Literature DB >> 12208333

Retrieval technique for the diagnosis of solid breast tumors on sonogram.

Wen-Jia Kuo1, Ruey-Feng Chang, Cheng Chun Lee, Woo Kyung Moon, Dar-Ren Chen.   

Abstract

We evaluated a series of pathologically proven breast tumors using an image-retrieval technique for classifying benign and malignant lesions. A total of 263 breast tumors (129 malignant and 134 benign) were retrospectively evaluated. The physician located regions-of-interest (ROI) of ultrasonic images and texture parameters (contrast, covariance and dissimilarity) were used in the process of the content-based image-retrieval technique. The accuracy of using the retrieval technique for classifying malignancies was 92.55% (236 of 255), the sensitivity was 94.44% (119 of 126), the specificity was 90.70% (117 of 129), the positive predictive value was 90.84% (119 of 131), and negative predictive value was 94.35% (117 of 124) for the proposed computer-aided diagnostic system. This computer-aided diagnosis system can provide a useful tool and its high negative predictive value could potentially help avert benign biopsies. It is unnecessary to perform any training procedures. This computer-aided diagnosis system can provide a second opinion for a sonographic interpreter; the main advantage in this proposed system is that we do not need any training. Historical cases can be directly added into the database and training of the diagnosis system again is not needed. With the growth of the database, more and more information can be collected and used as reference cases while performing diagnoses. This increases the flexibility of our diagnostic system.

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Mesh:

Year:  2002        PMID: 12208333     DOI: 10.1016/s0301-5629(02)00541-0

Source DB:  PubMed          Journal:  Ultrasound Med Biol        ISSN: 0301-5629            Impact factor:   2.998


  13 in total

1.  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

Review 2.  A review of breast ultrasound.

Authors:  Chandra M Sehgal; Susan P Weinstein; Peter H Arger; Emily F Conant
Journal:  J Mammary Gland Biol Neoplasia       Date:  2006-04       Impact factor: 2.673

3.  Breast mass classification on sonographic images on the basis of shape analysis.

Authors:  Hamid Behnam; Fahimeh Sadat Zakeri; Nasrin Ahmadinejad
Journal:  J Med Ultrason (2001)       Date:  2010-08-07       Impact factor: 1.314

Review 4.  Overview on subjective similarity of images for content-based medical image retrieval.

Authors:  Chisako Muramatsu
Journal:  Radiol Phys Technol       Date:  2018-05-08

5.  A similarity study of content-based image retrieval system for breast cancer using decision tree.

Authors:  Hyun-Chong Cho; Lubomir Hadjiiski; Berkman Sahiner; Heang-Ping Chan; Mark Helvie; Chintana Paramagul; Alexis V Nees
Journal:  Med Phys       Date:  2013-01       Impact factor: 4.071

6.  Computer-Aided Diagnosis in Mammography Using Content-based Image Retrieval Approaches: Current Status and Future Perspectives.

Authors:  Bin Zheng
Journal:  Algorithms       Date:  2009-06-01

7.  Ultrasound GLCM texture analysis of radiation-induced parotid-gland injury in head-and-neck cancer radiotherapy: an in vivo study of late toxicity.

Authors:  Xiaofeng Yang; Srini Tridandapani; Jonathan J Beitler; David S Yu; Emi J Yoshida; Walter J Curran; Tian Liu
Journal:  Med Phys       Date:  2012-09       Impact factor: 4.071

8.  Image analysis in medical imaging: recent advances in selected examples.

Authors:  G Dougherty
Journal:  Biomed Imaging Interv J       Date:  2010-07-01

9.  Design and development of a content-based medical image retrieval system for spine vertebrae irregularity.

Authors:  Aouache Mustapha; Aini Hussain; Salina Abdul Samad; Mohd Asyraf Zulkifley; Wan Mimi Diyana Wan Zaki; Hamzaini Abdul Hamid
Journal:  Biomed Eng Online       Date:  2015-01-16       Impact factor: 2.819

Review 10.  Quantification of heterogeneity as a biomarker in tumor imaging: a systematic review.

Authors:  Lejla Alic; Wiro J Niessen; Jifke F Veenland
Journal:  PLoS One       Date:  2014-10-20       Impact factor: 3.240

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