Feature Extraction and Classification of Breast Tumors Using Chaos and Fractal Analysis on Dynamic Magnetic Resonance Imaging

Abstract

Background: Breast cancer is one of the leading causes of death in the world. Early diagnosis of breast cancer can reduce the rate of mortality of this type of cancer. An increasing number of reports have confirmed the excellent sensitivity of dynamic contrast enhanced magnetic resonance imaging (DCE-MRI). Despite the excellent sensitivity of DCE-MRI, there is still some difficulty in the prediction of malignancy in these patients because of the lack of the optimum guidelines for the interpretation of breast magnetic resonance (MR) studies as well as the reported overlap in T1 and T2 relaxation times. Objectives: The aim of this study was to extract significant features from MRI images of the breast using chaos, fractal and time series analysis and to classify breast tumors into malignant and benign using the calculated features. Methods: In this research, we utilized the chaos theory and fractal analysis in the interpretation of breast tumors on DCE-MRI. This cross-sectional study was done at Pardisnoor imaging center during years 2015 and 2016 in Iran. Our sample size was 18 mass lesions, which were randomly selected among patients with BIRAD 3 and BIRAD 4 classification by the expert radiologist. The analysis was performed after injecting patients with a contrast agent and 18 mass lesions were extracted from dynamic MR images. After pre-processing and segmentation stages, time series of the tumor was generated for each MR image. The largest Lyapunov exponent (LLE) and statistical parameters for each mass lesion were extracted. Also, fractal analysis was utilized to extract meaningful features from mass contour to evaluate the roughness of tumor margin. Results: We found that the value of LLE in malignant tumors was higher than benign mass lesions. The obtained results demonstrated that chaos and time series features, such as LLE and non-circularity of the tumor, were the best parameters among all features. Conclusions: The extracted descriptors can improve the performance of classifiers in the early detection of breast cancer. Significant shape features can also help radiologists increase diagnosis accuracy in classification of suspicious breast masses.