Classification of Benign and Malignant Breast Tumors in Ultrasound Images Based on Multiple Sonographic and Textural Features

2011 ◽  
Author(s):  
Renjie Liao ◽  
Tao Wan ◽  
Zengchang Qin
Keyword(s):  
Breast Tumors ◽  
Textural Features ◽  
Ultrasound Images ◽  
Malignant Breast

scholarly journals Classification of Benign and Malignant Breast Tumors Using H-Scan Ultrasound Imaging

Diagnostics ◽  
2019 ◽  
Vol 9 (4) ◽  
pp. 182 ◽  
Author(s):  
Yali Ouyang ◽  
Po-Hsiang Tsui ◽  
Shuicai Wu ◽  
Weiwei Wu ◽  
Zhuhuang Zhou
Keyword(s):  
Ultrasound Imaging ◽  
Hermite Polynomials ◽  
Relative Size ◽  
Speckle Noise ◽  
Breast Tumors ◽  
New Method ◽  
Ultrasound Images ◽  
Ultrasound Technique ◽  
Malignant Breast

Breast cancer is one of the most common cancers among women worldwide. Ultrasound imaging has been widely used in the detection and diagnosis of breast tumors. However, due to factors such as limited spatial resolution and speckle noise, classification of benign and malignant breast tumors using conventional B-mode ultrasound still remains a challenging task. H-scan is a new ultrasound technique that images the relative size of acoustic scatterers. However, the feasibility of H-scan ultrasound imaging in the classification of benign and malignant breast tumors has not been investigated. In this paper, we proposed a new method based on H-scan ultrasound imaging to classify benign and malignant breast tumors. Backscattered ultrasound radiofrequency signals of 100 breast tumors were used (48 benign and 52 malignant cases). H-scan ultrasound images were constructed with the radiofrequency signals by matched filtering using Gaussian-weighted Hermite polynomials. Experimental results showed that benign breast tumors had more red components, while malignant breast tumors had more blue components in H-scan ultrasound images. There were significant differences between the RGB channels of H-scan ultrasound images of benign and malignant breast tumors. We conclude H-scan ultrasound imaging can be used as a new method for classifying benign and malignant breast tumors.

scholarly journals Classification of Benign and Malignant Breast Tumors in Ultrasound Images with Posterior Acoustic Shadowing Using Half-Contour Features

2015 ◽  
Vol 35 (2) ◽  
pp. 178-187 ◽  
Author(s):  
Zhuhuang Zhou ◽  
Shuicai Wu ◽  
King-Jen Chang ◽  
Wei-Ren Chen ◽  
Yung-Sheng Chen ◽  
...  
Keyword(s):  
Breast Tumors ◽  
Ultrasound Images ◽  
Malignant Breast ◽  
Acoustic Shadowing

scholarly journals Classification of benign and malignant breast tumors on the basis of 36 radiographic properties

Cancer ◽  
1973 ◽  
Vol 31 (2) ◽  
pp. 342-352 ◽  
Author(s):  
Laurens V. Ackerman ◽  
Anthony N. Mucciardi ◽  
Earl E. Gose
Keyword(s):  
Breast Tumors ◽  
Malignant Breast

scholarly journals A Benign and Malignant Breast Tumor Classification Method via Efficiently Combining Texture and Morphological Features on Ultrasound Images

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Mengwan Wei ◽  
Yongzhao Du ◽  
Xiuming Wu ◽  
Qichen Su ◽  
Jianqing Zhu ◽  
...  
Keyword(s):  
Breast Tumor ◽  
Texture Features ◽  
Morphological Features ◽  
High Dimensional ◽  
Svm Classifier ◽  
Ultrasound Images ◽  
Malignant Breast Tumor ◽  
Malignant Breast ◽  
Low Dimensional

The classification of benign and malignant based on ultrasound images is of great value because breast cancer is an enormous threat to women’s health worldwide. Although both texture and morphological features are crucial representations of ultrasound breast tumor images, their straightforward combination brings little effect for improving the classification of benign and malignant since high-dimensional texture features are too aggressive so that drown out the effect of low-dimensional morphological features. For that, an efficient texture and morphological feature combing method is proposed to improve the classification of benign and malignant. Firstly, both texture (i.e., local binary patterns (LBP), histogram of oriented gradients (HOG), and gray-level co-occurrence matrixes (GLCM)) and morphological (i.e., shape complexities) features of breast ultrasound images are extracted. Secondly, a support vector machine (SVM) classifier working on texture features is trained, and a naive Bayes (NB) classifier acting on morphological features is designed, in order to exert the discriminative power of texture features and morphological features, respectively. Thirdly, the classification scores of the two classifiers (i.e., SVM and NB) are weighted fused to obtain the final classification result. The low-dimensional nonparameterized NB classifier is effectively control the parameter complexity of the entire classification system combine with the high-dimensional parametric SVM classifier. Consequently, texture and morphological features are efficiently combined. Comprehensive experimental analyses are presented, and the proposed method obtains a 91.11% accuracy, a 94.34% sensitivity, and an 86.49% specificity, which outperforms many related benign and malignant breast tumor classification methods.

Fully automatic and segmentation-robust classification of breast tumors based on local texture analysis of ultrasound images

2010 ◽  
Vol 43 (1) ◽  
pp. 280-298 ◽  
Author(s):  
Bo Liu ◽  
H.D. Cheng ◽  
Jianhua Huang ◽  
Jiawei Tian ◽  
Xianglong Tang ◽  
...  
Keyword(s):  
Texture Analysis ◽  
Breast Tumors ◽  
Ultrasound Images ◽  
Robust Classification ◽  
Local Texture ◽  
Fully Automatic
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