Here in this paper a combined method of pixel based and region based mass detection is proposed. In the first step, the
background and pectoral muscle are filtered from mammography images and the image contrast is enhanced using an
adaptive density weighted approach. Then, in a coarse level, suspected regions are extracted based on mathematical
morphology and adaptive thresholding methods. Finally, to reduce the false positives produced in the coarse stage, a
useful feature vector based on ranklet transform is obtained and fed into a support vector machine classifier to detect
masses. MIAS (Mammographic Image Analysis Society) and Imam Hospital databases were used to evaluate the
performance of the algorithm. The sensitivity and specificity of the proposed method are 74% and 91% respectively. The
proposed algorithm shows a high degree of robustness in detecting masses of different shapes.
Recently Strain and strain rate imaging have proved their superiority with respect to classical motion estimation
methods in myocardial evaluation as a novel technique for quantitative analysis of myocardial function.
Here in this paper, we propose a novel strain rate imaging algorithm using a new optical flow technique which is more
rapid and accurate than the previous correlation-based methods. The new method presumes a spatiotemporal constancy
of intensity and Magnitude of the image. Moreover the method makes use of the spline moment in a multiresolution
approach. Moreover cardiac central point is obtained using a combination of center of mass and endocardial tracking.
It is proved that the proposed method helps overcome the intensity variations of ultrasound texture while preserving
the ability of motion estimation technique for different motions and orientations. Evaluation is performed on simulated,
phantom (a contractile rubber balloon) and real sequences and proves that this technique is more accurate and faster
than the previous methods.