Literature DB >> 19994502

Breast density analysis for whole breast ultrasound images.

Jeon-Hor Chen1, Chiun-Sheng Huang, Kuang-Che Chang Chien, Etsuo Takada, Woo Kyung Moon, Jeffery H K Wu, Nariya Cho, Yi-Fa Wang, Ruey-Feng Chang.   

Abstract

Breast density has been established as an independent risk factor associated with the development of breast cancer. The terms mammographic density and breast density are often used interchangeably, since most breast density studies are performed with projection mammography. It is known that increase in mammographic density is associated with an increased cancer risk. A sensitive method that allows for the measurement of small changes in breast density may provide useful information for risk management. Despite the efforts to develop quantitative breast density measurements from projection mammograms, the measurements show large variability as a result of projection imaging, differing body position, differing levels of compression, and variation of the x-ray beam characteristics. This study used two separate computer-aided methods, threshold-based and proportion-based evaluations, to analyze breast density on whole breast ultrasound (US) imaging and to compare with the grading results of three radiologists using projection mammography. Thirty-two female subjects with 252 images per case were included in this study. Whole breast US images were obtained from an Aloka SSD-5500 ultrasound machine with an ASU-1004 transducer (Aloka, Japan). Before analyzing breast density, an adaptive speckle reduction filter was used for removing speckle noise, and a robust thresholding algorithm was used to divide breast tissue into fatty or fibroglandular classifications. Then, the proposed approaches were applied for analysis. In the threshold-based method, a statistical model was employed to determine whether each pixel in the breast region belonged to fibroglandular or fatty tissue. The proportion-based method was based on three-dimensional information to calculate the volumetric proportion of fibroglandular tissue to the total breast tissue. The experimental cases were graded by the proposed analysis methods and compared with the ground standard density classification assigned by a majority voting of three experienced breast radiologists. For the threshold-based method, 28 of 32 US test cases and for the proportion-based density classifier, 27 of 32 US test cases were found to be in agreement with the radiologist "ground standard" mammographic interpretations, resulting in overall accuracies of 87.5% and 84.4%, respectively. Moreover, the concordance values of the proposed methods were between 0.0938 and 0.1563, which were less than the average interobserver concordance of 0.3958. The experiment result showed that the proposed methods could be a reference opinion and offer concordant and reliable quantification of breast density for the radiologist.

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Year:  2009        PMID: 19994502     DOI: 10.1118/1.3233682

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


  8 in total

1.  Left ventricular myocardium segmentation on delayed phase of multi-detector row computed tomography.

Authors:  I-Chen Tsai; Yu-Len Huang; Po-Ting Liu; Min-Chi Chen
Journal:  Int J Comput Assist Radiol Surg       Date:  2012-04-19       Impact factor: 2.924

2.  Value of the correct diagnostic pathway through conventional imaging (mammography and ultrasound) in evaluating breast disease.

Authors:  C A Pistolese; T Perretta; E Cossu; F Della Gatta; S Giura; G Simonetti
Journal:  Radiol Med       Date:  2011-06       Impact factor: 3.469

3.  Comparative study of density analysis using automated whole breast ultrasound and MRI.

Authors:  Woo Kyung Moon; Yi-Wei Shen; Chiun-Sheng Huang; Sheng-Chy Luo; Aida Kuzucan; Jeon-Hor Chen; Ruey-Feng Chang
Journal:  Med Phys       Date:  2011-01       Impact factor: 4.071

4.  Impact of positional difference on the measurement of breast density using MRI.

Authors:  Jeon-Hor Chen; Siwa Chan; Yi-Ting Tang; Jia Shen Hon; Po-Chuan Tseng; Angela T Cheriyan; Nikita Rakesh Shah; Dah-Cherng Yeh; San-Kan Lee; Wen-Pin Chen; Christine E McLaren; Min-Ying Su
Journal:  Med Phys       Date:  2015-05       Impact factor: 4.071

5.  Cluster-based filtering framework for speckle reduction in OCT images.

Authors:  M Hossein Eybposh; Zahra Turani; Darius Mehregan; Mohammadreza Nasiriavanaki
Journal:  Biomed Opt Express       Date:  2018-11-19       Impact factor: 3.732

6.  Opportunistic Breast Density Assessment in Women Receiving Low-dose Chest Computed Tomography Screening.

Authors:  Jeon-Hor Chen; Siwa Chan; Nan-Han Lu; Yifan Li; Yu Chieh Tsai; Po Yun Huang; Chia-Ju Chang; Min-Ying Su
Journal:  Acad Radiol       Date:  2016-06-06       Impact factor: 3.173

7.  Automated 3D ultrasound image segmentation to aid breast cancer image interpretation.

Authors:  Peng Gu; Won-Mean Lee; Marilyn A Roubidoux; Jie Yuan; Xueding Wang; Paul L Carson
Journal:  Ultrasonics       Date:  2015-10-31       Impact factor: 2.890

Review 8.  Imaging Breast Density: Established and Emerging Modalities.

Authors:  Jeon-Hor Chen; Gultekin Gulsen; Min-Ying Su
Journal:  Transl Oncol       Date:  2015-12       Impact factor: 4.243

  8 in total

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