Level-set based free fluid segmentation with improved initialization using region growing in 3D ultrasound sonography

Dae Hoe Kima, Konstantinos N. Plataniotisb, and Yong Man Roa

aDepartment of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 305-701, Korea; bThe Edward S Rogers Sr Department of Electrical and

Computer Engineering, University of Toronto, Toronto, Ontario, M5S 3GA, Canada

ABSTRACT

In this study, new free fluid segmentation method is proposed, aiming to increase segmentation accuracy on free fluids, at the same time, decrease processing time, regardless of the accuracy of initial seeds. In order to segment free fluid regions fast and accurate, we propose a new free fluid segmentation based on Chan-vese level-set with an improved initialization using minimum variance region growing. The proposed method is devised to take complementary effects on both methods. In experiments, the effectiveness of the proposed method is demonstrated with 3D US volumes in terms of Dice’s coefficient, volume difference, Hausdorff distance and processing time. Results show that the proposed method outperforms CVLS and MVRG in terms of processing time as well as segmentation accuracy.

1. INTRODUCTION Ultrasound (US) imaging has been widely used in a clinical diagnosis. Because US imaging is a portable and low-cost solution1, huge amounts of US data are acquired around the world2. Thus, due to the increase of data, the interests on medical image analysis and image-guided interventions on US emerge2. Especially, the focused assessment with sonography for trauma (FAST) examination is known to serve as a sensitive, specific and accurate tool for diagnosing a blunt trauma (free fluids)3. Free fluids reside in all three dimensions simultaneously, it is important to analyze the fluids in 3D US volumes, i.e., a fast and accurate segmentation of free fluids in 3D US is needed. However, few studies have been focused on the segmentation of free fluids in 3D US.

In this study, new free fluid segmentation method is proposed, aiming to increase segmentation accuracy on free fluids while decreasing processing time, even when an initial seed is far from true free fluid boundary. It has been widely accepted that a free fluid in US images of abdominal trauma appears as a dark area having a sharp angle4, locates in the free space between the surrounding abdominal organs5. In order to segment aforementioned free fluid regions fast and accurately, we proposed a new free fluid segmentation which is based on Chan-vese level-set (CVLS)6 with an improved initialization using Minimum variance region growing (MVRG)7. CVLS is an iterative segmentation that is widely used to segment general object in US images. However the CVLS method needs an accurate initial seed that is close to the target object, in order to converge fast. Providing accurate initial seed requires a cost (e.g., a manual initialization).

To solve the problem, we devise an improved initialization by employing MVRG. The MVRG is simple and fast, but inaccurate around the boundary of free fluids due to the increase of intensity variance around boundary. Thus, the proposed method is devised to take advantage of both CVLS and MVRG. By effectively combining MVRG and CVLS, we are able to increase segmentation accuracy, at the same time, decrease processing time. To the best of our knowledge, our work is the first attempt to devise the effective free fluid segmentation in 3D US image analysis.

2. METHODS In this paper, a new free fluid segmentation based on CVLS and an improved initialization is proposed. An initialization using MVRG is adopted to improve free fluid segmentation accuracy as well as processing time.

2.1 Proposed free fluid segmentation in 3D US volumes

In CVLS based segmentation in US, processing time is increased when an initial seed R0 is far from the ground truth (target volume RGT) boundary of free fluids. Considering volume difference between initial seed and ground truth, processing time can be modeled by:

Medical Imaging 2014: Computer-Aided Diagnosis, edited by Stephen Aylward, Lubomir M. Hadjiiski, Proc. of SPIE Vol. 9035, 90352Q · © 2014 SPIE · CCC code: 1605-7422/14/$18 · doi: 10.1117/12.2043953

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,)()()( 00 RRR VVtt GTCVLSCVLS −⋅Δ≈ (1)

where CVLStΔ is the processing time of a single iteration of CVLS, )( ⋅V is a volume of the given region.

In general, there is a trade-off between segmentation accuracy and processing time. To solve lengthy processing time in CVLS8, the proposed method devises an improved initialization by adopting less accurate but fast segmentation algorithm, especially MVRG. The MVRG is employed so that a region is growing based on intensity. This is well fit to segment free fluids because major characteristic of free fluids is intensity.

In this paper, we take coarse segmentation with MVRG as an improved initialization. Detailed process is as follows.

1) Initialization: The initial seed region R0 is defined as a binary volume as,

⎩⎨⎧

≠=

=,0

1)(0 px

pxxR

whenwhen

(2)

where p is the coordinate of the input seed point, R0(x) = 1 means the point x is included in an object, R0(x) = 0 means the point x is in background.

2) Improved initialization with coarse segmentation: MVRG is performed with the given initial seed region R0 to obtain an improved initialization followed by CVLS. We must decide a parameter of the coarse segmentation with MVRG, i.e., iteration number in MVRG. In this paper we propose a stopping criterion to reach the coarse segmentation. Segmentation accuracy is measured using stopping criterion comparison between the segmented object and the ground truth. Dice’s coefficient9 is used as a stopping criterion f in order to find a degree of overlap between the segmented object and the ground truth. The Dice’s coefficient is defined as

2/))()((

)(),(nGT

nGTnGT VV

VfRRRRRR

+∩

= , (3)

where nR is a segmented region in MVRG, )( ⋅V is a volume of the given region. The maximum Dice’s coefficient indicates the iteration stop of MVRG.

As a result, the coarse segmented region coarseR is obtained by maximizing Dice’s coefficient f as

),(maxarg nGTcoarse fn

RRRR

= . (4)

Figure 1 (a) shows the Pseudo code of the improved initialization with coarse segmentation.

.

1)(

)),(),((1

1

1

ncoarse

nn

nGTnGT

endnn

MVRGofiterationSingleffwhile

n

RR

RRRRRR

=

+←←

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+

−

.

1)(

)),(),((1,

1

1

0

mfine

mm

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coarse

endmm

CVLSofiterationSingleffwhile

m

RR

RRRRRR

RR

=

+←←

>←←

+

−

(a) Improved initialization with coarse segmentation (b) Fine segmentation Figure 1. Pseudo code of the proposed method. Note that f is stopping criterion that is a measurement where a larger value

means a higher segmentation accuracy to the target object.

3) Fine segmentation: Because it is hard for MVRG to reach near the object boundary due to the increase of intensity variance in fluid boundary, CVLS is performed to accurately segment free fluid regions, starting with the coarsely segmented region coarseR . Please see Figure 1 (b) for the algorithm, where mR is a segmented region in CVLS.

Combining the improved initialization and fine segmentation, the processing time of the proposed method can be represented as

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),,(maxarg),,(maxarg

,)()()()()( 00

mGTfinenGTcoarse

coarsefineCVLScoarseMVRGproposed

fftosubject

VVtVVtt

mn

RRRRRR

RRRRR

RR==

−⋅Δ+−⋅Δ= (5)

where MVRGtΔ is the processing time of a single iteration of MVRG. The proposed method can utilize the accurate segmentation of CVLS with reduced processing time by MVRG based improved initialization at CVLSMVRG tt Δ<Δ . Note level-set operation is known to be more complex than region growing for a segmentation8.

3. RESULTS The evaluation of the proposed free fluid segmentation has been performed with four 3D US volumes that contain free fluids. Manually segmented ground truth of free fluids is provided. In order to reduce the effect of speckle noise, the sparse representation based 3D US denoising10 is applied.

In order to demonstrate the effectiveness of the proposed free fluid segmentation, we performed quantitative comparisons of MVRG, CVLS and the proposed segmentation method with the following measurements; Dice’s coefficient9, volume difference9, Hausdorff distance11, and processing time. With the segmentation result and the ground truth, the Dice’s coefficient measures degree of overlap between two volumes, while volume difference measures volumetric changes between two volumes, Hausdorff distance measures how far two volume surfaces are from each other. Performance was measured for each volume and averaged among the volumes. A center of each free fluid was selected as an initial seed point. Note that the processing times were measured on a computing environment of MATLAB 2011b, Windows 7 64bit, Core i7 930 CPU and 16GB memory.

Figure 2 shows examples of segmentation results. Each segmented region is selected so that it maximizes the dice’s coefficient to the ground truth. As shown in the Figure 2 (a), MVRG shows under-segmented result. The reason is that as the segmented region is approaching to the object boundary, the intensity variance is increasing, while the maximum allowable variance of the segmented region is restricted. As shown in the Figure 2 (b), CVLS shows both under-segmented region and over-segmented region. That is due to the constraint of level-set that the initial region should be close to the object boundary. As shown in the Figure 2 (c), the proposed approach shows high segmentation accuracy that preventing over-segmentation or under-segmentation. Table 1 shows the average segmentation performances measured with the datasets. As shown in the table, the proposed approach increases segmentation performance (higher Dice’s coefficient and smaller volume difference) compared to other two methods with reducing time complexity compared to CVLS method. Note that MVRG and CVLS approach means segmentation is completed with either MVRG or CVLS, respectively. The processing time was measured in MATLAB environment to see relative comparison for the algorithms. Also note that, in this paper, a dice’s coefficient with a ground truth was used as the stopping criterion to show segmentation performance. In experiments, average number of iterations of each algorithm was measured at maximum dice’s coefficient between the segmented object and the ground truth for each volume (e.g., 65, 276, and 121 iterations for MVRG, CVLS and proposed segmentation method, respectively). Note 121 iterations for the proposed method consisted of 65 iterations for MVRG and 56 iterations for CVLS.

Table 1. Comparisons of the averaged segmentation performances.

Approach Performance measurement

Dice’s coefficient

Volume difference

Hausdorff distance

Processing time (sec)

MVRG 0.7040 0.5834 49.77 39.09 CVLS 0.7504 0.4851 47.11 182.3

Proposed method 0.7864 0.4151 46.91 77.63

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Figure 2(b) CV

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