segmentation of cca using frequecny implementation of active contours

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Research paper


J Digit Imaging DOI 10.1007/s10278-012-9481-7

Segmentation of the Common Carotid Artery Walls Based on a Frequency Implementation of Active ContoursSegmentation of the Common Carotid Artery WallsM Consuelo Bastida-Jumilla & Rosa M Menchn-Lara & Juan Morales-Snchez & Rafael Verd-Monedero & Jorge Larrey-Ruiz & Jos Luis Sancho-Gmez

# Society for Imaging Informatics in Medicine 2012

Abstract Atherosclerosis is one of the most extended cardiovascular diseases nowadays. Although it may be unnoticed during years, it also may suddenly trigger severe illnesses such as stroke, embolisms or ischemia. Therefore, an early detection of atherosclerosis can prevent adult population from suffering more serious pathologies. The intima media thickness (IMT) of the common carotid artery (CCA) has been used as an early and reliable indicator of atherosclerosis for years. The IMT is manually computed from ultrasound images, a process that can be repeated as many times as necessary (over different ultrasound images of the same patient), but also prone to errors. With the aim to reduce the inter-observer variability and the subjectivity of the measurement, a fully automatic computer-based method based on ultrasound image processing and a frequencydomain implementation of active contours is proposed. The

images used in this work were obtained with the same ultrasound scanner (Philips iU22 Ultrasound System) but with different spatial resolutions. The proposed solution does not extract only the IMT but also the CCA diameter, which is not as relevant as the IMT to predict future atherosclerosis evolution but it is a statistically interesting piece of information for the doctors to determine the cardiovascular risk. The results of the proposed method have been validated by doctors, and these results are visually and numerically satisfactory when considering the medical measurements as ground truth , with a maximum deviation of only 3.4 pixels (0.0248 mm) for IMT. Keywords Automated measurement . Image segmentation . Ultrasound . Intimamedia thickness

IntroductionM. C. Bastida-Jumilla (*) : R. M. Menchn-Lara : J. Morales-Snchez : R. Verd-Monedero : J. Larrey-Ruiz : J. L. Sancho-Gmez Tecnologas de la Informacin y las Comunicaciones Department, Universidad Politcnica de Cartagena, Campus Muralla del Mar, Antiguo Cuartel de Antigones, s/n, 30202 Cartagena, Spain e-mail: R. M. Menchn-Lara e-mail: J. Morales-Snchez e-mail: R. Verd-Monedero e-mail: J. Larrey-Ruiz e-mail: J. L. Sancho-Gmez e-mail:

Atherosclerosis consists of a thickening of the arterial walls. Although it is very spread into population and it may trigger strokes, embolisms or ischemia, it could be unnoticed for years. Therefore, medical research has been focusing on early detection of atherosclerosis. The intimamedia thickness (IMT) has emerged as an early and reliable indicator of atherosclerosis [1], making the tracking of the IMT possible with the aim of avoiding the worsening of atherosclerosis and cardiovascular risk. Another factor to take into account is that the IMT measurement is extracted by means of a B-mode ultrasound scan. Since it is a non-invasive technique, the IMT measurement facilitates studies over a large population. The use of different protocols to measure the IMT [2] and the variability between observers is a recurrent problem of the manual measurement procedure. Therefore, the


establishment of a single protocol to measure the IMT presents an important challenge. Nowadays, IMT is considered as a reliable indicator of atherosclerosis when the measurement protocol presents repeatability and reproducibility [3, 4]. In particular, the processing proposed is intended to take advantages of a methodical acquisition protocol [2], whose authors (from the Radiology Department from Hospital Universitario Virgen de la Arrixaca (Murcia, Spain)) have provided all the images used. The radiologist takes from one to three point pairs on the far (posterior) wall of the vessel along a 1-cm-long section proximal to the bifurcation. The measurement corresponding to the maximum IMT is recorded. The pictures were obtained with the ultrasound scanner Philips iU22 Ultrasound System, following the process described in [2]. Even though using the same transducer, the spatial resolution of the images varies from one to another, ranging from 0.029 to 0.067 mm/pixel. In other words, the radiologists use always the same probe, but it is their choice to change the image zoom or not. The manual procedure consists of marking a pair of points that delimits the IMT over a longitudinal cut of the CCA, around 1 cm after its bifurcation. On the image in Fig. 1, an example of the manual procedure is shown, where the interfaces between the lumen, where the blood flows, and the near (at the top of the image) and far (at the bottom) walls can be seen. At the far wall, a typical brightdark bright pattern can be appreciated. This pattern corresponds to the intimamediaadventitia layers of the arterial walls. The IMT is defined as the distance between the lumen intima interface (I5) and the mediaadventitia interface (I7), which is measured by means of two points (one on each interface) marked by the doctor, whereas through the segmentation of the interfaces I5 and I7 two lines would be obtained. As a result, not only would the IMT be measured more precisely but also other interesting statistics could be calculated along the artery length, such as mean, median or maximum IMT.Fig. 1 Interfaces to be detected and IMT over an example ultrasound image

Besides, the use of ultrasound image processing to extract IMT would increase reproducibility, avoid subjectivity and, since it is a non-invasive technique, it would also allow the automatic analysis of a large amount of images. The preceding idea makes the IMT automatic detection of great interest to make studies over a large population.

Background With the use of IMT as a cardiovascular risk indicator, there have been several attempts to improve its measurement procedure by making it user independent. Since the work of Gustavsson et al. [5], different image processing methods have appeared. Most of them were based on an analysis of vertical cuts of the image [5, 6], on active contours [7, 8] or on a combination of both [9]. Methods based on the analysis of the characteristics of vertical cuts of the image determine the boundary location by taking into account local information. Many of them included a cost function to minimize in order to introduce a continuity term to avoid irregular borders. Despite the use of a continuity term, the data are still locally derived. Moreover, none of them was completely automatic; they required user interaction, even though it consisted on giving only two points nearby the arterial wall or the horizontal seek position [5]. Those based on active contours avoid the locality problem but, since initialization is critical for active contours [10], the aforementioned works (amongst others) include user interaction to initialize the contours. The combination of both concepts (active contours and vertical analysis of the image) can even require some user interaction, except for the proposals of Molinari et al. [1113]. Up to our knowledge, fully automatic methods cannot be found in the literature until the publication of the work of Molinari et al. However, these methods (including Molinari et al.) do not deal with different spatial resolutions;


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Intima Media Adventitia


J Digit Imaging

the images they use present always the same zoom level or equivalence pixel to millimetres, which simplifies the wall detection. Moreover, they present some problems in the presence of blood turbulences or hard carotid plaques, and they do not segment the near wall. In our case, we pretend to obtain a fully automatic method, with an edge detection based on active contours which delimits near wall (I2) as well as extracts IMT.

of automatic initialization (see Automatic Initialization section) is the most elaborated in our process. In the sections below, each of the steps shown in Fig. 2 will be explained.

Wall Detection Automatic Initialization In the flow chart shown in Fig. 3, the followed steps to calculate the initial contours are shown. In the next sections, a description of each of the processing stages will be detailed. CCA Angle Correction Although most of the images present only a slight inclination, the CCA angle with respect to the horizontal direction is calculated to easily detect the position of the far wall. As a previous step to the angle correction, the image is cropped to make the image anonymous and to consider only the anatomical information on the ecography. The crop size is fixed and the same for all the images. Over this cropped image, a horizontal Sobel operator [14] is applied to detect the horizontal edges of the artery. After that, the main direction of the artery is detected by computing the Hough transform [15] of the edges. This orientation will give us the CCA angle, which can be corrected by rotating the cropped image. A bilinear spatial interpolation has been used in the rotation because it gives the best trade-off between computational cost and image quality [14]. Far Wall Detection The next step to obtain an automatic initialization (see Fig. 3) is to determine the location of the far wall (see flow chart in Fig. 4). Correlation of the cropped image with a model is used for this purpose. Since we want

CCA Segmentation Process The process followed to delimit the intima and adventitia layers of the CCA and the near wall is shown in Fig. 2. Firstly, the input parameters of the interfaces deli