Hamid Behnam1, Fahimeh Sadat Zakeri2, Nasrin Ahmadinejad3. 1. Electrical Engineering Department, Iran University of Science and Technology (IUST), Narmak, Tehran, Iran. behnam@iust.ac.ir. 2. Electrical Engineering Department, Iran University of Science and Technology (IUST), Narmak, Tehran, Iran. 3. Department of Radiology, Imam Hospital, Tehran University of Medical Sciences, Tehran, Iran.
Abstract
PURPOSE: To evaluate the efficiency of novel shape features for classification of benign and malignant sonographic breast masses. METHODS: Mass regions were extracted from the region of interest (ROI) sub-image by applying a segmentation algorithm based on the level set method. Six features (difference area with five features of mass pixel number viewed at different angles) were then extracted for further classification. A multilayered perceptron neural network (MLP) classifier was used to classify breast mass. The leave-one-case-out procedure was used on a database of 81 pathologically proved breast sonographic images of patients (47 benign cases and 34 malignant cases) to evaluate our method. RESULTS: The classification results showed overall accuracy was 93.83%, sensitivity 91.18%, specificity 95.74%, positive predictive value 93.94%, and negative predictive value 93.75%. CONCLUSION: The experimental results showed that this diagnostic system with the features proposed can improve the positive rate of biopsies, provide a second opinion for physicians, and be used as a useful tool for mass classification.
PURPOSE: To evaluate the efficiency of novel shape features for classification of benign and malignant sonographic breast masses. METHODS: Mass regions were extracted from the region of interest (ROI) sub-image by applying a segmentation algorithm based on the level set method. Six features (difference area with five features of mass pixel number viewed at different angles) were then extracted for further classification. A multilayered perceptron neural network (MLP) classifier was used to classify breast mass. The leave-one-case-out procedure was used on a database of 81 pathologically proved breast sonographic images of patients (47 benign cases and 34 malignant cases) to evaluate our method. RESULTS: The classification results showed overall accuracy was 93.83%, sensitivity 91.18%, specificity 95.74%, positive predictive value 93.94%, and negative predictive value 93.75%. CONCLUSION: The experimental results showed that this diagnostic system with the features proposed can improve the positive rate of biopsies, provide a second opinion for physicians, and be used as a useful tool for mass classification.
Entities:
Keywords:
Breast mass; Classification; Shape feature; Sonography