Literature DB >> 25843514

Intensity-Invariant Texture Analysis for Classification of BI-RADS Category 3 Breast Masses.

Chung-Ming Lo1, Woo Kyung Moon2, Chiun-Sheng Huang3, Jeon-Hor Chen4, Min-Chun Yang1, Ruey-Feng Chang5.   

Abstract

Radiologists likely incorrectly classify benign masses as Breast Imaging Reporting and Data System (BI-RADS) category 3. A computer-aided diagnosis (CAD) system was developed in this study as a second viewer to avoid misclassification of carcinomas. Sixty-nine biopsy-proven BI-RADS category 3 masses, including 21 malignant and 48 benign masses, were used to evaluate the CAD system. To improve the texture features, gray-scale variations between images were reduced by transforming pixels into intensity-invariant ranklet coefficients. The textures of the tumor and speckle pixels were extracted from the transformed ranklet images to provide more robust features than in conventional CAD systems. As a result, tumor texture and speckle texture with ranklet transformation achieved significantly better areas under the receiver operating characteristic curve (Az) compared with those without ranklet transformation (Az = 0.83 vs. 0.58 and Az = 0.80 vs. 0.56, p value < 0.05). The improved CAD system can be a second reader to confirm the classification of BI-RADS category 3 masses.
Copyright © 2015 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Breast cancer; Breast imaging and reporting data system; Computer-aided diagnosis; Ranklet; Ultrasound

Mesh:

Year:  2015        PMID: 25843514     DOI: 10.1016/j.ultrasmedbio.2015.03.003

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


  6 in total

1.  Radiomic model for predicting mutations in the isocitrate dehydrogenase gene in glioblastomas.

Authors:  Kevin Li-Chun Hsieh; Cheng-Yu Chen; Chung-Ming Lo
Journal:  Oncotarget       Date:  2017-07-11

2.  Effect of a computer-aided diagnosis system on radiologists' performance in grading gliomas with MRI.

Authors:  Kevin Li-Chun Hsieh; Ruei-Je Tsai; Yu-Chuan Teng; Chung-Ming Lo
Journal:  PLoS One       Date:  2017-02-03       Impact factor: 3.240

3.  Quantitative diagnosis of rotator cuff tears based on sonographic pattern recognition.

Authors:  Ruey-Feng Chang; Chung-Chien Lee; Chung-Ming Lo
Journal:  PLoS One       Date:  2019-02-28       Impact factor: 3.240

4.  LRSCnet: Local Reference Semantic Code learning for breast tumor classification in ultrasound images.

Authors:  Guang Zhang; Yanwei Ren; Xiaoming Xi; Delin Li; Jie Guo; Xiaofeng Li; Cuihuan Tian; Zunyi Xu
Journal:  Biomed Eng Online       Date:  2021-12-17       Impact factor: 2.819

5.  Radiomics of US texture features in differential diagnosis between triple-negative breast cancer and fibroadenoma.

Authors:  Si Eun Lee; Kyunghwa Han; Jin Young Kwak; Eunjung Lee; Eun-Kyung Kim
Journal:  Sci Rep       Date:  2018-09-10       Impact factor: 4.379

6.  Computer-aided diagnosis of isocitrate dehydrogenase genotypes in glioblastomas from radiomic patterns.

Authors:  Chung-Ming Lo; Rui-Cian Weng; Sho-Jen Cheng; Hung-Jung Wang; Kevin Li-Chun Hsieh
Journal:  Medicine (Baltimore)       Date:  2020-02       Impact factor: 1.817

  6 in total

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