3D reconstruction of carotid artery in B-mode ultrasound image using modified template matching based on ellipse feature

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Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization

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3D reconstruction of carotid artery in B-mode ultrasound image using modified template matching based on ellipse feature

I. Made Gede Sunarya, Eko Mulyanto Yuniarno, Tri Arief Sardjono, Ismoyo Sunu, P. M. A. (Peter) van Ooijen & I. Ketut Eddy Purnama

To cite this article: I. Made Gede Sunarya, Eko Mulyanto Yuniarno, Tri Arief Sardjono, Ismoyo Sunu, P. M. A. (Peter) van Ooijen & I. Ketut Eddy Purnama (2020) 3D reconstruction of carotid artery in B-mode ultrasound image using modified template matching based on ellipse feature, Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 8:3, 301-312, DOI: 10.1080/21681163.2019.1692235

To link to this article: https://doi.org/10.1080/21681163.2019.1692235

Published online: 22 Nov 2019.

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3D reconstruction of carotid artery in B-mode ultrasound image using modi fi ed template matching based on ellipse feature

I. Made Gede Sunarya ^a,b, Eko Mulyanto Yuniarno ^a,c, Tri Arief Sardjono ^a,d, Ismoyo Sunu^e, P. M. A. (Peter) van Ooijen ^fand I. Ketut Eddy Purnama ^a,c

aDepartment of Electrical Engineering, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia;^bDepartment of Informatic Engineering of education, Universitas Pendidikan Ganesha, Singaraja, Indonesia;^cDepartment of Computer Engineering, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia;^dDepartment of Biomedical Engineering, Faculty of Electrical Technology, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia;

eDepartment of Cardiology and Vascular Medical, Medical Faculty, Universitas Indonesia, Jakarta Barat, Indonesia;^fDepartment of Rehabilitation Medicine Radiology–Center of Medical Imaging, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands

ABSTRACT

Detection of vascular areas using B-mode ultrasound is required for automated applications such as registra- tion and navigation in medical operations. The limitations of Ultrasound imaging are the requirement of sonographer’s skills, expertise, and also knowledge when making data acquisition. It also influences the quality of the images. Carotid atherosclerosis can be treated with carotid artery stenting. The starting point of needle injection cannot be determined with certainty. The position of the arteries is in the body, therefore, determining the starting point of needle injection is done by estimation only and cannot be certainly determined. To be able to determine it, thefirst step needed is to determine the location of the carotid artery. We propose a 3D reconstruction of carotid artery using a modified template matching based on ellipse feature to determine it. It is processed using the procedure of data acquisition, preprocessing, segmentation, outlier selection of ellipse parameter fitting, visualisation. The proposed procedure with preprocessing produces the highest accuracy compared to the template matching method and the Hough Circle method with an accuracy value of 99.41% and has the smallest standard deviation value of 1.05. The best polynomial fitting results for all data is the polynomial equation on 22nd order with the mean error value of 0.26303.

ARTICLE HISTORY Received 22 April 2019 Accepted 8 November 2019 KEYWORDS

Carotid artery; 3D reconstruction; ellipse feature; segmentation; 3D visualisation

1. Introduction

The carotid artery is one of the vital organs in our body.

Atherosclerosis is the hardening of the carotid artery, and it causes the narrowing of the carotid artery. The narrowing can inhibit the oxygen supply in the blood to the brain. The nar- rowed or blocked carotid artery is called carotid artery stenosis or CAD (Carotid Artery Disease) (Sobieszczyk and Beckman2006).

The narrowing of the carotid artery can cause stroke disease.

Cardiovascular disease is a non-communicable disease that has the highest cause of death. Annually, the non-communicable disease causes more than 36 million deaths. In Indonesia, the number of stroke patients in 2013 based on the diagnosis of health workers is estimated at 1,236,825 people (0.7 %), and the diagnosis from symptom-estimate is 2,137,941 people (1.21 %) (RI and dan2014). Carotid atherosclerosis can be treated with carotid artery stenting. Carotid artery stenting can be done through the femoral, brachial/radial, and directly through the carotid arteries (Aziz et al. 2018). Currently, the detection and quantification of carotid stenosis are usually done by Magnetic Resonance (MR), Compute Tomography (CT), and Ultrasound (US) modalities. They help the decision-making process in inter- ventions. The results of this intervention can be used in assisting catheter insertion or removal of the plaque with a thrombus aspiration machine. Previous research determines the location of carotid arteries using MR or CT scan angiography

(Hameeteman et al.2009; Tang et al.2012). Studies using ultra- sound B-mode to determine the location of the carotid artery have been done by previous researchers (Mao et al. 2000;

Delsanto et al.2007; Destrempes et al.2009; Loizou et al.2009;

Yang et al.2011). MR has better image results than US, but it is not portable and the cost of intervention is more expensive. CT has a fast time taking data from MR data, but CT can have a radiation effect. Ultrasound is a widely used diagnostic tool, has a real-time process, versatile, dynamic, and non-invasive, and it can be used in human and veterinary medicine. The limitations of Ultrasound imaging are the requirement of skills, expertise, and also knowledge sonographer when making data acquisition.

It also influences the quality of the images (Coatney 2001).

Ultrasound also can be used in medical imaging (Carovac et al.

2011). Ultrasound variability can be quite large depending on the experience of the operators. Therefore, data taken by different sonographer has the possibility of producing different images.

The difference in image results can affect the process of locating and segmenting CA areas. Automatic determination of the loca- tion of the CA area can be used to avoid the subjectivity of the sonographer. Detection of vascular areas using B-mode ultra- sound is required for automated applications such as registration and navigation in medical operations. Another drawback is that this image tends to befilled with speckle noise and other arte- facts. Research with B-mode ultrasound images requires initial

CONTACTI. Made Gede Sunarya sunarya@undiksha.ac.id Department of Electrical Engineering, Faculty of Electrical Technology, Institut Teknologi Sepuluh Nopember, Jln. Teknik Mesin, Kampus ITS, Sukolilo, Surabaya 60111, Indonesia

2020, VOL. 8, NO. 3, 301–312

https://doi.org/10.1080/21681163.2019.1692235

© 2019 Informa UK Limited, trading as Taylor & Francis Group

processing to improve image quality. Better image quality in the Region of Interest (RoI) section will help in the process of deter- mining carotid artery areas. The initial processing techniques applied by previous researchers are the Gaussian filter (Carvalho et al.2015; Sunarya et al.2017) to get smoother images (blur), Median filters (Yeom et al. 2014; Poudel et al. 2016;

Sunarya et al.2017) to reduce noise in the form of small spots in the image (salt and pepper), Histogram Equalisation (Poudel et al. 2016) to increase the contrast of the image. The initial processing techniques are used in this paper in the order of Gaussian Filter, Histogram Equalisation, Median Filter, and Dilation. Initial processing techniques are used to improve image quality (image enhancement). Visually, the shape of the carotid artery looks like a circle when it is in a standard shape and looks like an ellipse when there is pressure by the ultrasound probe. Therefore, several previous studies used the circle feature (Golemati et al. 2007) and elliptical features to determine the area of the carotid artery (Gil et al.2000; Guerrero et al.2007;

Yeom et al. 2014; Sunarya et al. 2017). Determination of the starting point in needle injection and also the position of the needle tip, whether it is already in the artery or not, cannot be determined with certainty. The position of the arteries is in the body, so a determination of the starting point of needle injection is done by estimation and cannot be determined with certainty.

To be able to determine it, thefirst step needed is to determine the location of the carotid artery. The determination of the area of the carotid artery must be done quickly and in real-time.

Several previous studies have been done in the area of the artery segmentation on US images. Hamou and El-Sakka (2004) pro- posed a segmentation technique using several steps. Firstly, using morphology, canny edge detection, histogram equalisa- tion. The result is that the method has been accurate enough to segment ultrasound images with a different texture. Abdel- Dayem et al. (2005) proposed a method of segmentation in which there are several steps. The first step is preprocessing, segmentation using the watershed method, region, and then merging boundary extraction. It produces accurate contours.

Stoitsis et al. (2008) proposed a scheme using active contour to detect the wall of the carotid artery. It is initialised using Hough transform. The proposed method can be used in clinical practice to detect the carotid artery wall. The study of (Sunarya et al.2017) proposed a segmentation of carotid artery on ultrasound B-mode. The methods are based on morphology dilation, geo- metry, and gradient direction. Visualisation of objects in 3D gives a more tangible shape. 3D reconstruction of 2D images has been carried out by previous researchers. Reconstruction uses the help of magnetic tracker, optical tracker, and mechanical scanning (Prager et al.2010). The use of the tracker is needed to determine the distance between the world coordinate and the marker placed on the ultrasound probe. This study aims is to reconstruct the 3D object of CA lumen area using 2D B-mode images. In this study, the reconstruction was performed with data acquisition, preprocessing, segmentation, selection of outlier segmentation, ellipse parameterfitting, and visualisation methods.

2. The proposed procedure

In this study, the research procedure consists of six stages:

data acquisition, prepossessing, segmentation, outlier selection,

ellipse parameterfitting, and visualisation. The block diagram of the proposed procedure can be seen inFigure 1.

2.1. Data acquisitions

The data used in this study are the ultrasound image of the B-mode of the carotid artery. Data were obtained from 10 people. Each person was taken once into a data set. The description of the dataset is shown inTable 1. The image was acquired using the Smart Us EXT ultrasound modalities and Echo Wave II software. Transducer settings use frequency of 7.5 MHz, 30 mm depth, and gain of 85%–95%. The size of the ultrasound image resolution is 600 × 400 pixels. The data used are sequential data obtained from the data retrieval process with the probe moved in a right sweep direction. Data acquisi- tion is made by a 5 cm sweep and marking the start and the

Figure 1.Block diagram of the proposed procedure.

endpoint of data acquisition. Each data set has several frames that depend on the speed of the probe. Data retrieval is con- ditioned by the position and the direction of the probe is consistent. The image contains spatial data, and position data is assumed to be sequential from the starting point to the end of the data acquisition.

The acquisition of ultrasound B-mode carotid artery image was performed using the scheme inFigure 2. The captured data contains spatial data. Spatial data is obtained from the acquisi- tion using ultrasound modalities.Figure 3 shows the sample image data obtained in the data acquisition process.

2.2. Preprocessing

The preprocessing process is done to prepare better data in the segmentation process. The feature used is an elliptical morphology feature. Data on the surface area of the carotid artery are exposed for the ellipse’s features to be more visible. Preprocessing process aims to prepare images that show the area of the carotid artery. Preprocessing is needed on B-mode ultrasound images, since it has characteristics that are less visually clear and contain a lot of noise and speckle. We propose a preprocessing stage with a sequence of Gaussian filters, Histogram Equalisation, median filters.

Gaussian filters are applied to the image to obtain finer results for data in certain areas to be more uniformed. The standard deviation parameter value (σ) = 0.5. The result of the Gaussian filter process is followed by Histogram Equalisation. The data have a salt and pepper noise that

can interfere with the segmentation process. To overcome this obstacle, the medianfilter is used. In some parts of the image data, the split section with holes in the adjacent area can provide a high gradient value. Dilation morphology is used to overcome it.

2.3. Segmentation

The process of segmentation is done to separate the area of the carotid artery with other soft tissue areas. The segmenta- tion process uses the elliptical model feature. The ellipse model is shown inFigure 4.

αi¼2πi

N (1)

pi

! ¼! þci !di

(2)

nl

! ¼ ½bcosð Þ;αi asinð Þαi

j½bcosð Þ;αi asinð Þjαi (3)

dl

!¼½acosð Þ;αi bsinð Þαi (4) The ellipse model consists of the central pointc, the major axis aand the minor axisb. Minor axisbis calculated usingflatting factorfwhereb¼ ð1fÞa. Value offis−0.04 to 0.2. The value ofcðx;yÞrepresents each pixel position in the image wherexis the row, andyis the column. The value ofcstarts with the value cð0;0Þuntil the valuecðxmax;ymax). The angleαat the pointpis determined by the number of pointsNrepresenting the num- ber of points on the ellipse surface (Equation (1)). On the ellipse surface, there is a vectorpihaving a normal vectorniof pointi. The value of vectorpiis calculated using Equation (2) and the value of the normal vectorni is calculated using Equation (3).

The distance between the verticespiand the centre point isdiis calculated using Equation (4).

CAS¼X^N1

i¼0

n_l

! ð!Þpl

jð!Þjpl (5)

(a) Data acquisition procedure (b) Probe position on the pa- tient

Figure 2.The images result of preprocessing stage.

Table 1.The description of the dataset.

Dataset Power Depth Frequency Gain Number of Frames

DS1 −20 30 mm 7.5 MHz 95% 111

DS2 −20 30 mm 7.5 MHz 90% 117

DS3 −20 30 mm 7.5 MHz 85% 107

DS4 −20 30 mm 7.5 MHz 95% 101

DS5 −20 30 mm 7.5 MHz 95% 120

DS6 −20 30 mm 7.5 MHz 85% 106

DS7 −20 30 mm 7.5 MHz 90% 115

DS8 −20 30 mm 7.5 MHz 90% 120

DS9 −20 30 mm 7.5 MHz 90% 104

DS10 −20 30 mm 7.5 MHz 90% 110

Carotid artery positioning is determined on Carotid Artery Score (CAS) values. For a given mayor axis a and minor axis b, Carotid Artery Score (CAS) is calculated using the dot product of normal outward with the corresponding image gradient at the points on the ellipse. CAS calculation is shown in Equation (5). CAS values are stored as many as ten best values that represent the possibility of carotid artery position. The stored ellipse parameter consists of parameters centre pointc, major axisaand minor axisb. It will be used in the process of selecting the outlier fault segmentation of the carotid artery area.

2.4. Outlier selection

Selection of outlier is the process of determining segmentation error on a data based on all the available data. In the process of segmentation, there is the possibility of segmentation error in the carotid artery. The segmentation process stores ten best elliptical data to be used to overcome the segmentation error.

The steps taken to determine outlier results of carotid artery segmentation are to calculate the standard deviation (σ). The first step is done by taking the best CAS value for each data.

Standard deviation is calculated on the data centre axisx, the centre axisy; major axisa, minor axisb. Based on the obtained standard deviation values, we can determine the minimum limit ðMinðx;y;a;bÞÞ and the maximum limit ðMaxðx;y;a;bÞÞ from

the centre point of thex, centrey, major axisa, minor axisb. Variablekis the multiplier constant for the standard deviation valueðσÞ.

Min_x;y;a;b¼xx;y;a;bkσ (6)

Max_x;y;a;b¼xx;y;a;bþkσ (7)

The minimum limit and maximum limit are calculated using Equations (6) and (7). The determination of the outlier for each ellipse data is done by determining whether the parameter with the highest CAS value of each data is between the mini- mum and maximum values of each parameter or not. If all parameter values are within that range, then the parameters with the highest CAS value used are used, but if not, then the next CAS value is calculated whether it is within range or not. If there are parameters data within the range, then the para- meters represent the position of the carotid artery. If from 10 ellipse parameter data none is within range, then the ellipse parameter is represented with the previous data ellipse para- meterðn1Þ, because the carotid artery position data do not move away from the position of the data before.

2.5. Fitting ellipse parameters

Fitting of the parameters x;y;a;b is performed to obtain a mathematical function approaching each parameter. The shape of the human body is relatively smooth so the mathe- maticalfitting function can arrange the carotid artery segmen- tation to become smooth.

y¼a0þa1xþ þa_kx^k (8) The fitting method used is the polynomial fitting shown in Equation (8).

R²;Xⁿ

i¼1

yia0þa1xþ þakx^k

2

(9) With the error value shown in the Equation (9).R is the error value for each ellipse parameter. The selection of the polyno- mial degree that best matches the parameters x;y;a;b are determined by the error value generated between the polyno- mialfitting function with the parameter valuesx;y;a;b. 2.6. Point cloud visualisation

The polynomialfitting functions obtained for the parameters x;y;a;b are visualised using 3D point cloud. The polynomial fitting function for each parameter is used to draw the ellipse shape. The distance of thefirst ellipse and the last ellipse is 5 cm with the distance between ellipses is 0.01 cm. The small dis- tance between the ellipses is used to obtain smooth visualisa- tion. Visualisation using CloudCompare software. The 3D visualisation methods are based on the 3D reconstruction from scattered points cloud (Giaccari2017).

3. Result

The result of each stage is shown in this part. The results of the preprocessing stages can be seen inFigure 5. The Gaussian

Figure 3.Sample of carotid artery ultrasound image.

Figure 4.Block diagram of the proposed procedure.

filter process produces smoother images. Histogram equalisa- tion provides images with even contrast. The median filter provides images with reduced salt and pepper noise. The dila- tion process produces images with more extensive areas on high intensity values, so it creates more precise boundaries in the artery lumen. The preprocessing process is continued with the segmentation process. The segmentation process is done by proposing a segmentation method with an ellipse feature with image enhancement. The results of segmentation are compared with the ellipse method without preprocessing, hough circle method with preprocessing, hough circle method without preprocessing, template matching method with pre- processing, template matching method without preprocessing.

The correct result of segmentation is determined by looking at whether the results of segmentation are in the area of the carotid artery or not. If the result of segmentation is in the area of the carotid artery, then the results of segmentation are right while it is wrong if it is outside the area of the carotid artery. The determination of accuracy percentage of segmenta- tion methods for each dataset is done by counting the percen- tage number of correct segmentation. Figure 6 shows a comparison of image segmentation results. The comparison of the accuracy percentage of the segmentation method for each dataset can be seen inTable 2.

Each image candidates are selected with the highest CAS.

The ten best Carotid Artery Score (CAS) of each image repre- sents the ellipse parameters in pixel size. The stored parameters are the ellipse centre point c(x, y), major axis (a), minor axis (b), and CAS value of thefirst frame in pixels size format are shown inTable 3.

Comparison between pixels with metrics is 1 pixel = 0.8x10¹mm. This comparison value is obtained from the ultra- sound device. 3D visualisation is done using metrics scale (10-

1 mm). Ellipse centre point c(x, y), major axis (a), minor axis (b), CAS value of thefirst frame in metrics size format are shown in Table 4.

The result of mean and standard deviation (constant k = 0.5) are shown inTable 5. The standard deviation value is calculated using the best parameter values of each of the sequence images.

The minimum and maximum parameter values (Row x, Column y, Major axis a, Minor axis b) inTable 5 are used to select the best parameters for each of the 10 best parameters in sequence images. Each image is represented by an ellipse parameter. The best ellipse parameters are selected descend- ing from the best CAS value. The parameters chosen are para- meters that have all parameter values (x, y, a, b) in the range of minimum and maximum values. The results of the selected parameters for sequence images are shown inTable 6. If from 10 ellipse parameter data none is within the range, then the ellipse parameter is represented with the previous data ellipse parameter (n-1). The image sequence is a sequence of images from image 1 to the image at the end of the image frame. The value of the parameter (x, y, a, b) is the value of the selected ellipse parameter. Based on the selected parameters, data then form an ellipse surface cloud point. The snippet of point cloud data can be seen inTable 7.

The result of point cloud visualisation using cloud compare software by some view can be seen inFigure 7. The polynomial fitting equation is used to create a more smooth form of Carotid Artery visualisation. Ellipse parameter values, centre point (x, y), major axis (a), minor axis (b) are calculated sepa- rately. The polynomialfitting equation chosen is the equation that produces the smallest error value.

Table 8 shows the results of the calculation error value of the 2nd – 30th order polynomial fitting on Dataset 1.

(a) Gaussian Filter (b) Histogram Equalization

(c) Median Filter (d) Dilation

Figure 5.The images result of preprocessing stage.

(a) Correct Segmentation using Proposed procedure

(b) Correct Segmentation using Hough circle with preprocessing

(c) Correct Segmentation using Template matching with prepro- cessing

(d) Incorrect Segmentation using Proposed procedure

(e) Incorrect Segmentation using Hough circle with preprocessing

(f) Incorrect Segmentation using Template matching with prepro- cessing

(g) Correct Segmentation using El- lipse without preprocessing

(h) Correct Segmentation using Hough circle without preprocessing

(i) Correct Segmentation using Template matching without preprocessing

(j) Incorrect Segmentation using Ellipse without preprocessing

(k) Incorrect Segmentation using Hough circle without preprocessing

(l) Incorrect Segmentation using Template matching without prepro- cessing

Figure 6.The images result of preprocessing stage.

Table 2.The accuracy percentage comparison of the segmentation method for each dataset.

Template Matching Hough Circle Proposed Method

Dataset (DS) Pre(%) No Pre(%) Pre(%) No Pre(%) Pre(%) No Pre(%)

DS1 74.77 54.95 99.10 90.99 100.00 63.06

DS2 100.00 100.00 98.29 72.65 100.00 76.07

DS3 82.24 81.31 100.00 100.00 100.00 100.00

DS4 0.99 47.52 66.34 33.66 100.00 62.38

DS5 0.00 0.00 100.00 24.17 97.50 12.50

DS6 0.00 5.66 52.83 80.19 100.00 65.09

DS7 100.00 100.00 86.96 99.13 100.00 100.00

DS8 100.00 100.00 75.00 65.00 97.50 65.00

DS9 100.00 100.00 100.00 100.00 100.00 100.00

DS10 65.45 84.55 99.09 50.00 99.09 30.91

Mean 62.35 67.40 87.76 71.58 99.41 67.50

SD 44.43 38.93 17.19 27.93 1.05 29.18

Pre: With Preprocessing. No Pre: Without preprocessing.

The smallest mean error results are obtained in the poly- nomial fitting equation with 22^nd order. The polynomial fitting equation on the centre point parameters row fðyÞ, centre point column fðxÞ, major axis fðaÞ, minor axis fðbÞ are respectively shown in Equation 10(a–d) The visualisa- tion result of the point cloud using polynomial fitting 22^nd order can be seen in Figure 8.

The best mean error results from each dataset are shown in Table 9. From the entire dataset, the smallest mean error value is calculated from the whole dataset for each ellipse parameter and each order. The smallest mean error is in the polynomial fitting equation of 22^ndorder with a value of 0.26303. The point cloud is visualised using the 3D reconstruction from scattered points cloud method (Giaccari2017) with the addition of light placement.

fðyÞ ¼6:87E³⁴y²²7:85E³¹y²¹þ4:15E²⁸y²⁰þ1:34E²⁵y¹⁹þ 2:98E²³y¹⁸4:79E²¹y¹⁷þ5:76E¹⁹y¹⁶5:25E¹⁷y¹⁵þ

3:65E¹⁵y¹⁴1:90E¹³y¹³þ7:16E¹²y¹²1:72e¹⁰y¹¹þ1:16e⁹y¹⁰þ 9:84e⁸y⁹4:84e⁶y⁸þ1:23e⁴y⁷2:04e³y⁶þ2:30e²y⁵ 0:17y⁴þ0:82y³2:31y²þ3:33yþ177

(10) fðxÞ ¼4:53e³³x²²5:65e³⁰x²¹þ3:28e²⁷x²⁰1:18e²⁴x¹⁹þ 2:95e²²x¹⁸5:45e²⁰x¹⁷þ7:66e¹⁸x¹⁶8:40e^16x15þ

7:29e¹⁴x¹⁴5:03e¹²x¹³þ2:78e¹⁰x¹²1:22e⁸x¹¹þ4:27e⁷x¹⁰ 1:17e⁵x⁹þ2:5:44e⁴x⁸4:07e³x⁷þ4:82e²x⁶4:27e¹x⁵þ 2:54x⁴9:72x³þ21:55x²23:21xþ273:57

(11) fðaÞ ¼ 3:98e³³a²²þ4:83e³⁰a²¹2:73e²⁷a²⁰þ9:51e²⁵a¹⁹ 2:29e²²a¹⁸þ4:05e²⁰a¹⁷5:44e¹⁸a¹⁶þ5:65e¹⁶a¹⁵4:62e¹⁴a¹⁴þ 2:97e¹²a¹³1:51e¹⁰a¹²þ6:06e⁶a¹¹1:89e⁷a¹⁰þ4:51e⁶a⁹ 8:01e⁵a⁸þ1:01e³a⁷8:31e³a⁶þ3:33e²a⁵þ6:98e²a⁴ 1:48a³þ6:73a²12:67aþ57:86

(12) fðbÞ ¼ 2:89e³³b²²þ3:55e³⁰b²¹2:02e²⁷b²⁰þ7:14e²⁵b¹⁹ 1:74e²²b¹⁸þ3:12e²⁰b¹⁷4:25e¹⁸b¹⁶þ4:50e¹⁶b¹⁵3:75e¹⁴b¹⁴þ 2:47e¹²b¹³1:30e¹⁰b¹²þ5:37e⁹b¹¹1:75e¹⁶b¹⁰þ4:46e⁶b⁹ 8:68e⁵b⁸þ1:264e³b⁷1:32e²b⁶þ9:58e²b⁵4:33e¹b⁴þ 1:01b³0:38b²2:59bþ46:68

(13) The 3D visualisation of dataset 1 from the different view is shown in Figure 9. Figure 10 shows the results of 3D Reconstruction visualisation for each dataset.

4. Discussion

This study resulted in 3D artery reconstruction with visualisation of ellipse-based artery-based surfaces. This research begins with data acquisition, preprocessing, segmentation, selection of out- lier, curvefitting using polynomialfitting, and point cloud visua- lisation. Data acquisition produces ten datasets obtained from 10 people. Each dataset consists of different amounts of images depending on the speed at which the probe moves in the data acquisition process. Data acquisition uses the same frequency and depth settings. The gain setting varies with a value of 85%

−95%, the value is determined by whether good or bad image is produced for each person. The length of the data collection path is 5 cm, so the distance between images is 5 cm divided by the

Table 3.The CAS value of thefirst frame in pixels size format.

Centre point (c)

Row(y) Column(x) Major axis (a) Minor axis (b) CAS

224.00 332.00 62.00 54.56 18.36

224.00 332.00 65.00 54.60 18.27

224.00 330.00 62.00 54.56 18.14

224.00 332.00 57.00 54.72 17.88

224.00 332.00 60.00 55.20 17.87

224.00 330.00 57.00 54.72 17.71

224.00 334.00 65.00 54.60 17.59

224.00 330.00 53.00 55.12 17.49

224.00 328.00 57.00 54.72 17.41

224.00 330.00 55.00 55.00 17.16

Table 4. Example of selection ellipse parameter for 10 best CAS in metrics (1x10¹mm) size format.

Centre point (c)

Row(y) Column(x) Major axis (a) Minor axis (b) CAS

179.20 265.60 49.60 43.65 18.36

179.20 265.60 52.00 43.68 18.27

179.20 264.00 49.60 43.65 18.14

179.20 265.60 45.60 43.78 17.88

179.20 265.60 48.00 44.16 17.87

179.20 264.00 45.60 43.78 17.71

179.20 267.20 52.00 43.68 17.59

179.20 264.00 42.40 44.10 17.49

179.20 262.40 45.60 43.78 17.41

179.20 264.00 44.00 44.00 17.16

Table 5.The mean and standard deviation of the parameter on Dataset 1 with k = 0.5.

Parameter Mean Standard deviation Minimum Maximum

Row (y) 179.81 0.78 179.42 180.20

Column (x) 256.66 6.21 253.56 259.77

Major axis (a) 49.72 1.40 49.01 50.42

Minor axis (b) 44.79 1.17 44.20 45.37

Table 6.The best data parameter for each image on dataset 1.

Centre point (c)

Image sequenced y x Mayor axis (a) Minor axis (b)

1 179.2 265.6 49.6 43.648

2 179.2 265.6 49.6 43.648

3 179.2 265.6 49.6 43.648

4 179.2 265.6 49.6 43.648

5 179.2 265.6 49.6 43.648

. . .

107 180.8 248 49.6 45.632

108 180.8 248 49.6 45.632

109 180.8 248 49.6 45.632

110 180.8 248 49.6 45.632

111 1180.8 248 49.6 45.632

Table 7.The resulting sample of ellipse point cloud data.

Position

y x z

216.00 175.00 4.50

216.00 179.00 9.01

217.00 171.00 13.51

235.00 145.00 18.02

235.00 145.00 22.52

242.00 218.00 27.03

256.00 222.00 31.53

234.00 213.00 36.04

252.00 221.00 40.54

262.00 136.00 45.05

number of images in each dataset. Preprocessing stage is pro- cessed for each image. Thefirst stage of preprocessing is the Gaussian Filter. The result is a smoother image (blur) and reduces pixel values to extreme values. The Histogram Equalisation pro- duces an image with a histogram value more evenly distributed.

The Median filter process is used to remove salt and pepper noise. Morphology dilation process is used to close small gaps in the lumen artery.Table 2shows that in the proposed method and the Hough Circle method, the results of segmentation with

the preprocessing process give higher accuracy results than the results of segmentation without preprocessing. In the segmen- tation process, the proposed method is compared with template matching and the hough circle method.Table 2also shows that the proposed method with preprocessing produces the highest accuracy compare to other methods with an accuracy value of 99.41% and the smallest standard deviation with a value of 1.05.

Small standard deviation results and high accuracy show good segmentation results and not extensive data distribution. Outlier selection is determined by the maximum and minimum values of the ellipse parameter.Table 5shows the range value of the Row (x) parameter is 179.42–180.20, the Column (y) parameter is 253.46–259.77, the Major axis parameter is 49.01–50.42, the Minor axis parameter is 44.20–45.37. Each image will be repre- sented by the best parameter values from segmentation results.

Table 6shows the best parameter values for dataset 1, consisting of 111 values representing 111 images in dataset 1. The best parameter values are used for visualisation in the form of the point cloud. The results obtained are not smooth because it is influenced by the segmentation process. The polynomialfitting method is used to produce a smoother point cloud visualisation.

The polynomialfitting is used to process the point cloud of each dataset processed using the 2–30 order polynomial. Table 8 shows that the smallest mean error in dataset 1 is the order 22^ndfitting polynomial with a value of 0.53344. The mean error value shows the decreasing trend from order 2 to 22^ndorder and shows the trend of increasing mean error after order 22^nd. The polynomialfitting calculation produces an equation function for each parameter. The results show that the mean error value for all data used is the 22^ndorder polynomialfitting equation. The point cloud visualisation results with the 22^ndorder polynomial fitting show a smoother relationship between point clouds. The 3D reconstruction from scattered points cloud method is used to visualise the results of point clouds in the form of surfaces. The results of 3D visualisation are displayed in various angles, such as default angle, X-Y angle, X-Z angle, and Y-Z angle. 3D (a) Top

view

(b) front view

(c) Back view

(d) left view (e) Right view

(f) front iso- metric view

(g) Back iso- metric view Figure 7.Point cloud visualisation in 3D view.

Table 8.Mean error result of the 2nd–30th order polynomialfitting on dataset 1.

Centre point (c)

Order y x Mayor axis (a) Minor axis (b) Mean error

2 0.30204 3.0151 1.9274 1.3562 1.6502

3 0.29978 2.5898 1.7492 1.2831 1.4805

4 0.28811 2.051 1.7215 1.2602 1.3302

5 0.28429 1.945 1.4769 1.0214 1.1819

6 0.25252 1.8157 1.4314 0.99821 1.1244

7 0.21951 1.8001 1.4121 0.92103 1.0882

8 0.21193 1.7902 1.3651 0.90428 1.0679

9 0.16692 1.7217 1.3237 0.68862 0.97524

10 0.16491 1.6921 1.3135 0.6766 0.96178

11 0.15199 1.6893 1.2342 0.48914 0.89117

12 0.1461 1.6811 1.234 0.48218 0.88584

13 0.14608 1.5797 1.1295 0.48208 0.83434

14 0.14604 1.5779 1.1093 0.46898 0.82556

15 0.14547 1.4861 1.029 0.46854 0.78226

16 0.13591 1.3876 0.95267 0.46296 0.73477

17 0.13305 1.328 0.94907 0.44333 0.71335

18 0.12279 1.1839 0.94671 0.41601 0.66736

19 0.10563 1.1132 0.94667 0.41456 0.64502

20 0.10486 1.1131 0.78558 0.31075 0.57857

21 0.087017 1.0895 0.76257 0.30989 0.56224

22 0.085275 1.0368 0.72555 0.28608 0.53344

23 0.17616 1.0313 0.74471 0.52225 0.61861

24 0.091038 1.4876 0.70822 0.2792 0.64152

25 0.084699 1.0713 0.72237 0.273 0.53784

26 0.1162 0.99498 1.1466 0.68122 0.73476

27 0.14799 3.6972 1.0134 0.45484 1.3284

28 0.075538 7.611 2.9762 1.0977 2.9401

29 0.074768 3.1191 1.2874 0.65511 1.2841

30 0.1073 2.914 1.0203 0.54089 1.1456

visualisation results are also shown for all datasets. 3D visualisa- tion of each dataset shows the results of 3D reconstruction based on power retrieval performed on different people with the same data capture distance.

5. Conclusion

This study produced a 3D visualisation of the surface car- otid artery. This research consists of the following steps:

data acquisition, preprocessing, segmentation, outlier selection, ellipse fitting parameters, visualisation. The pre- processing process provides higher accuracy results in the

proposed method than the results of segmentation without preprocessing. The proposed procedure with pre- processing produces the highest accuracy compared to the template matching method and the hough circle method with an accuracy value of 99.41% and has the smallest standard deviation value of 1.05. The best polynomial fit- ting results for all data are that the polynomial equation produces the smallest mean error value of 0.26303 in 22^nd order. The modified template matching with ellipse fea- ture, therefore, can be used to determine the carotid artery position in the B-mode ultrasound image and visualise it in three dimensions.

(a) Top view

(b) front view (c) Back view (d) left view (e) Right view

(f) front iso- metric view

(g) Back iso- metric view Figure 8.Point cloud visualisation using polynomialfitting 22^ndorder in 3D view.

(a) Normal view (b) XZ view (c) YZ view (d) XY view Figure 9.Visualisation of 3D reconstruction result of dataset 1.

Acknowledgments

The authors extend their gratitude to the Indonesian Government In This Case, the Ministry of Research, Technology, and Higher Education who bear the costs for the student participating in this program, BUDI DN–LPDP, Ministry of Finance Indonesian Government for the Ph.D. scholarship program.

Disclosure statement

No potential conflict of interest was reported by the authors.

Funding

This work was supported by the The Ministry of Finance Indonesian Government; The Ministry Of Research, Technology, And Higher Education Indonesian Government.

Notes on contributors

I. Made Gede Sunaryawas born in Gianyar on 25 July 1983. received his S.Kom degree from Department of Computer Science, Universitas Gadjah Mada, Indonesia in 2006. He received Master of Computer Science degree from Universitas Gadjah Mada, Indonesia in 2012, and now he still study for his Doctoral degree at Department of Electrical Engineering, Institut Teknologi Sepuluh Nopember, Indonesia. His research interest is in digital image processing, intelligent sys- tem, computer vision, 3D reconstruction.

Table 9.The best mean error results from each dataset.

Centre point (c)

Dataset

The best

order y x

Mayor axis (a)

Minor axis (b)

Mean error

1 22 0.085275 1.0368 0.72555 0.28608 0.53344

2 21 1.6582 3.8638 0.28295 2.3534 2.0396

3 23 0.17149 0.27146 0.43391 0.2664 0.28581

4 22 0.23252 0.21186 0.17871 0.40815 0.25781

5 22 0.35634 2.2514 1.6514 0.2469 1.1265

6 22 0.10798 0.8439 0.14378 0.19805 0.32343

7 21 1.51E-27 1.7781 0.37888 0.18229 0.58482

8 22 1.8294 0.99873 1.0187 1.2493 1.2741

9 22 0.39289 0.35434 0.21293 0.32621 0.3216

10 22 0.61912 1.419 0.78139 0.49938 0.82972

(a) Dataset 1 (b) Dataset 2 (c) Dataset 3 (d) Dataset 4 (e) Dataset 5

(f) Dataset 6 (g) Dataset 7 (h) Dataset 8 (i) Dataset 9 (j) Dataset 10 Figure 10.Visualisation of 3D reconstruction result.

Eko Mulyanto Yuniarnoreceived his S.T. degree in 1994, Master of Technology in 2004, and doctoral degree in 2013 from Department of Electrical Engineering, Institut Teknologi Sepuluh Nopember, Indonesia. Currently, he is a lecturer of Department of Computer Engineering, Institut Teknologi Sepuluh Nopember, Indonesia. His research interest is in image processing, computer vision, and 3D reconstruction.

Tri Arief Sardjono was born in Surabaya on 12 February 1970. He studied Electrical Engineering at the Institut Teknologi Sepuluh Nopember in Surabaya, Indonesia, graduated in 1994. In 1996 he started to study at the Biomedical Engineering Program, Institut Teknologi Bandung, Indonesia on Health management Information System for Community Health Center. He started his PhD- study at the University of Groningen / University Medical Center Groningen, in 2003 and completed it in September 2007. The research topic was X-ray image analysis of Scoliotic Patient. Since 1995 he is working at the Electrical Engineering Department, Institut Teknologi Sepuluh Nopember Surabaya, Indonesia.

Ismoyo Sunureceived his general practitioner from Universitas Diponegoro, Indonesia in 1981. He received Cardiologist and Blood Vessel specialist from Universitas Indonesia, Indonesia in 1995, and he received his doctoral degree from the Universitas Indonesia, Indonesia in 2010. Currently, he is works at Pusat Jantung Nasional Harapan Kita Hospital. His speciality in cardiologist and blood vessel. His international qualifications are FIHA, FAsCC, FICA. He is member of IDI and PERKI.

P. M. A. (Peter) van Ooijenis a computer scientist working in radiology for over 15 years. Involved in multiple PACS installations and in scientific research on Medical Imaging Informatics. He is Associate Professor Medical Imaging Informatics at the dept. of Radiation Oncology of the University Medical Center Groningen (UMCG) and coordinator of the machine learning lab of the Data Science Center in Health (DASH) of the UMCG. He is also a Board Member European Society of Medical Imaging Informatics (EuSoMII), Member Subcommittee on Professional Issues and Economics in Radiology (PIER) of the European Society of Radiology (ESR), Editorial Board Member PLOS ONE, Editorial Board Member Journal of Digital Imaging. His focus research areas are Medical Imaging Informatics, Advanced Visualization in Medicine, and the clinical application of Data Science and Machine Learning.

I. Ketut Eddy Purnamareceived his S.T. degree from Department of Electrical Engineering, Institut Teknologi Sepuluh Nopember, Indonesia in 1994. He received Master of Technology degree from Institut Teknologi Bandung, Indonesia in 1999. And he received a doctoral degree from the University of Groningen, The Netherlands in 2007. Currently, he is a lecturer of Department of Computer Engineering, Institut Teknologi Sepuluh Nopember, Indonesia. His research interest is in data mining, medical image processing, and intelligent system.

ORCID

I. Made Gede Sunarya http://orcid.org/0000-0003-4586-2694 Eko Mulyanto Yuniarno http://orcid.org/0000-0003-1243-3025 Tri Arief Sardjono http://orcid.org/0000-0002-7842-5151 P. M. A. (Peter) van Ooijen http://orcid.org/0000-0002-8995-1210 I. Ketut Eddy Purnama http://orcid.org/0000-0002-7438-7880

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