Literature DB >> 21626954

Classification of scattering media within benign and malignant breast tumors based on ultrasound texture-feature-based and Nakagami-parameter images.

Yin-Yin Liao1, Po-Hsiang Tsui, Chia-Hui Li, King-Jen Chang, Wen-Hung Kuo, Chien-Cheng Chang, Chih-Kuang Yeh.   

Abstract

PURPOSE: Benign and malignant tumors can be classified by using texture analysis of the ultrasound B-scan image to describe the variation in the echogenicity of scatterers. The recently proposed ultrasonic Nakagami parametric image has also been used to detect the concentrations and arrangements of scatterers for tumor characterization applications. B-scan-based texture analysis and the Nakagami parametric image are functionally complementary in ultrasonic tissue characterizations and this study aimed to combine these methods in order to improve the ability to characterize breast tumors.
METHODS: To validate this concept, radio-frequency data obtained from 130 clinical cases were used to construct the texture-feature parametric image and the Nakagami parametric image. Four texture-feature parameters based on a gray-level co-occurrence matrix (homogeneity, contrast, energy, and variance) and the Nakagami parameters of the benign and malignant tumors were calculated. The usefulness of an individual parameter was determined and scatter graphs indicated the relationship between two selected texture-feature parameters. Fisher's linear discriminant analysis was used to combine the selected texture-feature parameters with the Nakagami parameter. The performance in classifying tumors was evaluated based on the receiver operating characteristic curve.
RESULTS: The results indicated that there is a trade-off between sensitivity and specificity when using an individual texture-feature parameter or when combining two such correlated parameters to discriminate benign and malignant cases. However, the best performance was obtained when combining selected texture-feature parameters with the Nakagami parameter.
CONCLUSIONS: The study findings suggest that combining B-scan-based texture analysis and the Nakagami parametric image could improve the ability to classify benign and malignant breast tumors.

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Year:  2011        PMID: 21626954     DOI: 10.1118/1.3566064

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


  21 in total

1.  Ultrasound texture-based CAD system for detecting neuromuscular diseases.

Authors:  Tim König; Johannes Steffen; Marko Rak; Grit Neumann; Ludwig von Rohden; Klaus D Tönnies
Journal:  Int J Comput Assist Radiol Surg       Date:  2014-12-02       Impact factor: 2.924

2.  Clinical study of a noninvasive multimodal sono-contrast induced spectroscopy system for breast cancer diagnosis.

Authors:  K Yan; Y Yu; E Tinney; R Baraldi; L Liao
Journal:  Med Phys       Date:  2012-03       Impact factor: 4.071

3.  Quantitative ultrasound characterization of therapy response in prostate cancer in vivo.

Authors:  Deepa Sharma; Laurentius Oscar Osapoetra; Mateusz Faltyn; Natalie Ngoc Anh Do; Anoja Giles; Martin Stanisz; Lakshmanan Sannachi; Gregory J Czarnota
Journal:  Am J Transl Res       Date:  2021-05-15       Impact factor: 4.060

4.  Conventional frequency ultrasonic biomarkers of cancer treatment response in vivo.

Authors:  Ali Sadeghi-Naini; Omar Falou; Hadi Tadayyon; Azza Al-Mahrouki; William Tran; Naum Papanicolau; Michael C Kolios; Gregory J Czarnota
Journal:  Transl Oncol       Date:  2013-06-01       Impact factor: 4.243

5.  Analysis of Coherent and Diffuse Scattering Using a Reference Phantom.

Authors:  Ivan M Rosado-Mendez; Lindsey C Drehfal; James A Zagzebski; Timothy J Hall
Journal:  IEEE Trans Ultrason Ferroelectr Freq Control       Date:  2016-03-25       Impact factor: 2.725

6.  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

Review 7.  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

8.  Noninvasive characterization of locally advanced breast cancer using textural analysis of quantitative ultrasound parametric images.

Authors:  Hadi Tadayyon; Ali Sadeghi-Naini; Gregory J Czarnota
Journal:  Transl Oncol       Date:  2014-12       Impact factor: 4.243

9.  Robust phase-based texture descriptor for classification of breast ultrasound images.

Authors:  Lingyun Cai; Xin Wang; Yuanyuan Wang; Yi Guo; Jinhua Yu; Yi Wang
Journal:  Biomed Eng Online       Date:  2015-03-24       Impact factor: 2.819

10.  Early prediction of therapy responses and outcomes in breast cancer patients using quantitative ultrasound spectral texture.

Authors:  Ali Sadeghi-Naini; Lakshmanan Sannachi; Kathleen Pritchard; Maureen Trudeau; Sonal Gandhi; Frances C Wright; Judit Zubovits; Martin J Yaffe; Michael C Kolios; Gregory J Czarnota
Journal:  Oncotarget       Date:  2014-06-15
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