Breast tumor classification using different features of quantitative ultrasound parametric images

2019 ◽  
Vol 14 (4) ◽  
pp. 623-633 ◽  
Author(s):  
Soa-Min Hsu ◽  
Wen-Hung Kuo ◽  
Fang-Chuan Kuo ◽  
Yin-Yin Liao
Keyword(s):  
Quantitative Ultrasound ◽  
Breast Tumor ◽  
Tumor Classification ◽  
Parametric Images ◽  
Breast Tumor Classification

scholarly journals Statistical Model Based Breast Tumor Classification in Contourlet Transform Domain

2021 ◽  
pp. 21-26
Author(s):  
Shahriar Mahmud Kabir ◽  
Md Sayed Tanveer ◽  
ASM Shihavuddin ◽  
Mohammed Imamul Hassan Bhuiyan
Keyword(s):  
Statistical Model ◽  
Breast Tumor ◽  
Breast Tumors ◽  
Tumor Classification ◽  
Classification Model ◽  
Classification Task ◽  
Gaussian Kernel ◽  
Parametric Images ◽  
Model Based ◽  
Breast Tumor Classification

Determination of breast tumors from B-Mode Ultrasound (US) image is a perplexing one. Researches employing statistical modeling such as Nakagami, Normal Inverse Gaussian (NIG) distributed parametric images in this classification task have already explored but experimentation of those statistical models on contourlet transformed coefficient image in breast tumor classification task has not reported yet. The proposed method is established by considering 250 clinical cases from a publicly available database. In this database each clinical case exists as *.bmp format. In the preprocessing step firstly, the ultrasound B-Mode image is binarized to detect the lesion contour. Then contourlet transformation is employed. These contourlet sub band coefficients are shown to be modeled effectively by Nakagami and NIG distributions. These Nakagami and NIG parametric images are obtained by estimating the parameters of those prior statistical distributions locally. Few shape and statistical features are chosen according to their effectiveness on those parametric images. The benign and malignant breast tumors are classified utilizing these features with different classifiers such as the support vector machine, k-nearest neighbors, fitted binary classification decision tree, binary Gaussian kernel classification model, linear classification models for binary learning with high-dimensional etc. It is observed that classification performance of NIG statistical model based parametric version of contourlet coefficient images gained better accuracy than those of Nakagami statistical model. GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 7, Dec 2020 P 21-26

A Radiomics Approach With CNN for Shear-Wave Elastography Breast Tumor Classification

2018 ◽  
Vol 65 (9) ◽  
pp. 1935-1942 ◽  
Author(s):  
Yongjin Zhou ◽  
Jingxu Xu ◽  
Qiegen Liu ◽  
Cheng Li ◽  
Zaiyi Liu ◽  
...  
Keyword(s):  
Shear Wave ◽  
Breast Tumor ◽  
Shear Wave Elastography ◽  
Tumor Classification ◽  
Breast Tumor Classification

Breast Tumor Classification of Ultrasound Images Using a Reversible Round-Off Nonrecursive 1-D Discrete Periodic Wavelet Transform

2009 ◽  
Vol 56 (3) ◽  
pp. 880-884 ◽  
Author(s):  
Hsieh-Wei Lee ◽  
Bin-Da Liu ◽  
King-Chu Hung ◽  
Sheau-Fang Lei ◽  
Chin-Feng Tsai ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Breast Tumor ◽  
Tumor Classification ◽  
Ultrasound Images ◽  
Periodic Wavelet ◽  
Breast Tumor Classification

Analysis of Elastographic and B-mode Features at Sonoelastography for Breast Tumor Classification

2009 ◽  
Vol 35 (11) ◽  
pp. 1794-1802 ◽  
Author(s):  
Woo Kyung Moon ◽  
Chiun-Sheng Huang ◽  
Wei-Chih Shen ◽  
Etsuo Takada ◽  
Ruey-Feng Chang ◽  
...  
Keyword(s):  
Breast Tumor ◽  
Tumor Classification ◽  
Breast Tumor Classification
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