The purpose of this study is to propose an advanced phase-based optical flow method with improved tracking accuracy for motion flow. The proposed method is mainly based on adaptive bilateral filtering (ABF) and Gabor based spatial filtering. ABF aims to preserve the maximum boundary information of the original image, while the spatial filtering aims to accurately compute the local variations. Our method tracks the optical flow in three stages. Firstly, the input images are filtered by using ABF and a spatial filter to remove noises and to preserve the maximum contour information. The component velocities are then computed based on the phase gradient of each pixel. Secondly, irregular pixels are eliminated, if the phase differences are not linear over the image frames. Lastly, the entire velocity is derived by integrating the component velocities of each pixel. In order to evaluate the tracking accuracy of the proposed method, we have examined its performance for synthetic and realistic images for which the ground truth data were known. As a result, it was observed that the proposed technique offers higher accuracy than the existing optical flow methods.

Microcalcification detection is an important part of early breast cancer detection. In this paper, we propose a microcalcification detection algorithm using adaptive contrast enhancement in a mammography CAD (computer-aided diagnosis) system. The proposed microcalcification detection algorithm includes two parts. One is adaptive contrast enhancement in which the enhancement filtering parameters are determined based on noise characteristics of the mammogram. The other is a multi-stage microcalcification detection. The results show that the proposed microcalcification detection algorithm is much more robust against fluctuating noisy environments.