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Research Article

Enhancement of Angiogram Images Using Pseudo Color Processing

Mohammad A.U. Khan, Rabya Bahadur Khan, Shahid Bilal, Asad Jamil and Mehr Ali Shah

An angiogram is an X-ray image that uses fluoroscopy to take pictures of the blood flow within an artery. Due to the overlap of non-vascular structures, the small vessels with low contrast are hardly visible. Pseudo-color processing is an enhancement technique that accentuates certain features that are essential for a given application but hidden with low contrast otherwise. We applied pseudo color to an angiogram image on the basis of scale. Vessels are colored in such away that they are differentiated well. Further, we have enhanced the images by applying transparent colors to the images.

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Mohammad A.U. Khan, Rabya Bahadur Khan, Shahid Bilal, Asad Jamil and Mehr Ali Shah, 2008. Enhancement of Angiogram Images Using Pseudo Color Processing. Information Technology Journal, 7: 210-214.

DOI: 10.3923/itj.2008.210.214



An angiogram is an X-ray image that uses fluoroscopy to take pictures of the blood flow within an artery. Angiogram images are of great significance in explaining blood vessel problems such as aneurysms, narrowed areas and blockage caused by blood clots or the buildup of fat and calcium deposits. However, the acquired angiogram images are inadequate to explain vessels with low contrast. The contrast problem with an Angiogram image is due to bodily tissues, such as bones, that create shadows on X-ray image. The main limitations on acquired angiogram images are the overlap of non-vascular structures and this explains the fact that small vessels with low contrast are hardly visible (Fig. 1). For facilitating, diagnose arterial disease and angioplasty or bypass surgery it is essential to enhance these angiogram images (Alejandro et al., 1998) to an acceptable level.

Conventionally grayscale domain is used to enhance there angiogram images. Grayscale images are typically composed of shades of gray, varying from black at the weakest intensity to white at the strongest. Human beings can only distinguish a few dozen gray level values (Abidi et al., 2006), therefore, grayscale enhancement of the angiogram image pose an inherit limitation.

On the other hand, human beings can differentiate thousands of colors; so we claim that the use of color for human interpretation could improve the identification of different objects. Pseudo coloring of grayscale images is a typical process used to increase the visual quality of a given image. A pseudo-colored image is derived from a grayscale image by mapping each pixel value to a color according to a table or function.

Fig. 1: An ordinary angiogram image

Pseudo color processing is an enhancement technique that accentuates certain features that are essential for a given application but hidden with low contrast otherwise. However, this whole process is feasible once we can identify some characteristic associated with those hidden features. Then exploiting these characteristic, one can provide pseudo-colors. In present angiogram case, we identify the width of the vessel as our parameter for providing false colors. Therefore, a pre-processing step is adopted to capture various vessels with variety of width. Once captured, the vessels with a range of width are further enhanced by providing them with suitable colors. The resulting angiogram image provides enhanced perceptual range.

One of the results is shown in Fig. 2 which clearly shows that pseudo color processing has been successful in enhancing low contrast vessels.

Fig. 2:
An Angiogram image enhanced using pseudo coloring clearly increases the visual quality of low contrast vessels


Pseudo coloring of grayscale images can significantly improve the ability to detect weak features, structures and patterns in an image by providing image details that would not be noticed otherwise (Troccaz et al.,1997). A better qualitative overview of complex data sets will then assist in identifying regions of interest for more focused quantitative analysis, by making similarly joined areas in the scene more distinguishable (Abidi et al., 2005). The problem in pseudo color processing is assigning a color to a particular gray level intensity. To extract information present in the angiogram images different techniques are followed. Some of them work at a fixed scale and some of them use the non linear combinations of finite differences.

Intensity level slicing: Intensity level slicing is a particular kind of grey level mapping where specific ranges on the input image are mapped to easily seen values in the output image as shown in Fig. 3.

Grey level transformation: Grey level slicing is another way of coloring grey level image. This approach performs three independent transformations on the gray level of any input pixel (Czerwinski et al., 1999). The three results are then fed separately into the red, green and blue channels of a color television monitor. This method produces a composite image, whose color content is modulated by the nature of the transformations on the gray-level values of an image. The results of grey level transformation are in shown in Fig. 4.

One of the major drawbacks associated with intensity slicing is the simple fact that it entirely ignores in the contents present in the image. However, Pseudo color processing is primarily done to enhance certain features in the image.

Fig. 3: Angiogram image using intensity slicing false contouring effect is enhanced

Fig. 4:
An Angiogram image using grey level transformation absorbs low contrast vessels due to blind color transformation

In Grey level transformation the vessels are blindly colored. Hence, the low vessel structures are absorbed. The important features present in angiogram images from medical point of view are vessels. Logically the first step should be to locate these vessels and then color them accordingly to get an enhanced angiogram image.


We applied pseudo color to an angiogram image on the basis of scale. Vessels are colored in such away that they are differentiated well and the visual quality of an angiogram becomes better then the gray scale enhancing technique. While if the angiogram image is colored blindly, then the vessels are not differentiated well. Therefore, some criteria must be defined on the basis of which we give the colors, so that these vessels could be enhanced to better differentiation.

Fig. 5: Proposed method

Fig. 6:
Angiogram image colored using HSI model. The details of low vessel structures are more precise

As by Abidi et al. (2005), they have applied HSI model on X-ray luggage images and found that it is superior as compare to RGB-based coloring technique. Hence, we also claim that if HSI model is efficient in coloring X-ray luggage images, it will also be adequate in coloring angiogram images to locate the small vessels (Fig. 5).

In HSI model each color is represented by hue, saturation and intensity component. Hue is a color attribute that describes a particular wavelength in the spectrum. Saturation is the measure of the degree to which a pure color is diluted by a white light. Intensity represents how little black has been added to the color. The intensity component is decoupled from the color information in the image and the hue and saturation components are intimately related to how humans perceive color. These features make the HSI model suitable for developing image processing algorithms for human interpretation (Fig. 6).

In the approach followed by Alejandro et al. (1998), they have conceived vessel enhancement as a filtering process that searches for geometrical structures. They have analyzed the local behavior of an image L, by its Taylor expansion.

This expansion approximates the structure of an image up to second order. ∇0,s and H0,s are the gradient vector and Hessian matrix of the image computed in x0 at scale s.

Fig. 7: (a) The second order derivative of a Gaussian kernel of the range (-s, s). (b) The second order ellipsoid where λ1

Fig. 8:
(a) image extracted when s = 1, (b) image formed when the value of s = 3. It shows different vessels from the same image for different values of s

By analyzing the second order information, we can say Hessian justifies the perspective of vessel detection. By using the second derivative of scale s (Alejandro et al., 1998) generates a kernel that measures the length and the width in the region (-s, s) Fig. 7 describes the length of the ellipsoid while λ2 describes its width.

Fig. 9:
(a) An original angiogram image, (b) Angiogram image colored using cosine color and HSI model. (c) and (d) angiogram image colored using transparent colors in addition to HSI model. The results retrieved in (c) and (d) are more detailed and precise as compared to (a) and (b)

Eigen-value analysis is used to extract the principle directions that can be used to decompose the local second order structure of the image (Alejandro et al., 1998). On various s values we get different images within vessel that matches the value of s (Fig. 8).

These vessels are then colored using the following formula:



For further mathematical detail see (Alejandro et al., 1998). But there are some overlapping vessels inside other vessels in Angiogram images. The information about these vessels might be lost even in HSI model (Czerwinski et al., 1999). Therefore, some other mechanism must be applied with HSI Model in order to retrieve all information. To extract hidden information we have three different method Cosine color maps, Exponential color maps and transparent color maps (Rafael et al., 2004). Amongst all these the transparent color maps produce better results. All these results are discussed in the next section. We apply transparent colors in addition to HSI. The results retrieved from the combination of HSI and Transparent colors were more effective and detailed as shown in Fig. 9.


In present study, basically we extract the vessel structure and then on the basis of different scale we apply different color transforms, to the angiogram images which will further enhance the angiogram image. Further we use the transparent colors for best visualization of the overlapping vessel structures. In the future we will expand our database by applying this algorithm on furthermore images to make our model more robust.

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Abidi, B.R., Y. Zheng, V.A. Gribok and M.A. Abidi, 2006. Improving weapon detection in single energy x-ray images through pseudo coloring. IEEE Trans. Syst. Man Cyber., 36: 784-796.
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Rafael, C.G., R.E. Woods and S.L. Eddins, 2004. Digital image processing. pp: 194-240.

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