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

Abstract

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.

Influence of the Computer-Aided Decision Support System Design on Ultrasound-Based Breast Cancer Classification

“…However, images are more than pictures, and they can be used to extract multiple powerful features that are not visible to the naked eye [ 24 , 25 ]. This became possible with the introduction of radiomics analysis, which tries to exploit the full information content of medical images for cancer diagnosis [ 26 , 27 , 28 ]. For this purpose, radiomics analysis can provide new parameters reflecting important characteristics of the tumor microenvironment.…”

Dual-Intended Deep Learning Model for Breast Cancer Diagnosis in Ultrasound Imaging

“…Radiomics have demonstrated a high potential to discover the characteristics of diseases in medical imaging that cannot be seen by the naked eye [ 18 , 19 , 20 , 21 , 22 , 23 ]. These imaging throughputs are typically a useful complement to clinical and biological covariates to enhance diagnostic capacity.…”

“…Important parameters for the diagnosis of breast cancer are related to tumor morphological information, which is often checked by physicians, and baseline characteristic features verified by CAD [ 13 , 14 ]. Imaging throughputs, or radiomics , decode information on the characteristics that were not visible to the naked/untrained eyes and can have significant effects on cancer diagnosis/prognosis [ 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 ].…”

Ultrasound-based radiomics: current status, challenges and future opportunities

“…Radiomics features are extracted from the training sets and verified in the independent validation sets. The extracted features mainly include shape features, first-order gray histogram features, second-order and higher-order texture features, and other features based on filtering and transformation [21]. Some researchers believe that high-order features and second-order texture features can reflect internal tumor heterogeneity to a certain extent, which provides helpful information for improving the diagnostic efficiency of tumors and predicting a treatment response [22].…”