Author/Authors :
Abdolmaleki Parviz نويسنده Department of Biophysics, Faculty of Science, Tarbiat Modares University, P.O. Box: 14115/175 Tehran, I.R. IRAN , Pouladian Majid نويسنده Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University , Nirouei Mahyar نويسنده Department of Medical Radiation Engineering, Science and
Research Branch, Islamic Azad University, Tehran, IR
Iran , Akhlaghpour Shahram نويسنده Pardisnoor Medical Imaging Center, Tehran, IR
Iran
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.
