Carotid Artery B-Mode Ultrasound Image Segmentation based on Morphology, Geometry and Gradient Direction
I Made Gede Sunarya
*a,b, Eko Mulyanto Yuniarno
a, Mauridhi Hery Purnomo
a, Tri Arief Sardjono
a, Ismoyo Sunu
c, I Ketut Eddy Purnama
aa
Department of Electrical Engineering, Institut Teknologi Sepuluh Nopember, Jl. Raya ITS, Sukolilo, Surabaya, Indonesia, 60111;
bInformatic Engineering of education, Ganesha University of
Education, Jl. Udayana No. 11, Singaraja, Buleleng, Bali, 81116;
cDepartment of Cardiology and Vascular Medical, Medical faculty, Universitas Indonesia, Jl. Letjen S. Parman, Kav.87, Slipi,
Jakarta Barat
ABSTRACT
Carotid Artery (CA) is one of the vital organs in the human body. CA features that can be used are position, size and volume. Position feature can used to determine the preliminary initialization of the tracking. Examination of the CA features can use Ultrasound. Ultrasound imaging can be operated dependently by an skilled operator, hence there could be some differences in the images result obtained by two or more different operators. This can affect the process of determining of CA. To reduce the level of subjectivity among operators, it can determine the position of the CA automatically. In this study, the proposed method is to segment CA in B-Mode Ultrasound Image based on morphology, geometry and gradient direction. This study consists of three steps, the data collection, preprocessing and artery segmentation. The data used in this study were taken directly by the researchers and taken from the Brno university's signal processing lab database. Each data set contains 100 carotid artery B-Mode ultrasound image. Artery is modeled using ellipse with center c, major axis a and minor axis b. The proposed method has a high value on each data set, 97% (data set 1), 73 % (data set 2), 87% (data set 3). This segmentation results will then be used in the process of tracking the CA.
Keywords: ultrasound image, carotid artery, segmentation, morphology, gradient direction
1. INTRODUCTION
Carotid artery is one of the vital organs in the human body. Carotid Artery (CA) is used as markers and diagnosis of Carotid Artery disease (CAD). CAD happened because of narrowing of the CA, which are blood vessels that supply blood with oxigen to the brain. Atherosclerotic plaque is caused by this narrowing. Atherosclerosis is a hardening of the arteries, which can affect arteries throughout the body. When the CA are blocked or narrowed, this condition is called carotid artery stenosis or CAD[1]. This narrowing can cause stroke. Annually, more than 36 million people died of non-communicable diseases (63% of all deaths). Globally non-communicable diseases the number one cause of death is cardiovascular disease each year. Cardiovascular disease is a disease caused by malfunctioning of the heart and blood vessels, such as coronary heart disease, Heart failure or heart disease, Hypertension and Stroke. Number of patients with stroke in Indonesia in 2013 based on the diagnosis of health personnel (health workers) estimated at 1,236,825 people (7.0 ‰), while based on the diagnosis of health workers/symptom-estimate as many as 2,137,941 people (12.1 ‰)[2].
CA features that can be used is the position, size and volume. Position feature can used to determine the initial initialization of the tracking process. Examination of the CA features can use some modalities, including the use of ultrasound.
Ultrasound can be used in medical imaging[3]. Ultrasound devices is consists of transmitter pulse generator, transducer, focusing control unit, the control unit for focusing, compensating amplifiers, digital processor and display[3]. Ultrasound is a diagnostic tool that widely used, is real time proces, versatile, dynamic and noninvasive, can use in human and veterinary medicine. Ultrasound imaging as a research tool also has limitations and disadvantages. One of the limitation is that it requires the knowledge, expertise and skilled operator/sonographer to have high-quality images, obtain accurate, repeatable[4]. Ultrasound imaging operate dependently by operator, because of that, there is a chance a small differences
* E-mail : [email protected] ; phone : (+62) 362 27213
in the images result obtained by two or more different operators. This can affect the process of determining an organ, for example carotid artery. To reduce the level of subjectivity among operators, it can determine the position of the carotid artery automatically.
Several previous studies have been done in the area of the artery segmentation. [5] Proposed segmentation techniques CA in noisy B-mode ultrasound images based on morphology, canny edge detection and histogram equalization. The experimental results show that the proposed schema is accurate enough to segment the different texture in ultrasound images. [6] Proposed method of segmentation with the order of steps are preprocessing, watershed segmentation, region and merging boundary extraction. The proposed method can produce accurate contours. [7] Proposed scheme carotid artery wall detection using active countour are initialized with hough transform. The scheme provided a tool that detect the CA wall in ultrasound image and it can be apllied in clinical practice. [8] Proposed method based on morphology methods and canny edge detector. This can save the physicians time to handle the patients and could meet the demand of the clinical usage. [9] proposed segmentation algorithm based on the level-set. It can segment the media adventitia boundary and lumen intima boundary of the CA for computing the volume of the CA wall. [10] Proposed a hybrid method that is used to evaluate atherosclerosis through a mathematical morphology approach and GVF-Snake method. [11] Develop an automatic technique to outlining based on the active disc formalism. Matched filter is used with a template size. The template size is chosen based on an estimate of the average size of the CA. [12] Fuse B-mode ultrasound and VFI (Vector Flow Imaging) to segment the vessel . [13] Using ellipse model to identifying femoral artery, reconstructing and finally registering a model of the surrounding anatomy to the ultrasound images. Segmentation artery only using a Gaussian filter on the preprocessing stage. New approach to segment CA based on morphology, geometry and gradient direction is proposed in this study.
2. METHODS
The study consisted of three steps, data collection, preprocessing and artery segmentation. Figure 1 shows the block diagram of steps performed in this study.
Figure 1. Block diagram of proposed method
2.1 Data Collection
The data that is used in this study were taken directly by the researchers (data set 1 and 2) on two different persons and from Brno university's signal processing lab database (data set 3). Retrieving data using ultrasound equipment Tranducer and beamformer (SmartUs EXT), Software: EchoWave II. Settings on the ultrasound equipment with a 7.5 MHz frequency and gain of 55%. On data collection, required precise probe positioning is required to get good data. Data is obtained in the form of video data in AVI format (.avi). Data processing is done on each frame of video in the form of an ultrasound image.
2.2 Preprocessing
In preposessing step, ultrasound images are proccesed using gausian filter, thresholding, median filter and morphology operations. The Ultrasound images are filtered using a gaussian filter with the aim of making the pixels softer (smooth) and reduces noise in the form of pixels with high intensity. Gaussian filter using the parameters of standard deviation (σ)
= 0.5. Thresholding makes a binary image by changing all the pixels are below a threshold value to zero and a value above the threshold value into one. Median filter’s purpose is to reduce the noise in the form of white pixels that is contained in the images. Image processing techniques that are based on the shape of a segment or region in the image named morphological operations. Morphological operations used in this study is the dilation operation to enlarge the size of the object segment by adding a layer around the object.
Data Collection
Thresholding Morphology
(Dilation) Gaussian
Filter
Artery Segmentation Median
Filtering Preprocessing
mim mitm
2.3 Artery Segmentation
In Artery segmentation, Artery is modeled using ellipse model [13] shown in Figure 2.
Figure 2. Artery Model using Ellipse.
Ellipse model consist of major axis a, minor axis b and central point c. There is a vector p contained in the ellipse and the distance vector d which is the center point c of the vector p with angle α. Vector n is the normal vector of the vector p.
N is the number of points that represent the surface of the ellipse can be calculated using Equation 1-4.
𝛼𝑖=2𝜋𝑖
𝑁 (1)
𝑑𝑖
⃗⃗⃗ = [𝑎 cos(𝛼𝑖) , 𝑏 sin(𝛼𝑖)] (2)
𝑝𝑖
⃗⃗⃗ =𝑐⃗⃗ + 𝑑𝑖 ⃗⃗⃗ 𝑖 (3)
𝑛𝑖
⃗⃗⃗ = [𝑏 cos(𝛼𝑖),𝑎 sin(𝛼𝑖)]
|[𝑏 cos(𝛼𝑖),𝑎 sin(𝛼𝑖)]| (4)
Equation ellipse with major axis a, minor axis b and centers c, calculated score Carotid Artery Score (CAS) with the Equation 5.
𝐶𝐴𝑆 = ∑ 𝑛⃗⃗⃗⃗ ∙ 𝑖 𝐺 (𝑝⃗⃗⃗⃗ )𝑖
|𝐺 (𝑝⃗⃗⃗⃗ )|𝑖
𝑁−1𝑖=0 (5)
CAS value is obtained from the dot (dot product) of the value of the gradient vector direction 𝑝⃗⃗⃗ (𝐺(𝑝𝑖 ⃗⃗⃗ ))with a normal 𝑖 point 𝑛⃗⃗⃗⃗ . Number of point N are using 32 points. This multiplier will generate the maximum value if the direction of a 𝑖 normal point parallel to the direction of the gradient vector 𝑝⃗⃗⃗⃗ . The value of the major axis a within the range 55 to 65 𝑖 pixels for the captured data by researcher and 25 to 50 pixels to the image of Brno university’s signal processing (SP) lab database. Value minor axis b =((1-f)*a) obtained using factor of flattening (flattening factor) f with a value of 0 to 0.1 with the difference in value of 0.02.
3. RESULT AND DISCUSSION
CA segmentation has been successfully performed. Data that used in this study consists of 100 sequence CA ultrasound images for each data set. The number of all data is 300 CA ultrasound images. One of original and final result CA ultrasound image segmentation can be seen in Figure 3.
(a) (b)
Figure 3. Carotid Artery Ultrasound Image (a) Original, (b) Final result of segmentation (shown by arrow)
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The process is performed based on the method in Figure 1. After retrieving CA ultrasound image, it filtered with gaussian filter with σ = 0.5. Gaussian filter image results is processed using thresholding process to produce a binary image. The threshold value is 0.07 for data set 1 and data set 2. The threshold value is 0.10 for data set 3. The image is then processed by morphology dilation method.
Image preprocessing results then processed using an elliptical equation (Equation 1-4) and for each pixel calculated maximum value of CAS (Equation 5). The process produce the midpoint of the ellipse in the ultrasound image.
All of data sets processed using the proposed method. Figure 4 shows the results of each phase at each different data set.
Data Set 1
Data Set 2
Data Set 3
Figure 4. Image result each step from different data set(Column 1- 6 sequence proses : (1)original image, (2)gausian filter, (3)Threshold, (4)median filter,(5)morphology dilation, (6)Segmenting carotid artery (shown by arrow)
The successfully of carotid artery segmentation determined by the position of the midpoint of the ellipse models that occur in the area of the CA. CA segmentation results produce results a good segmentation. The data set 1 provides the highest result is 97%. The results also give good results on data that taken with different ultrasound device and settings. Data set 3 is the data that taken with different ultrasound device and settings with others data set, it’s provide the segmentation accuracy 87%. Table I shows the result of the accuracy of the CA segmentation.
Table 1. Accuracy of Segmentation Data Set Image
Quantity
Number of True
Percentage of True
Sequence number of false segmentation
1 100 97 97% 1,14,63
2 100 73 73% 2,8,9,11,12,16,18,25,28,29,31,42,52,54,55,56,6
1,65,66,67,68,71,72,89,91,93,100
3 100 87 87% 8,15,18,19,31,40,73,80,92,95,97,99,100
Average Percentage of true 85.67%
Segmentation results of the proposed method, compared to the previous method. The method used for comparison is the Circle Hough Transform (CHT), CHT with preprocessing and Ellipse Method. The proposed method showed the highest result compared to all data sets. The highest value shown in the data set 1 with an accuracy of 97%. Comparison of the proposed method with other methods can be seen in Table II. According to Table II, the proposed method has a high value on each data set. This segmentation results will then be used in the process of CA tracking position.
Table 2. Result of Comparing Method
Percentage of True Method
Data Set 1 Data Set 2 Data Set 3
Circle Hough Transform (CH-T) 0% 3% 14%
CH-T with Preprocessing 3% 0% 1%
Ellipse[13] 4% 0% 63%
Proposed method 97% 73% 87%
4. CONCLUSION
The proposed method for segmenting CA which is the preliminary of the study in determining the position of the carotid artery. This study consisted of three steps, the data collection, preprocessing and segmentation arteries. Preprocessing used is Gaussian filter, thresholding, Median Filter, Morphology dilation. The result image then segmented using an elliptical geometry and direction of gradient. Of the three data sets are used, the proposed method has a high value. This segmentation results will then be used in the process of tracking the CA.
ACKNOWLEDGMENT
The authors would like to thank to BUDI DN - LPDP scholarship for their support through Indonesian education scholarship program.
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