Digital Image Stabilisation with Sub-Image Phase Correlation Based Global Motion Estimation

Digital Image Stabilisation with Sub-Image Phase Correlation Based Global Motion Estimation

Global motion is estimated from the local motions of four sub images each of which is detected using phase correlation based motion estimation. The global motion vector is decided according to the peak values of sub-image phase correlation surfaces, instead of impartial median filtering. The peak values of sub-image phase correlation surfaces reveal reliable local motion vectors, as poorly matched sub images result in considerably lower peaks in the phase correlation surface due to spread.

Digital image stabilization systems aim to remove irregular global motion effects from an image sequence in order to obtain a compensated sequence that displays smooth camera movements only

The digital image stabilization system can be divided into two parts: the global motion estimation systems and the motion correction system. The motion estimation system is responsible for the estimation of interframe global motion vectors, which are forwarded to the motion correction system. The motion correction system accomplishes the stabilization of the image sequence according to the global motion model or objective

The global motion vector of the image frame can then be decided based on the peak amplitude values of local motion vectors. Three different approaches can be utilized to evaluate the global motion:

1. The local motion vector with the largest peak amplitude

can be assigned as the global interframe motion vector.

2. The two highest peak amplitude values can be detected and the corresponding two local motion vectors can be averaged to obtain the global interframe motion vector.

If the result of the averaging is not an integer, the result is rounded to the nearest integer towards the motion vector with the highest peak amplitude.

3. All local motion vectors, not previously discarded, can be weighted proportionally to their peak amplitude values and the result can be assigned as the global interframe motion vector.

Image Enhancement based Improved Multi-scale Hessian Matrix for Coronary Angiography

Image Enhancement based Improved Multi-scale Hessian Matrix for Coronary Angiography

 The coronary angiography image is easy to be affected by many factors, such as vascular thickness varied huge, complex background noise, uneven illumination intensity and so on. Many vascular image enhancement methods are propose, such as linear filters , morphology filter, anisotropic diffusion filter and so on. The most common method is vascular enhancement filter based on Hessian matrix introduced in paper.

Multi-scale fusion in the multi-scale vessel enhancement filter is used to solve the vascular characteristics that the size of blood vessel images is different.  Multi-scale Hessian matrix method is mentioned through combining Hessian matrix method with other image enhancement methods.

In this paper, a method of image de-noising and enhancement based on the improved multi-scale Hessian matrix that integrate the multi-scale Hessian matrix with morphological top-hat method for coronary angiography images is proposed.

MORPHOLOGICAL TOP-HAT OPERATIONS

Morphological opening operation can be used to smooth the outline of objects, disconnect narrow neck, eliminate thin projections etc

The highlights can be removed by the morphological opening operation, because the area of highlights smaller than the structure element. Background image obtained by the opening operation is subtracted from the original image, the acquired vascular tree have got image enhancement, the process is known as top-hat operation.

MULTI-SCALE HESSIAN MATRIX VESSEL EXTRACTION

Hessian matrix, resulting in a lot of background noise (aperture etc.), and many small tiny blood vessels disappeared at the same time. To be able to solve these problems, an improved method combining multi-scale Hessian matrix with morphological top-hat operation is proposed in this paper.

An improved multi-scale Hessian matrix, combined with morphological top-hat operation for the detection of coronary angiography is presented in the paper.

CHORONARY IMAGE ENHANCEMENT USING DECIMATION-FREE DIRECTIONAL FILTER BANKS

CHORONARY IMAGE ENHANCEMENT USING DECIMATION-FREE

DIRECTIONAL FILTER BANKS

• The detection and enhancement of Coronary arterial trees (CAT’s)in an angiogram image is an important pre-processing task.
• In this paper, they have proposed a decimation-free directional filter bank (DFB) structure.It provides output in the form of directional images as opposed to directional sub-bands provided in previous DFBs.
• Angiograms acquired are low in contrast.Therefore we have to prepare angiogram image before it can given as input to the proposed DFB structure.
• The preparation steps involve removing non-uniform illumination from the image.Then proposed DFB structure outputs directional images.The final enhanced result is constructed on a block-by-block basis by comparing energy of all the directional images and picking one that provides maximum energy. The enhancement that results in the final image is due to the fact that we can separate omni-directional background noise from CAT structure which is
  pre-dominantly a directional feature.

• An angiogram image enhancement algorithm receives an input angiogram image, applies a set of intermediate steps on the input image and finally outputs the enhanced image.

1.   Non-uniform Illumination Correction
   An input angiogram image has a varying illumination pattern that needs to be removed. Although, there are many spatial domain techniques available to get rid of non-uniform illumination structure,we opted for homomorphic filtering to extract non-uniform illumination of the test image
2.   Normalization 
    is a pixel-wise operation. The main purpose of normalization is to get an output image with desirable mean and variance, which facilitates the subsequent processing.
3.    Creation of Directional Images

3.1.      Design of Directional Filters 

• The directional analysis employed in this paper decomposes the spectral region of a given image into wedge-shaped passband regions.
• It is easily shown that these wedge-shaped regions correspond to directional components of an image. 
• The filters related towedge-shaped regions are commonly referred to as fan filters.
• However, in this structure,decimators at each stage are taken out and filters are designedby linear transformation in the frequency domain to get fan filters.
• Furthermore, to avoid ringing artifact in the output, ideal fan filters are avoided by employing non-ideal hour-glass filters using an FIR

                 3.2 Directional Images

• The first step employed in directional images creation is to remove the spatial varying mean term by filtering with a highpass filter.
•  In this paper, rectangularly separable highpass filter was used.

4.   Reconstruction of Enhanced Image

Digital Image Stabilization by Adaptive Block Motion Vectors Filtering

Filippo Vella at el introduced a robust algorithm for video sequences stabilization using Adaptive Block Motion Vectors Filtering. According to them Digital Image Stabilization systems can be subdivided in three modules: motion estimation, detection of unwanted movements and compensation. They used proposed algorithm as motion estimator a module that produces block motion vector. Motion Estimation evaluated on blocks gives a motion vector for each block considered. The advantages of this method same motion estimator of the mpeg encoder can be used and a big set of motion vectors can be evaluated for each frame. The drawback is that not all block motion vectors (BMV) are reliable. This algorithm considered two areas as foreground and background and motion estimation is done in these two areas. For each area of the frame, block motion estimation is done and a number of motion vectors are estimated. From them a global motion vector for foreground and one for background is evaluated. To calculate a Global Motion Vector for the areas a single Motion Vector must be evaluated from the set of BMVs. The most frequent vector in the extract as the Global Motion Vector. The square histogram considers the most frequent motion vector in the frame and it will work better than two linear histograms. There two histograms are built for foreground and background and the maximum of each histogram will give a GMV for each region.

When detecting unwanted movements, they considered about GMV in foreground and background. If GMV for foreground and background are equal, the detected motion is considered as the global motion that affects the frame. Otherwise a decision must be taken whether to stabilize accordingly to the first or the second. To decide whether to consider motion of foreground or background the number of blocks that produce the GMV is considered. If the background area GMV is produced by a higher number of blocks than the foreground area GMV, background is stabilized, otherwise foreground is stabilized.

When unwanted motion of the frame is detected, a motion compensation is done. The AGMV is the vector that accumulates the components of the GMV of each frame and to have a stabilization of the whole sequence the Absolute GMV (AGMV) is used. A movement can be classified as jiggling or panning. Discrimination between jiggling and panning has been considered to avoid to inhibit wanted motions. Filippo Vella at ell solved the problem by maintaining the value of the AGMV constant when panning is detected.

Development of digital substraction angiography

Digital subtraction angiography (DSA) is one of the most important examinations in the

diagnosis and treatment of blood vessels. The radiation dose can be reduced using this examination because the vessels are visualized clearly; however, it is very difficult to apply the DSA technique to the coronary arteries because of the severe motion artifacts caused by cardiac motion and respiration.Myerowitz and his team reported that the coronary arteries are difficult to visualize using intravenous DSA because of their small size, rapid movement, and overlying structures. Therefore, many techniques have been studied to improve DSA image quality.Bentoutou and his team used a template-matching technique and 3D space motion detection for improving the accuracy of registration. A warping technique for the mask frame was used in a report by Meijering and his team with the aim of reducing motion artifacts. These techniques require a relatively long computation time. Therefore, it would be difficult to apply these techniques clinically because most of them do not enable real-time image processing. They propose a simple but effective technique for reducing motion artifacts in DSA. This method will be useful in the clinical environment because It enables real-time processing. In the present study, the average time taken to create DSA images of the coronary artery were 0.03 s per frame, without any special hardware board. In addition, this method can be performed by a commercially available stand-alone PC. Our DSA technique is easily installed into catheterization laboratories in hospitals. The results of subjective and objective evaluation showed that motion artifacts in DSA images were effectively reduced using our method. Because the average standard deviation of the pixel value of DSA images obtained using the new technique was 2.36 less than that of the conventional DSA images, motion artifacts caused by rapid movement were decreased. Therefore, this method will be useful in cardiologists’ decision making, especially for the peripheral blood vessels.