A statistical framework for breast tumor classification from ultrasonic images

Segmentation and classification of ultrasonic breast images is extremely critical for medical diagnosis. Over the last years, various techniques have already been presented for this objective. In this paper, a proposed framework is presented to segment a given ultrasonic image with breast tumor and classify the tumor as being benign or malignant. The proposed framework depends on an active contour segmentation model to determine the tumor region, and then extract it from the ultrasonic image. After that, the Discrete Wavelet Transform (DWT) is used to extract features from the segmented images. Then, the dimensions of the resulting features are reduced by applying feature reduction approaches, namely, the Principal Component Analysis (PCA), the Linear Discriminant Analysis (LDA) and both of them together. The obtained features are submitted to a statistical classifier and the strategy of voting is used to classify the tumor. In the simulation work, 160 benign and malignant breast tumor images collected from Sirindhorn International Institute of Technology (SIIT) website are used. The average processing time for a 256 × 256 image on a laptop with Core i5, 2.3 GHz processor and 8GB RAM is 1.8 s. From the simulation results, it is found that the utilization of the PCA approach provides the best accuracy of 99.23% among the three feature reduction approaches applied. Finally, the proposed framework is compared with the Support Vector Machine (SVM) classification to evaluate its performance in terms of accuracy, sensitivity, precision, and specificity. It is noticed that the proposed framework is efficient and rapid, and it can be applied for ultrasonic breast image segmentation and classification, and thus it can assist the specialists to segment and decide whether a tumor is benign or malignant.