Hamid Behnam; Nasrin Ahmadinejad
Journal:  J Med Syst       Date:  2010-11-17       Impact factor: 4.460

3.  Malignant and benign breast masses on 3D US volumetric images: effect of computer-aided diagnosis on radiologist accuracy.

Authors:  Berkman Sahiner; Heang-Ping Chan; Marilyn A Roubidoux; Lubomir M Hadjiiski; Mark A Helvie; Chintana Paramagul; Janet Bailey; Alexis V Nees; Caroline Blane
Journal:  Radiology       Date:  2007-01-23       Impact factor: 11.105

4.  Optimized approach to decision fusion of heterogeneous data for breast cancer diagnosis.

Authors:  Jonathan L Jesneck; Loren W Nolte; Jay A Baker; Carey E Floyd; Joseph Y Lo
Journal:  Med Phys       Date:  2006-08       Impact factor: 4.071

5.  Enhancement of breast CADx with unlabeled data.

Authors:  Andrew R Jamieson; Maryellen L Giger; Karen Drukker; Lorenzo L Pesce
Journal:  Med Phys       Date:  2010-08       Impact factor: 4.071

6.  Segmentation of elastographic images using a coarse-to-fine active contour model.

Authors:  Wu Liu; James A Zagzebski; Tomy Varghese; Charles R Dyer; Udomchai Techavipoo; Timothy J Hall
Journal:  Ultrasound Med Biol       Date:  2006-03       Impact factor: 2.998

Review 7.  Anniversary paper: History and status of CAD and quantitative image analysis: the role of Medical Physics and AAPM.

Authors:  Maryellen L Giger; Heang-Ping Chan; John Boone
Journal:  Med Phys       Date:  2008-12       Impact factor: 4.071

8.  Prevalence scaling: applications to an intelligent workstation for the diagnosis of breast cancer.

Authors:  Karla Horsch; Maryellen L Giger; Charles E Metz
Journal:  Acad Radiol       Date:  2008-11       Impact factor: 3.173

9.  Three-dimensional ultrasound guidance of autonomous robotic breast biopsy: feasibility study.

Authors:  Kaicheng Liang; Albert J Rogers; Edward D Light; Daniel von Allmen; Stephen W Smith
Journal:  Ultrasound Med Biol       Date:  2010-01       Impact factor: 2.998

10.  Quantitative ultrasound image analysis of axillary lymph node status in breast cancer patients.

Authors:  Karen Drukker; Maryellen Giger; Lina Arbash Meinel; Adam Starkey; Jyothi Janardanan; Hiroyuki Abe
Journal:  Int J Comput Assist Radiol Surg       Date:  2013-03-24       Impact factor: 2.924

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