Edge-detection is all the time a major problem in the compute rearly vision, and it plays an important role in image processing. This pap er r eviews classical and new methods of edge-detection and discusses its applicati on in medical image processing.

Study of mechanism of medicine actions, by quantitative analysis of cultured cardiac myocyte, is one of the cutting edge researches in myocyte dynamics and molecular biology. The characteristics of cardiac myocyte auto-beating without external stimulation make the research sense. Research of the morphology and cardiac myocyte motion using image analysis can reveal the fundamental mechanism of medical actions, increase the accuracy of medicine filtering, and design the optimal formula of medicine for best medical treatments. A system of hardware and software has been built with complete sets of functions including living cardiac myocyte image acquisition, image processing, motion image analysis, and image recognition. In this paper, theories and approaches are introduced for analysis of living cardiac myocyte motion images and implementing quantitative analysis of cardiac myocyte features. A motion estimation algorithm is used for motion vector detection of particular points and amplitude and frequency detection of a cardiac myocyte. Beatings of cardiac myocytes are sometimes very small. In such case, it is difficult to detect the motion vectors from the particular points in a time sequence of images. For this reason, an image correlation theory is employed to detect the beating frequencies. Active contour algorithm in terms of energy function is proposed to approximate the boundary and detect the changes of edge of myocyte.
Algorithms ; Animals ; Animals, Newborn ; Cell Movement ; Cells, Cultured ; Female ; Image Interpretation, Computer-Assisted ; Image Processing, Computer-Assisted ; Myocardial Contraction ; Myocytes, Cardiac ; cytology ; Pregnancy ; Rats ; Rats, Wistar