Effect of PDE-based noise removal on GVF-based deformation model on lesion detection in breast phantom x-ray images from Fischer"s fused FFDM and ultrasound (FFDMUS) imaging system

Abstract

It has been recently established that fusion of multi-modalities has led to better diagnostic capability and increased sensitivity and specificity. Fischer has been developing fused full-field digital mammography and ultrasound (FFDMUS) system. In FFDMUS, two sets of acquisitions are performed: 2-D X-ray and 3-D ultrasound. The segmentation of acquired lesions in phantom images is important: (1) to assess the image quality of X-ray and ultrasound images; (2) to register multi-modality images, and (3) to establish an automatic lesion detection methodology to assist the radiologist. In this paper, we studied the effect of PDE-based smoother on the gradient vector flow (GVF)-based active contour model for breast lesion detection. CIRS X-ray phantom images were acquired using FFDMUS, and region of interest (ROI) samples were extracted. PDE-based smoother was implemented to generate noise free images. The GVF-based strategy was then implemented on these noise free samples. Initial contours were set as default, and GVF snake then converged to extract lesion topology. The performance index was calculated by computing the difference between estimated lesion area and ideal lesion area. Our performance index with GVF (without PDE smoothing) yielded an average percentage error of 10.32%, while GVF with PDE yielded an average error of 9.61%, an improvement of 7%. We also optimized our PDE smoother for least GVF error estimation, and to our observation, we found the optimal number of iteration was 140. We also tested our program written in C++ on synthetic datasets.