Automated Detection of Air Embolism in OCT Contrast Imaging: Anisotropic Diffusion and Active

Contour based Approach

Kausik Basak1, Rusha Patra2, Manjunatha M1, Pranab Kumar Dutta1, 2 1School of Medical Science and Technology

2Department of Electrical Engineering Indian Institute of Technology Kharagpur

Kharagpur 721 302, India

Abstract – Embolism can be a cause of life threatening situation for which early detection and diagnosis is of major importance. This work describes an automatic approach for air embolism detection and measurement of different morphological features of the embolus using OCT contrast imaging technique. Firstly, the channel has been segmented through morphological processing. Manually selecting the initial contour for active contour (AC) technique is time consuming. To overcome this, anisotropic diffusion (AD) is implemented to automatically select the initial contour prior to AC. A snake based AC is executed to segment out the embolus. The proposed emboli segmentation mechanism has been compared with other segmentation techniques and it has been observed that it can efficiently extract the embolus with high segmentation accuracy (92% - 94%) and reduced computational time. Different morphological descriptors showing the shape properties of the embolus have been computed to perform the shape analysis of it and measuring the criticality of the blockage area. It has been experimented that this method can also track multiple emboli flowing through the microchannel, thereby facilitating the study of contrast imaging in air embolism detection.

Keywords - Optical coherence tomography (OCT), active contour, air embolism, image segmentation, contrast imaging.

I. INTRODUCTION Embolism can arise for different reasons, like: due to

clotting of blood in the blood vessels, formation of air bubble and also fat decomposition in microcirculation [1-3]. These emboli mostly clog to the capillary bed and restrict the further blood flow which hampers the transportation of oxygen and nutrients to the cells. This results in damage of the tissue structure as the cells stop functioning. That is why; a relative measurement of blood flow changes due to presence of blockage is of major importance in the clinical environment because it features as a crucial indicator in such pathological condition.

Doppler sonography and computed tomography (CT) are mostly used for diagnostic purpose for emboli detection. Doppler ultrasound is a potential technique to detect emboli in cases of carotid artery stenosis, acute stroke and atrial fibrillation [4]. Doppler signals are deviated due to the presence of emboli in the blood vessels [4]. In this context, transcanial

Doppler technique facilitates the study of symptomatic embolism in cerebral circulation [1]. CT scan also plays a significant role in emboli detection. Pulmonary CT angiography is utilized for pulmonary embolism, in which blood flow in lung artery is obstructed due to formation of blood clot or presence of air bubble [5]. Other hand, electrocardiography (ECG) is also helpful in this domain to assist the clinician. Normal ECG pattern changes due to presence of pulmonary embolism in case of patients, suffering from acute myocardial infarction [6, 7].

In this context, contrast based imaging technique can provide high resolution images of embolus in blood flow path. This paper focuses on the optical coherence tomography (OCT) based contrast imaging technique for embolism detection. Not only the location of the emboli, but also the morphological parameters of the emboli like: area, length, depth, aspect ratio, circularity etc. carry significant clinical information. This paper highlights active contour (AC) based segmentation to locate the blockage and to perform the shape analysis of the embolus. Manually selecting the seed point lacks in the context that the boundary should be placed accurately near actual boundary and the process of energy minimization as well as tracking the boundary become time consuming [9]. AD overcome this limitation by locating the seed contour. An improved snake based AC is implemented, incorporating a grow energy force which helps to track the contour from the origin of seed contour towards original boundary [9]. This will substantially reduce computational time and automate the overall process. The technique also facilitates the tracking of moving emboli over a sequence of images. This study considers only the air embolism case among different embolism phenomenon. Though this method is limited to the superficial/peripheral regions of the human body, but it can open up a new dimension for peripheral embolism detection with minimal invasiveness.

II. MATERIALS AND METHODS

A. Air Embolism Air bubble enters into the circulation due to pressure

gradient between the source of air and the vasculature. This pressure gradient helps the air emboli to flow through the vascular system [12]. The air emboli stops at the regions where

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larger vessels are bifurcated into narrower ones, thereby creating an obstruction in blood flow. Air embolism can be a cause of heart attack and stroke when it blocks the blood flow in brain circulation and coronary artery respectively [1, 12].

In venous air embolism, air enters into the systemic circulation and travels through the right side of the heart to the lung and causes a sudden blockage in blood flow through the artery that feeds the lungs. This phenomenon is also known as pulmonary air embolism. This can be a cause of respiratory distress and hypoxia [3, 5, 6, 14]. According to a survey around USA, pulmonary embolism (PE) has been found in one of 500 people, in which approximately 11% cases reach to death whereas untreated PE is observed to have a high mortality rate of about 30% [5, 13]. In contrast, arterial air embolism occurs when an emboli logs into the arterial system and lumps the area fed by the artery. This causes ischemia in different organs of the body and when it lands in brain circulation, it will likely be a cause of stroke. Often, it also hinders the blood flow through coronary artery, thereby causing of ischemic heart attack [1]. Thus, air embolism can become a life threatening disorder and a computerized diagnosis will be helpful to assist the clinicians to a great extent.

B. Basic Speckle Contrast Theory Speckle is developed on the screen when a highly coherent

light (laser) illuminates an optically rough surface and the resulting backscattered light interfere each other constructively and destructively [15]. Such pattern is altered due to movement of object. When the movement is high enough then it causes the image to be blurred. If the object is still, then the phase differences involved remain constant over time and so does the pattern. Depending on coherence of incident light and surface properties, the statistics of this speckle pattern change.

Fercher et al. investigated a non-invasive method for diagnosing retinal problem through retinal blood flow measurement [16, 17]. The speckle contrastC can be derived as the ratio of the spatial standard deviationσ of intensity I to the mean intensity I of speckle pattern:

22I IσCI I

−= = (1)

In case of digitally acquired images, camera integration time (T) fells a major impact on image blurring. If the integration time is long compared to the typical speckle decorrelation time, then the recorded image will be blurred enough. In practice, it is kept in the order of speckle decorrelation time (in the millisecond range). Fercher and Briers have derived the relation between de-correlation time cτ , exposure time T and the speckle contrast, assuming that the flow velocity profile is Lorentzian type [18].

⎟⎟⎠

⎞⎜⎜⎝

⎛⎟⎟⎠

⎞⎜⎜⎝

⎛−−=

c

cs TTI ττσ 2exp1

21 (2)

In the higher flow region, the decorrelation time of speckle pattern is small which corresponds to a small contrast value.

Similarly, stationary particle, having larger cτ , posses contrast value close to unity.

C. Sample Preparation Flow channels have been prepared in the paraffin blocks

maintaining an inner diameter of 2 mm and keeping them 0.5 – 1 mm below the superficial surface. Different geometric channels have been formed such as, straight channel (with constant and varying depth from the superficial layer) and Y – shaped channel (for forming a dummy vascular structure).

D. Experimental Setup The imaging scheme is depicted in figure 1. At the heart of

the instrumentation there is a SS-OCT system in which a swept laser source is tuned across a broad lasing wavelength to illuminate the interferometer. The swept source has a built-in Mach-Zehnder Interferometer (MZI, Thorlabs INT-MZI-1300) that provides the frequency clock for the laser. The output of the laser is coupled into a fiber based Michelson interferometer and split into the reference and sample arms using a broadband 50/50 coupler.

Figure 1. Speckle contrast instrumentation using OCT

The SS-OCT facilitates high speed depth profile imaging and collects interference signals through simultaneous multiple imaging channels. The en-face images are acquired from the CMOS camera. Besides, the cross-sectional images that reveal the samples internal structure are captured through the OCT channel. A mixture of saline and red gel ink has been pumped by a programmable syringe pump (NE – 4000, Newera) at a rate of 400–700 microlitre/min through channel for maintaining a suitable velocity range (14 – 18 mm/sec) and contrast variations. The swept source engine is triggered to start the lasing operation. A scanning laser (1325 nm), having an average output power of 10 mW, is directed on the sample channel for acquiring 2D cross sectional images at a rate of 20 fps. The transverse resolution of the imaging module is 15 micron and axial resolution (air/water) is 12/9 micron. The acquired images are further used for different image processing applications.

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E. Image Processing Algorithms Contrast variation arises due to difference in optical

properties between fluid and air bubble. The blockage area can roughly estimate from this raw contrast image but for finding different shape features of the embolus (area, perimeter, aspect ratio etc.), image processing (IP) techniques are executed sequentially. They are discussed in following sections.

1) Preprocessing: It refers to reduce the background noise level as well as enhancing the area of interest (AOI). A median filter has been applied to reduce the background noise. This would subsequently blur the AOI also. The intensity of AOI has been improved by dithering the image to lowest and highest grey levels (binary image). This helps to segment the required channel from background portion.

2) Channel Segmentation: Morphological Processing: Channel segmentation carries importance for selecting the specific channel (containing embolus) and performing different shape feature analysis of embolus with respect to the channel. The following steps have been implemented to segment out the channel.

a) The dithered image consists of speckle or grainy patterns. The regions in between the grainy speckles are filled to make the channel portion uniform.

b) Median filtering is performed to remove the speckles situating outside the uniform region (channel).

c) Invert the image for boundary labelling of different portions of the image.

d) Labelling the connected components for sorting out different regions according to the number of pixels falling inside each region.

e) The largest region represents the bottom portion and the 2nd largest region is the upper portion of the channel.

f) Morphological closing to smooth the boundary of both regions.

g) Take the boundary co-ordinate to segment the channel region.

3) Seed Contour Detection: Anisotropic Diffusion (AD) Filtering: Detecting seed contour is the first step of segmenting the embolus within microchannel. AD reduces the insignificant part by smoothing homogeneous content of an image using a Gaussian kernel while keeping edge information. The original image Io(x,y) is convolved with a Gaussian filter G(x,y;t) of variance t (scale-space parameter), thereby producing a successive number of more and more blurred images [21]. The family of blurred images I(x,y,t) can be represented as,

( ) ( ) ( )0, , , , ;= ∗I x y t I x y G x y t (3)

Larger values of t correspond to images at coarser resolution. Mathematically, the AD equation can be written as,

( )( ) ( ), , , ,tI div c x y t I c x y t I c I= ∇ = Δ + ∇ ⋅∇ (4)

For a constant c(x,y,t), the above diffusion equation becomes isotropic as given by [21],

tI c I= Δ (5)

In practice, the exact boundary is unknown, so an estimate of the boundary (edge) location is computed with respect to the space-scale. Let, E(x,y,t) be such an estimate. Hence, the conduction coefficient ( ), ,c x y t can be represented as a function of the estimate as given by,

( )c g E= (6)

Where, g(·) has to be a nonnegative, monotonically decreasing function with g(0) = 1, resulting a smoothed intensity levels within each region without affecting boundaries where the magnitude of E is large. Perona and Malik have shown that the simplest estimate of the edge positions are given by the gradient of the brightness function i.e. ( ) ( ), , , ,E x y t I x y t= ∇ [21]. Therefore,

( ) ( )( ), , , ,c x y t g I x y t= ∇ (7)

Different functions can be used for g(·), like:

( )2

exp Ig I K⎛ ⎞⎛ ⎞∇⎜ ⎟∇ = −⎜ ⎟⎜ ⎟⎝ ⎠⎝ ⎠

(8)

and ( ) 2

1

1

g II

K

∇ =⎛ ⎞∇+ ⎜ ⎟⎝ ⎠

(9)

Here, K controls the sensitivity of edges and the Laplacian operator is computed using 8-nearest neighbour discretization. AD filtering roughly extracts the bubble (emboli) boundary which is further utilized as an initial guess for active contour mechanism.

4) Embolus Segmentation: Active Contour (AC) Model: Segmentation has been performed using an improved snake based AC technique [9]. The basic idea is to fit an energy-minimizing spline along the boundary of an object, characterized by different internal and external image forces [10]. The goal is to reach for a curve where the weighted sum of internal and external energy will be minimum. The basic equation can be formulated as,

( )( ) ( )( ){ }1

int0

snake extE E v s E v s ds= +∫

(10)

where, the position of snake is represented by a planar curve

( ) ( ) ( )( ),v s x s y s= , intE is the internal energy force, used to

smooth the boundary during deformation. extE represents the external energies, pushing the snake towards the desired object boundary. Various numerical implementations of snake have been proposed; finite difference method, dynamic programming, simulated annealing, greedy algorithm and so on. Here greedy algorithm is adopted for snake computation [9]. Prior to the implementation of snake, the coordinates of seed contour is transformed into polar form ( ),ρ θ with

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quantization step sρ and ( )2s n

πθ = respectively. The energy

function of this model is given by,

Figure 2. Graphical representation of Active Contour in polar coordinates

( ) ( ) ( ) ( )( )

1

0

n

cont i curv i image i grow ii

E aE v bE v cE v dE v−

=

= + + +∑

(11)

According to figure 2, for each point over the curve ( ) for 0,1, 2,...., 1iv i n= − , the energy values at those points

{ }, ,i i i iv v v− +Ω = are calculated and iv is moved to the point

with the minimum energy among these three where iv − and

iv +are the two discrete points adjacent to iv at the radial

direction. This operation is performed iteratively until the number of moved contour points is sufficiently small or the iteration time exceeds a predefined threshold. The energy functions are described below.

contE : It is the continuity spline energy which forces the contour to be continuous. It is given as,

( ) ( )1 1 cont j j i j i j iE v d v v vρ ρ ρ− −= − − + − − ∈ Ω (12)

where, 1 1 and t t t tv vd

n nρ ρ

ρ− −− −= =∑ ∑

The normalized form of continuous energy is given by,

( ) ( )( ) ( )

maxj i

cont jcont j j i

cont jv

E vE v v

E v∈Ω

= ∈ Ω (13)

curvE : The curvature energy smoothes the contour boundary. It is represented as,

( ) ( )2 2

1 1 1 12 2 curv j i j i i j i j iE v v v v vρ ρ ρ+ − + −= − + + − + ∈Ω

(14)

and the normalized form is:

( ) ( )( ) ( )

max

j i

curv jcurv j j i

curv jv

E vE v v

E v∈ Ω

= ∈ Ω (15)

imageE : It symbolizes image energy and works on the intensity values of the image to attract the contour towards closest image edge. Its actual form and normalized form are,

( ) ( ) ( ) ( )1 1

1 1, , R R

image j j s j j s j j ir r

E v I r I r vR R

ρ ρ θ ρ ρ θ= =

= + × − − × ∈Ω∑ ∑ (16)

( )( ) ( )

( ) ( ) ( )

min

max minj i

j ij i

image j image jvimage j j i

image j image jvv

E v E vE v v

E v E v∈ Ω

∈ Ω∈ Ω

−= ∈ Ω

− (17)

where, ( ),I ρ θ symbolizes gray level intensity at ( ),ρ θ .

growE : Another form of external energy (grow energy) that helps to expand the contour from the centre towards the boundary. It is defined as,

( ) if v and

0 elsejj i v origin

grow j

e v I I TE v

+⎧ ⎫= − <⎪ ⎪= ⎨ ⎬⎪ ⎪⎩ ⎭

(18)

( ) ( )0

1 1 and j

i v ij

v i origin iv v

I I v I I vk k k k∈ Ψ ∈ Ψ

= =× ×∑ ∑

(19)

where, e is a negative constant and T is a threshold. jvΨ and

0Ψ are two k k× ( k is an odd number) sub-blocks with centre points at iv and the centroid of the contour respectively. Threshold T determines the range upto which the change in intensity is allowed to stop the outward movement of the contour.

III. RESULTS AND DISCUSSION Detection of air embolism has been performed on the raw

speckle images in an automated way from pre-processing, channel segmentation and detection of seed contour as well as embolus within the micro-channel. The overall programming part has been carried out in MATLAB environment (version 7.10.0). Step-by-step results are illustrated in following part.

A. Preprocessing and Channel Segmentation A 3X3 median filter is convolved with the input raw

speckle image. It is followed by dithering the image to enhance the intensity in AOI by converting the grey levels into binary values. Clinically channel segmentation implies to the selection of specific vessel in microvasculature system, where the emboli is located. This can be accomplished by different morphological operations like: dilation, erosion, boundary selection through connected component labeling etc. over the dithered image. The raw speckle image, dithered image and corresponding segmented out channel image are depicted in figure 3.

B. Seed Contour Detection Conventional anisotropic diffusion by Perona and Malik is

implemented on the dithered image [21]. A Gaussian kernel is used to smooth intra-region details while keeping edge

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information. A 2D network structure of 8 neighboring nodes is considered for diffusion conduction. Equation 8 is used here for diffusion co-efficient calculation, because it prioritizes high contrast edges over low contrast edges. Based on trial and error approximation, K value has been optimized in the range of 120 – 130. Figure 4(a) shows detected seed contour.

C. Emboli Segmentation The centre is fixed at the centroid of seed contour.

Quantization step size for angel θ is 1sθ = ° (n = 360) and for ρ is sρ = 1 pixel. The weighting parameters of different

energy functions are kept to be constants as a = 1, b = 0.8, c = d = 0.5. The length of radial direction (R) for computing image energy is chosen small (R = 3) for detecting sharp edges. For calculating grow energy, intensity in each iteration has been computed over 3x3 pixel (size of k) block. It should be small for tracking local minima or boundaries. Here, e and T value has been taken -0.8 and 20 respectively. Figure 4(b) justifies that AC (with snake) can effectively track the boundary of emboli present within the channel.

The proposed technique can also detect more than one blockage present in microchannel. This has been experimented with two air bubbles present in AOI. Anisotropic diffusion helps to segregate different regions in the image like: upper-lower portion of the channel, and the bubbles. Boundary labeling and sorting of those regions with respect to the number of pixels facilitates the segmentation of seed contours for the air bubbles. After sorting in a descending order, the first two regions are the channel’s lower and upper portions respectively. Rest regions above a threshold number of pixels are the bubble portions within the channel. The coordinates of these seed contours are then utilized for the active contour operation. This would substantially increase the computational time for the overall implementation, but time required for segmentation of the second bubble is small in comparison to the first bubble. Figure 5 depicts segmentation of two emboli within the microchannel.

D. Feature Computation The overall algorithm has been implemented over 40

numbers of time-frames taking at a rate of 20 fps and the mean values and standard deviations are tabulated in Table I. The shape descriptors change in each frame reflects the deformation of the air bubble. Measurements have been performed based on the resolution of OCT system: 15 µm in transverse and 12 µm in axial direction (for air).

(a) (b) (c)

Figure 3. (a) Input speckle image, (b) dithered image, (c) corresponding segmented out channel

(a) (b)

Figure 4. (a) Seed Contour of the embolus after Anisotropic Diffusion, (b) segmented embolus after AC implementation

Figure 5. Two segmented out emboli after AC implementation

E. Performance Evaluation Evaluation of the proposed approach has been performed

with other segmentation techniques like: Otsu thresholding [22] and level set method [23] on the basis of segmentation accuracy. This has been computed at the output of different techniques with respect to the ground truth. Mathematical expression for such accuracy measure is given by,

( ) 1 100

−= − ×

abs A BSegmentation Accuracy

B (20)

Where, A represents the segmented area computed using different segmentation procedures and B is the ground truth. Segmentation accuracy for different techniques is tabulated in Table II. It has been observed that the proposed method can extract the embolus with a high segmentation accuracy compared to Otsu thresholding. At the same time, level set method is also showing high accuracy in segmenting the emboli, but the computation time is quite larger than the proposed approach. Therefore, the proposed technique is a suitable tool for embolus segmentation and thus the method reflects good accuracy.

TABLE I. FEATURE SET OF EMBOLI PRESENT IN CHANNEL Features Mean Values ± Standard Deviation

Area 0.8425 ± 0.01154 mm2 Perimeter 3.4702 ± 0.05432 mm Major axis 1.3532 ± 0.00681 mm Minor axis 0.7972 ± 0.01066 mm Eccentricity 0.8078 ± 0.00653 Circularity 0.9561 ± 0.02825

Aspect ratio 0.5891 ± 0.00885

TABLE II. COMPARISON BETWEEN DIFFERENT SEGMENTATION TECHNIQUES Techniques Segmentation Accuracy (in %)

Otsu Thresholding 87 – 89 AC with snake 92 – 94

Level set method 93 – 96

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IV. CONCLUSION This paper proposes an automatic approach for detecting air

embolism in OCT contrast imaging. The proposed mechanism has been evaluated with respect to other segmentation techniques. Peripheral air embolism can be easily detected with a high degree of segmentation accuracy (92% – 94%) and the computational time is also reasonable than other approaches. Besides, AD application helps to minimize manual labor by automating the procedure of seed contour initialization. An improved snake based AC enhances the boundary tracking ability with reduced computational time. Multiple blockages can also be identified within the channel with this approach. Morphological segmentation of the microchannel points to the measurement of different shape descriptors of the embolus with respect to the channel. These parameters describe different structural properties of the embolus and also describing how much portion of the channel has been blocked. Variability in the shape features of the emboli, computed over a number of time frames, reflects deformation of the bubble during movement through the channel. In future prospects, these shape descriptors can be utilized further in classifying the emboli for a computer assisted diagnosis of air embolism. Hence, it will substantially reduce the rate of pathological cases in peripheral microcirculation by early diagnosing the air embolism.

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