Abstract— The delineation of blood vessels on medical images is an essential step in the computer aided diagnosis of the vessels’ diseases. In this paper, it will be examined four different image processing methods in order to detect the vascular map in X-ray cerebral or cardiac angiograms.

Keywords—image processing, angiography, temporal imageseries.

INTRODUCTION

Angiography is a gold standard technique used to evaluate abnormalities occurred in blood vessels, providing images with good spatial and temporal resolution.

On digital images, the image processing techniques can be easily applied. The simplest methods are the thresholding and the histogram operations which imply human intervention. More automatic approaches are difficult to be implemented due to different vessels’ scales and organs shadows. These noises and artifacts can determine incorrect contours and discontinuities. In general, segmentation step is dependent on the image type and its clinical application.

A good review about the segmentation and tracking techniques of vessels is presented in [1] for certain medical images types, vessels’ type, etc.

There are four different image processing methods to obtain the vessel enhancement in X-ray angiograms. The well-known digital subtraction angiograghy in its linear and logarithmic version, and more complicated nonlinear filtering: Gabor and Frangi vesselness which includes histogram operations, Hessian-based method for delineating the vascular map and thresholding.

The Gabor filtering was successfully applied for the detection of cerebral vessels [2-3], retinal vessels [4-8] and cardiac vessels [9].

I. MEDICAL IMAGE ACQUISITION

X-ray angiography is based on the projectionalradiography and it is a clinical method for assessment the geometric and filling properties of the blood vessels.

The vessels are soft tissues, and in order to make them radio-opaque, an iodine based contrast agent is injected into the circulation.

The first images in the series are without contrast agent, and then the substance is mixed with blood. As the contrast material evolves it makes visible the arteries, arterioles, capillaries, venules, veins and sinuses.

The acquired images are planar and monochromatic angiograms from the heart and brain, each ones with their specific properties (see Table I).

TABLE I. IMAGE CHARACTERISTICS

Parameters Cardiac CerebralNo. of images per series 70 30

Spatial resolution 512*512 1024x1024

Temporal resolution 15 images/second 3 images/second No. of bites stored for image 8 10

In the case of coronarography, the images are taken from one healthy person or from 10 patients with complete or partially stenosis on the main vessels. On the other hand, the cerebral angiograms are acquired from 5 people with artero-venous malformation.

Images are acquired in DICOM format, the acronym for Digital Imaging and Communications in Medicine.

Tache I.A., Vessels Enhancement in X-ray Angiograms, IEEE International Conference on e-Health and Bioengineering Iasi, Romania, November 2015, WOS:000380397900202, DOI: 10.1109/EHB.2015.7391549

IEEE copyright

Vessels Enhancement in X-ray Angiograms Irina Andra Tache1

Affiliation 1: Department of Automatic Control and Systems Engineering, University Politehnica of Bucharest, Romania

irina.tache@acse.pub.ro, irina.andra@gmail.com

II. VESSEL ENHANCEMENT METHODS

The vessels’ study from an image is based on topology or geometry, on pixels gray-levels and on neighbourhood information.

The image segmentation and contour detection are essential steps in image processing. These results can be useful in diagnosis establishment, computer guided surgery and treatment evolution.

The segmentation methods vary in function of acquisition type, applicability, human intervention, etc. Sometimes, some filtration or other image processing techniques such as morphological operations are used before or after the image segmentation.

The vessel enhancement operations presented in the following sections are complex spatial filtering which are preliminary for the angiography segmentation.

Digital Subtracted Angiograms (DSA)

This is a well-known technique of extracting the blood vessels from digital images. It implies the selection of a mask image (Imask(x,y)) without the contrast agent and its subtraction from all the opacified images from the series (Iopacified(x,y)).

The purpose of this technique is to remove the static anatomical structures from an image series. In this case the anatomy of the patient remains identical in the opacified as in the unopacified images. They can be mathematically modelled using the linear attenuation coefficient (mp) and the thickness (lp) of the patient as described by the following formulas [10]: ,

(1) f ,

(2) Where mv is the vessel thickness and lv is the linear attenuation coefficient.

Considering the equations (1-2) a better technique will be the logarithmic DSA as in (3): _ , ln ,ln f , l .

(3)

The thickness of the human body is eliminated; the image subtraction equation is linearized and it can be used for blood flow estimations. The difficulties of the above mentioned methods appear when the motion artifacts become important, such as in the case of coronary angiograms. For the case of cerebral angiogram, a simple method for removing the noises due to patient head motion is disseminated in [11].

Meijering in [12] synthesize a series of motion and registration algorithms special dedicated to digital subtracted angiograms. Kagadis in [13] on the other hand proposed a new method for quantitative vascular imaging from DSA.

B. Vessels enhancement filtering

The vessel enhancement is a nonlinear filtering method, which deals with the problem of non-additive noises

without a normal distribution and seeks the geometrical structures which can be the best candidates of being vessels. Knowing that the vessels are connected tubular structures, piece wise linear with curvilinear orientations, some filtering techniques are specifically designed, such as the Gabor filtering and the Frangi vesselness.

B.1. Gabor filtering

The Gabor filtering use the directionally selective band pass filters which are successfully applied for orientated line detection. The multiscale version offers the possibility to be adapted for different frequencies [6]. By tuning these filter frequencies, the noise is eliminated and the vessel centerline is enhanced, making the method ideal for the low contrast images with a blurring background [3].

In the image complement, the vessels have positive contrast so they are brighter than background and need only the real kernel.

The function of the real Gabor filter kernel orientated in the angle φ = -π/2 is g(x, y) [14] and it is expressed in (4): , 12 12· cos 2

(4)

Where is the central spatial frequency, and control the spatial expending orizontally and vertically related to the frequency and orientation band, ⁄ is the aspect ratio which is a measure of the filter’s asymmetry, τ is a measure of the lines detector and controls the filter scale.

The kernel for other angles from -π/2 to π/2 is generated with the coordinate transformation from the equation 5: , · · , ··

(5)

Where φ is the filter orientation obtained with a rigid rotation in the x-y plane. For vertical components φ is set to zero. The period of the cosine is τ, therefore ν = 1/ τ.

Many parameters are related to the scale τ, which is adjusted accordingly to the sizes of the curvilinear structures.

The maximum local response is selected for different scales and orientations in order to construct the vessels’ map.

Sang in [2] stated that a small scale succeeds to detect the vessels’ edges and a large scale, the centerlines, and they offer an exemplification for the cerebral angiograms.

B.2. Frangi’s vesselness filtering

Frangi in [15] proposed an enhancement filter based on the analysis of the eigenvalues of the Hessian matrix of the intensity image. Every element of the matrix is constructed with the aid of the second-order Gaussian derivatives which offers the possibility to measure the contrast in the direction of the derivative between the image regions inside and outside the scale range (-s, s):

(6)

Where subscripts indicate the image gradients and hxy is the convolution of the image J with the scaled version of the second-order Gaussian derivative g(x,s):

, 12 (7)

The standard deviation is related to the scale (s) which depends on the vessels’ dimensions.

Considering the two eigenvalues with the property |γ2| ≤ |γ1|, then the vesselness filtering [15] adapted for the two dimensional case is constructed as:

VF x, y , s e A 1 e R , γ 00 , γ 0 (8)

Where A is a measure of the difference between the two eigenvalues and R is a measure of the whole curvature strength, α1 and α2 are two scaling factors related to the sensitivity of the measure A and R, respectively [16]. The high eigenvalues correspond to vessel detection. Therefore, in the multiscale version of the filtering, only the local maximum is selected to construct the final filtered image. C. Thresholding

For images with bimodal histogram, therefore the

pixels’ values have two local peaks, a punctual transformation based on thresholding can be applied. If a gray level is below a certain range, it will belong to one group of pixels. If it is above the established range, it will belong to other group.

A well known thresholding technique is the method of Otsu [17].

All these methods are implemented into Matlab®2011b and compered.

RESULTS

A cerebral angiogram was considered to be more relevant for exemplification of the above mentioned image processing methods (see Table II). The image used for the current image processing is indicated in parenthesis.

TABLE II. MEDICAL IMAGE PROCESSING

1. Original image 2. Digital subtraction angiography

3. Image Complement for step 2

4. Thresholding P = 0.41 for step 3

5. Image complement for step 1 6. Single scale Gabor filtering for step 5

7. Multiscale Gabor filtering - GF for step 5

8. Multiscale Frangi filtering - FF for step 5

9. Thresholding (GF) for step 7 10. Thresholding (FF) for step 8 The digital subtraction angiography (step 2)

eliminates the background structures and produces an output image only with the blood vessels. The problem resides in the contrast decreasing which makes almost impossible the visualization of the small vessels. Therefore two different multiscale filtering are applied on the initial image.

The image complement (step 3) consists in the subtraction of the maximum contrast resolution from the pixels’ values, which produces an inversion of the colors in the output image. This image is preferable, especially for the Gabor filtering, because all the vessels must be marked in bright color.

The thresholding operations use either the Otsu method for the determination of the optimal threshold or the manual setting (step 4, 9, 10).

For the Gabor filtering two versions are compared, one with a single scale (step 6) and the other one with a multiscale approach (step 7). The improvement is evident in the second case, because the scale is tuned accordingly to the vessels’ diameters of the cerebral angiograms. The diameters of the cerebral and coronary vessels vary from 4-20 pixels and the scale is adapted to this range in order to detect the thick and thin vessels. The angle θ is rotated from 0 to 90 degrees for detecting the structures with different orientations. The output image is composed of the maximum magnitude response for each pixel over all the combinations.

After visual inspection of the steps 9-10 it was decided that the Frangi multiscale filtering worth to be used for further image analysis.

III. CONCLUSIONS

An important step in improving the diagnosis of medical images is the segmentation process which can be regarded as an objects’ classification with the goal of localization or contour detection of the objects. This target is not properly achieved if the noise is not correctly filtered in the original image. Therefore, an image enhancement is a crucial operation for angiograms.

The Frangi vesselness filtering demonstrated to be the appropriate image processing for vessels extraction.

All the processing steps presented in this paper can be further used for determination of vessel’s radius, cross-section and length.

ACKWOLEGMENT

This research was funded by the UEFISCDI National Project II for Partnership, Grant No. 130 / 29.07.2012 “High PErformance Computing of PersonAlized CaRdio ComponenT Models”.

REFERENCES

[1] Kirbas, C., Quek, F., (2004), A Review of Vessel Extraction Techniques and Algorithms, ACM Computing Surveys, Vol. 36, No. 2 , pp. 81–121

[2] Sang N., Tang Q., Liu X., Weng W., (2004), Multiscale Centerline Extraction of Angiogram Vessels Using Gabor Filters, First International Symposium, Computational and Information Science , Shanghai, China, pp. 570–575

[3] Cao, Z., Liu X., Peng, B., Moon Y.S., (2005), DSA Image Registration based on Multiscale Gabor Filters and Mutual Information, IEEE International Conference on Information Acquisition, Hong Kong and Macau, China

[4] Li, Q., You, J., Zhang, L., Bhattacharya, P., (2006), A Multiscale Approach to Retinal Vessel Segmentation Using Gabor Filters and Scale Multiplication, IEEE International Conference on Systems, Man, and Cybernetics, Taipei, Taiwan

[5] Soares JV, Leandro JJ, Cesar RM, Jelinek HF, Cree MJ,( 2006),Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification, IEEE Trans Med Imaging, Volume 25(9), pp. 1214-1222.

[6] Rangayyan R.M., Ayres F.J., Oloumi F., Oloumi F., Eshghzadeh-Zanjani P., (2008), Detection of blood vessels in the retina with multiscale Gabor filters, J. Electron. Imaging 17(2), pp. 023018/1 - 7

[7] Siddalingaswamy, P.C., Gopalakrishna Prabhu K., (2010), Automatic detection of multiple orientated blood vessels in retinal images, J. Biomedical Science and Engineering Vol. 3, pp. 101-107

[8] Kharghanian R., Ahmadyfard, A., (2012), Retinal Blood Vessel Segmentation Using Gabor Wavelet and Line Operator, International Journal of Machine Learning and Computing, Vol. 2, No. 5

[9] Sun, K.Q., Sang, N., (2008), Morphological enhancement of vascular angiogram with multiscale detected by Gabor filters, Electronics Letters, Volume:44 , pp. 86-87

[10] Hasegawa, B. H., (1990) The Physics of Medical X-Ray Imaging, 2nd edition, Madison, Medical Physics Publishing]

[11] Tache I.A., Vermandel M., Vasseur C., Udrea A., Popescu D. – Preliminary Results for Automatic Detection of Arterio-venous Malformations from Medical Images, Proceedings of the 19th International Conference on Control Systems and Computer Science (CSCS-19), Vol. 2, pp. 313-318, Bucharest, Romania, May 2013

[12] Meijering, E., (2000) Image Enhancement in Digital X-Ray Angiography, Ponsen & Looijen Wageningen Publishing

[13] Kagadis G.C., Spyridonos P., Karnabatidis D., Diamantopoulos A., Athanasiadis E., Daskalakis A., Katsanos K., Cavouras D.,Mihailidis D.,Siablis D., Nikiforidis G.C., (2008), Computerized Analysis of Digital Subtraction Angiography: A Tool for Quantitative In-vivo Vascular Imaging, Journal of Digital Imaging, Vol 21, No 4

[14] Ayres, F.J., Rangayyan, R.M., (2007), Design and performance analysis of oriented feature detectors, J. Electron. Imaging, Volume 16(2), pp. 023007.-1-12

[15] Frangi, A.F., W.J. Niessen, R.M. Hoogeveen, T. van Walsum, M.A. Viergever, (1999), Model-based quantitation of 3-D magnetic resonance angiographic images, IEEE Trans. Med. Imaging, 18 (10) pp. 946–956

[16] Schrijver, M., (2002), Angiographic Image Analysis to Assess the Severity of Coronary Stenosis, Ph.D. thesis at University of Twente

[17]Otsu, N., (1979), A Threshold Selection Method from Gray-Level Histograms, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 9, No. 1, pp. 62-66.