Sequential images, which composed of a stack of 2D static images in order arranged one by one, contain not only information that static images carry, but also information of motions hidden among frames, the latter appearing to be more important. To detect this information, optical flow detecting technique, which is a well-known method that applies computer vision theory to detection of dynamic information, analyzes the velocity field of each pixel in images from the viewpoint of statistics [1Liu X, Wende S, Xianhong S. Motion analysis of echocardiographic sequences: state of the art[J] Int J Biomed Eng 2003; 1: 6-11., 2Anna W, Silin X, Yue Y, et al. Medical Image Registration Method Based on Improved Optical Flow Model J Image Graphics 2010; 2: 153-8.]. According to our experience working on contour-based optical flow detecting [3Qiang Lin. A Detecting Method for contour-Based Optical Flow Field of Heart in Ultra- sound B-Scan Images Acta Electronics Sinica 1996; 24(4): 122-5.], it always suffers from many difficulties on mathematic analysis when it is used to solve the problem on deformable objects and non-continuous view angles. Furthermore, there exist many statistic, conjectural and uncertain problems as far as specific understanding of velocity is concerned. Therefore many new detecting methods on Echocardiography Sequences [4Sun Zheng. Estimation of Cardiac Motion Based on Non-rigid Motion Theory Opto-Electronic Eng 2008; 9: 113-7.-8Song M, Haralick RM, Sheehan FH. Ultrasound imaging simulation and echocardi ographic image synthesis. Proceedings [J] Int Conference Image Processing 2000; 3: 420-3.] have been developed. In this study, we have searched for a new method to detect dynamic information from sequential images, which can rebuild gray (position) ~time function (waveform) on direction lines to detect dynamic information on moving views, moving objects, and moving characteristic points in the images.

Rebuilding a gray (position)~time function (waveform) on an arbitrary direction line is to reposition the recognized gray level of views, objects or characteristic points on that direction line and further to confirm its position by capturing the white dots on the line. Because these direction lines are artificial, the positions of those white dots can be confirmed. On the other hand, views and objects in the same frame of sequential images are relatively still, leading the white dots can be confirmed uniquely, while altering with the shifting frames of sequential images. Accordingly, with the certainty of the time interval between two neighboring frames, these white dots, which move originally on the direction lines, are extended with the time. Consequently the gray (position) ~ time waveform (function) on direction lines are obtained. Moving equation on direction lines are obtained when these dots are confirmed as moving objects. From the point of kinematics, the most important moving basic information data can be obtained. Therefore we can mine data in depth and find correlative new dynamic information with the combination of kinematics and dynamics.

The movements of viewing angles, objects or characteristic points of practical images, however, are very complex. Therefore it is impossible that they simply move back and forth on a single direction line in most cases. As a result, the setting of direction lines of sequential images in a practical application should be a combination of many approaches so as to detect practical information and to engage the tracked target points with the combination of the settings of direction lines and certain algorithms. In this paper, two methods are developed to solve this problem. One is to catch the bright points in the ECG a form of single directional stepping translation with frames. Another is to obtain a non-functional motion model that is in turn applied on the direction line to capture functional moving points. Both methods are employed in practice [9Liqin H, Qiang L. Movement track extracting in omnidirectional M-mode echocardiographic image In: Proceedings of the First International Symposium on Test Automation and Instrumentation. SEP13-16; Beijing; 2006; pp. 538-42., 10Qiang L, Wenji W. An omnidirectional M-mode echocardiography system and its clinical application Computerized Medical Imaging Graphics 2006; 30: 333-8.]. According to the moving situations of image objects, more direction lines can be set, leading to more data and new accurate dynamic information being mined and detected.

In this paper, a method has been developed, which combines with certain algorithms, which focuses on the tracking of moving objects to set the direction lines, and is based on the movement of moving objects with the help of sequential images, composed of static images.

As discussed above, direction lines are used to track moving objects, so the optimal direction line is just the trace of a moving object being tracked. However, finding a direction line from images is difficult if not possible. To this end, what we can do is to set artificially some controllable and positionable direction lines which can capture the predefined view, objects and characteristic points. And then we can combine with some related algorithms to set direction lines to obtain a moving trace of the engaged objects. That is gray (position) ~time function, which first-order differential is the velocity of movement, and second-order is its acceleration. Thus, we can mine various moving information.

The method used to set direction lines can be classified into two kinds. The first is the setting of fixed direction lines and the second is the setting of direction line variables within frames. The details are given below.

Omnidirectional gray ~ time echocardiography waveform, or called Omnidirectional M-mode Echocardiography, is the most typical example of the setting of fixed arbitrary number direction line, and is a fruitful method in our research. According to the rebuilding principle of Omnidirectional gray ~ time waveform, if the direction of a sample line selected is the same as the actual motion direction, the gray intensity data of the pixels on them (e.g. B) can generate gray ~ time waveform combined with corresponding temporal relationships. As shown in Fig. (4), the gray ~ time waveform of three lines is rebuilt from long and short axis echocardiography respectively and represents motion waveform of the wall of ventricle structure, which is because these gray values can stand for the boundary of cardiac structure in ultrasound images. Furthermore, all these resulted M-mode echocardiography are synchronised in time, which reflects the movements of different parts in different cardiac structures.

As for Omnidirectional gray ~ time waveform detected from echocardiography sequential images, we can select arbitrary direction of any parts in any structures to set sample line (direction line), which can be used by to follow the motion tracks of this part. Thus, sample lines are not confined by the direction of ultrasound wave-line any longer. Consequently a substantial improvement in detection accuracy is obtained. Moreover, we can compare the motion parameter of every echocardiography and their temporal-phase relationships, because any arbitrary number sample line whose waveform is synchronous in time can be selected. As a result, the idea of Omnidirectional M-mode Echocardiography Waveform System has been patented by the National Invent Patent of China (No. ZL 98 125713.5).

The most extinctive difference between our Omnidirectional gray ~ time waveform system and Anatomy M-mode Echocardiography [19Yang Hu, Wei Guo. Clinical Application of M-mode Echocardiography Adv Cardiovascular Dis 2008; 29(5): 716-8.] is that we have obtained synchronous Omnidirectional gray ~ time waveform group on any parts, any direction and arbitrary columns (corresponding to arbitrary sample lines) based on dynamic information detected from sequential images with synchronous ECG, whereas. The anatomical M-mode echocardiography shows only two or three images simultaneously even when they come from a different cardiac period.

Compare with Tissue Doppler echocardiography [20Daowen Zhen, Jianping Bin, Shengxiong N, et al. Quantitative evaluation of left ventricular diastolic function by doppler tissue imaging China Journal of Modern Medicine 2008; 18(15): 2216-.], our system has some characteristics as specified in the follows [21Qiang Lin, Jianghong S. A dynamic information of echocardiography—omnidirectional m -mode echocardiography Chinese J Scientific Instrument 2005; 26(4): 437-0.]:

• the accelerator comes from differential coefficient to athletic function directly not from frequency excursion indirectly.
• the orientation can changes freely.
• Groups of arbitrary direction lines in any part of any structure can be set and presented corresponding to Omnidirectional echocardiography images with synchronous ECG so as to be compatible with waveforms and temporal phases.

The dynamic analysis here we made in sequential images is similar to amplifier analysis made for several object units in static images. One example is that an image of streets in a static whole scene may only contain lines. However, when further amplified, these lines can display cars, shops, or other details in these streets. Under the similar pattern, the sequential images show the “whole scene” of every cardiac structure from its full view. It can be implemented by the way of Omnidirectional gray ~ time wave- form detecting method to acquire detailed knowledge of the motion status, namely dynamic details of any parts in any structures (including motion waveform, amplitude, velocity, and accelerate, etc. when the point’s movement being along with the time). The wave- form, which we obtained, is the motion trace or motion equation of the detecting part in cardiac structure along with time. On the base of it, one-order timing of differential of Omnidirectional gray ~ time wave- form is the velocity dsdt=v→ of detecting movement of cardiac structure and two-order is acceleration (d2sdt2=a→). As follows Fig. (7).

In summary, it is the most important to obtain new dynamic information in the hemodynamics study. So we implement the data mining of new dynamic information from every cardiac structure from sequential images. We have applied the method to the analysis of cardiac B- scan/color scan (or echocardiography) sequential images. In fact, this motion analysis method on sequential images can also be employed to many other medical sequential images such as X-ray, coronary-contrast, etc. Consequently, this method for motion analysis can also be improved and developed further to fit the characteristics of each imaging modality.

With the advanced development of science and technology, the method detailed in the paper and used to analyze dynamic information is becoming much profounder. The main methods currently used can be classified as a viewpoint-based approach, which is employed on sequential images, focusing on dynamic objects and on movement. While based on sequential images, the method proposed in this paper constitutes all its syntaxes, such as the setting of the direction lines and the algorithm combined, for moving objects principally. So it has some characteristics such as understanding motion details of each cardiac structure, confirming moving information data, and correcting detecting results. To this end, the prior knowledge bases are required in order to set direction lines easier to analyze the motion information.

At present, it is required that all pixels are relatively stationary in a single sequential image. The time interval between two neighboring frames is 1/25 second in the PAL system and1/30 second in the NTSC system, which is a very long time for usual objects. So, if the time of frame interval is reduced farther, the dynamic information detection of higher-speed movement can be obtained with this proposed method.

In addition, the direction lines used above are all straight lines. In the future, if they are modified into controllable, positionable, and non-linear direction lines, it will be easier to track more complex movement leading to the improvement of this approach further.