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An efficient volumetric segmentation of cerebral lateral ventricles
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ScienceDirect ScienceDirect Procedia Computer Science 00 (2018) 000–000 Procedia Computer Science 00 (2018) 000–000
Available online at www.sciencedirect.com
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Procedia Computer Science 133 (2018) 561–568
International Conference on Robotics and Smart Manufacturing (RoSMa2018) International Conference on Robotics and Smart Manufacturing (RoSMa2018)
An efficient volumetric segmentation of cerebral lateral ventricles An efficient volumetric segmentation of cerebral lateral ventricles a a
Ankur Biswasaa*, P. Bhattacharyabb, S.P. Maitycc Ankur Biswas *, P. Bhattacharya , S.P. Maity
Department of Computer Science & Engineering, Tripura Institute of Technology, Agartala, Tripura (W), India b Departament ofScience Mathematics, National Institute of Technology, Agartala,Agartala, Tripura(W), India(W), India Department of Computer & Engineering, Tripura Institute of Technology, Tripura c b of I.T., IIEST, Institute Shibpur,ofHowrah, West Agartala, Bengal, India Departament Department of Mathematics, National Technology, Tripura(W), India c Department of I.T., IIEST, Shibpur, Howrah, West Bengal, India
Abstract Abstract
Human brain is a set of four communicating network of ventricles heaving with cerebrospinal fluid (CSF) which is Human brain a setbrain of fourparenchyma. communicating ventricles heaving with cerebrospinal fluid (CSF) is located insideis the An network efficientofsegmentation of cerebral lateral ventricles one which in each located inside braintheparenchyma. An efficient segmentation of cerebral lateral ventricles one inIneach hemisphere can the support study of efficient pathologies for successful conclusion of various diseases. this hemisphere can support theenergy study optimised of efficienttechnique pathologies for successful conclusionofoflateral various diseases. In MR this paper, an efficient and fast for volumetric segmentation ventricles from paper, optimised technique forgeodesic volumetric segmentation lateral ventricles from MR imagesanofefficient human and brainfast is energy proposed which is based on active contours of using level set method. The images of approach human brain is proposed which basedstages: on geodesic active contours level set method.stage, The proposed consists of mainly fouris main 1. Preprocessing stage,using 2. Presegmentation proposed of mainly four main 1. Preprocessing stage, 2. on Presegmentation 3. Contourapproach Evolutionconsists with Energy optimisation stage,stages: 4. Termination stage. Experiments multislice MRIstage, data 3. Contour Evolution with optimisation stage,of4.0.915 Termination Experiments on multislice MRI data obtained dice coefficient of Energy 0.955, jaccard coefficient and otherstage. surface distance measures demonstrate the obtained dice of approach 0.955, jaccard coefficient 0.915 and other surface distance measures demonstrate the advantages of coefficient the proposed in both accuracy of and efficiency. advantages of the proposed approach in both accuracy and efficiency. © 2018 The Authors. Published by Elsevier Ltd. © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) © 2018 The Authors. Published by Elsevier Ltd.committee This is an open access article under thescientific CC BY-NC-ND license Peer-review under responsibility of the of the(https://creativecommons.org/licenses/by-nc-nd/4.0/). International Conference on Robotics and Smart Manufacturing. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/). Keywords: Lateral Ventricle; MR Images; Volumetric; Geodesic active contour and level set methods Keywords: Lateral Ventricle; MR Images; Volumetric; Geodesic active contour and level set methods
1. Introduction 1. Introduction Lateral ventricle segmentation of MRI with no inoculation of any agent and no radiation harm facilitates Lateral aided ventricle segmentation of MRI with noasinoculation agent and no radiation harm facilitates computer disease diagnosis structurally, such narrownessofofany the cerebral aqueduct and foramina implying computer aided disease as narrowness ofAs thecerebrospinal cerebral aqueduct and foramina produced implying they can become blockeddiagnosis by bloodstructurally, following a such haemorrhagic stroke. fluid is continually theythe can become blocked by blood followinga ablockage haemorrhagic stroke. As cerebrospinal fluid continually produced by choroid plexus within the ventricles, of outflow leads to increasingly highis pressure in the lateral by the choroid plexus within the ventricles, a blockage of outflow leads to increasingly high pressure in the lateral
* Corresponding author. Tel.: +91-9436168323. E-mail address:author. [email protected] * Corresponding Tel.: +91-9436168323. E-mail address: [email protected] 1877-0509© 2018 The Authors. Published by Elsevier Ltd. This is an open access under the CC by BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/). 1877-0509© 2018 Thearticle Authors. Published Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/). 1877-0509 © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the International Conference on Robotics and Smart Manufacturing. 10.1016/j.procs.2018.07.084
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Ankur Biswas et al. / Procedia Computer Science 133 (2018) 561–568 Ankur Biswas, P. Bhattacharya, S.P. Maity / Procedia Computer Science 00 (2018) 000–000
ventricles. As a consequence, this commonly leads in turn to acute obstructive hydrocephalus. Features that are supportive in making the conclusion of acute obstructive hydrocephalus includes [1]. Enlargement of the temporal horns (best indicator), Transependymal oedema, or periventricular oozing, may be visible as high T2 signal on MRI or low-density change on CT around the margins of the ventricles. Irritation of the membranes (meningitis) and of the ventricles (ventriculitis) which is caused by infection or the preamble of blood causing haemorrhage can be presumed from lateral ventricle segmentation. In addition, Intraventricular hemorrhage (IVH) in preterm neonates born at less than 35 weeks gestational age and weight less than 1500 gm is a common disease with an occurrence of 12-20% [2] can be monitored by volumetric ventricle segmentation which is more sensitive to longitudinal changes in ventricular volume. It is shown that volumetric measurement offers the best manifestation of the anatomical structure of interest in medical image out of one-dimensional, two-dimensional and volumetric measurements [3] Manual segmentation done in earlier studies in order to quantify 3D ventricle volumes [4,5], is too strenuous and time consuming to make it clinically feasible. It is also exigent due to its irregular shape and indistinct boundaries, even for experts. There is a major contribution of work in favour of segmentation of the left ventricle of human heart, but contribution is still required for the joint segmentation of the both the lateral ventricles of human brain. Semi automatic as well as Region growing algorithms offers an effective result for MR image segmentation [6,7]. Classical active contours such as snakes [8] and level sets [9,10] also proved capable in variety of medical image segmentation problems. Although geodesic active contours has been exposed to be simple, efficient and relatively accurate [11, 12] but has limitations to leak through gaps in object edges in presence of noise or unclear boundaries. 3D snake applied on MRI to segment the left ventricle of heart [13] involve a high level of user interaction to avoid the contour leakage. Thus, accurate and efficient automatic or semi-automatic ventricle segmentation is highly desired in clinical practice. In this paper, we propose an energy optimized technique for volumetric delineating lateral ventricles from MR images of human brain based on geodesic active contours using level set method for the ease of implementation. We show that the introduced challenging energy optimization of the geodesic active contours problem can be globally optimized in Caselles’s formulation to achieve a substantial speed-up in computation. Moreover, we introduce a noise removal technique to prevent the tendency of contours to leak through the gaps. We compare the proposed technique with other region growing algorithms and claim that the proposed technique does not require any post processing stage like clean up step as followed in region growing segmentation algorithms that require filling holes and removing small connected components which reduces the computational complexity to a large extent.
2. Previous Work This section describes the different methods of segmentation of lateral ventricle of human brain structure in MR images using region growing algorithms. One of the region growing methods is Connected Threshold that uses flood fill iterator algorithm in order to decide the criterion for a pixel is to be included in the current region or not from visiting the neighboring pixels. The criterion used by the filter is based on an interval of intensity values provided by the user, lower threshold, Ilower and upper threshold Iupper. The region-growing algorithm includes those pixels whose intensities are within the interval.
I ∈ [ Ilower , Iupper ]
(1)
Where I (x ) is the image and X is the position of the particular neighbor pixel being considered for inclusion in the region. The result of this algorithm is shown in fig.1(b). Second method is the Confidence Connected filter that does not require user input as previous case; instead it computes the mean, µ and standard deviation, σ of intensity values for total pixels currently included in the region. The standard deviation is multiplied with the user input factor ’c’ and is used to define a range around the mean,
Ankur Biswas et al. / Procedia Computer Science 133 (2018) 561–568 Ankur Biswas, P. Bhattacharya, S.P. Maity / Procedia Computer Science 00 (2018) 000–000
(a)
(b)
563 3
(c)
Fig. 1. Segmentation results for cerebral ventricle using a. original image; b. ConnectedThreshold; c. Confidence Connected
µ ± cσ. Neighbor pixels whose intensity values are within the range are included in the region. The iterative process is completed when no more neighbor pixels are left to satisfy the criterion or maximum number of iterations is reached. As a drawback it requires three parameters as user input: multiplier, initial neighbourhood radius and Iterations. The following equation illustrates the inclusion criterion used by this filter,
I ( x ) ∈ [ µ − cσ , µ + cσ ]
(2)
Where I (x ) is the image and X is the position of the particular neighbor pixel being considered for inclusion in the region. The result of this algorithm is shown in fig.1(c).
3. Proposed Methodology This section describes the different stages of the proposed approach. The methodology utilizes the MR image extracted from MIDAS datasets in order the segment the lateral ventricles of human brain using energy optimization in Caselles’s formulation. The overall workflow of the proposed methodology is shown in fig.2. It consists of four main stages:
3.1. Preprocessing. The 3D region of interest (ROI) i.e., the area that contains the lateral ventricle is selected and alienated from the image in order to minimize the computational workload. The alienated ROI is resampled (supersampled) by a fixed factor (2 in proposed method) in x, y and z voxels using Linear interpolation (for better quality). The re-sampling is carried out using Curvature anisotropic diffusion algorithm for reducing the noise from the image and enhancing the boundaries of the ventricles to avoid its tendency to leak out.
Fig. 2. Work flow of the proposed methodology.
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Ankur Biswas, P. Bhattacharya, S.P. Maity / Procedia Computer Science 00 (2018) 000–000 Ankur Biswas et al. / Procedia Computer Science 133 (2018) 561–568
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3.2. Presegmentation In this stage the pre- segmentation parameter Clustering approach is set that requires input for number of cluster and foreground clusters depicting the Cluster probability density plot as shown in fig. 3.
3.3. Contour Evolution with Energy optimization. To initiate the final segmentation task, geodesic contours with optimized energy, region competition force to push the contour inwards or outwards and curvature force to formulate the contour boundary smoother and can prevent from leaking into narrow objects are set on the image. The stiffness of the contour is also taken into consideration. Level set method eases the actual implementation of the contours. The contours are moved by image driven forces to the boundaries of the desired objects. The initialization and execution of it requires the energy function (the weighted combination of internal and external forces) to be optimized. The curve energy, E is given by the following function where C represents the evolving curve, and I represent the image data
E=
∫
(3)
f ( I ) ds
c(s)
where f is decreasing function of the image data. Energy of the curve can be defined by a functional, 1
1
1
0
0
0
E [( C ( s )] = α ∫ E internal ( C ( s )) ds + β ∫ E image ( C ( s )) ds + γ ∫ E external ( C ( s )) ds
(4)
Curve with minimal geodesic length is searched in Caselles’s formulation. 1
Emin ( C ) = ∫ g (| ∇Gσ * I (C ( s )) |) | C ′( s ) | ds
(5)
0
where g : [0,∞] →R+ is a strictly decreasing function such that g(r) →0 as r →∞, e.g.,
g (| ∇ G σ * I ( x , y ) |) =
1 1 + ∇ Gσ * I ( x, y )
(6)
The curve will be attracted towards the region where the weight g (| ∇ G σ * I ( C ( s ) |) is low i.e, the edges of the image. The method is implemented by 3D geodesic active contour method developed by [14] using ITK-SNAP [15]. The segmentation terminates when the entire region of the lateral ventricles are covered fully.
Fig. 3. Cluster Probability density plot as Clustering presegmentation parameters.
Ankur Biswas et al. / Procedia Computer Science 133 (2018) 561–568 Ankur Biswas, P. Bhattacharya, S.P. Maity / Procedia Computer Science 00 (2018) 000–000
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3.4. Termination The segmentation terminates when the entire region of the lateral ventricles are covered fully and pixels values are replaced as new level set. The overall workflow of the entire methodology is depicted in fig. 3 and algorithm used is given below in Algorithm 1.
Algorithm 1 ( Proposed Methodology) 1. Preprocess I(x) Set ROI Position(91,70,72); ROI Size(69,24,39) Resample ROI(69,24,39) ROI(138,48,78) 2. Pre-segmentation (Clustering) No_of_cluster=5; Foreground_cluster=1 3. Initialize Geodesic Active Contour Region_Competition_Force=1.0 Curvature_Force=0.2 function v = GAC(I, v) 4. while( !converged ){ for( i = 0; i < number_of_points; i++ ) { Econt= α* contourDerivative(v, 2); Ecurv= β* contourDerivative(v, 4); Eimg= interp2 (Eimage, v(:,2), v(:,1)); Eimg_norm = weight.k * Eimg / norm (Eimg); Econ = inputForces(); Einternal = Econt + weight.external * Ecurv; Eexternal = weight.external * (Eimg + Econ); v = updateGCM(v, Einternal, Eexternal);} } 5. Terminate.
4. Experimental Results In this section we present the results of proposed methodology and other variants of region growing algorithms in SITK [16]. The image is loaded and preselected seed point (132,142,96) in the left ventricle is utilised. Result of segmentation using three region growing algorithms: 1. Connected Threshold utilises the thresholding values of [100,170], 2. Confidence Connected computes the mean and standard deviation using constant multiplier of 2, and 3. Vector Confidence Connected combines T1 and T2 image and utilises multiplier of 4. Results obtained utilising the parameters are shown in Fig. 4 and the intermediate results of segmentation obtained using proposed methodology are shown in fig. 5.
(a)
(b)
(c)
(d)
(e)
Fig. 4. Comparison of segmentation results of using different scheme. a. original image; b. Connected Threshold; c. Confidence Connected; d. Vector Confidence Connected; e. Proposed method)
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Ankur Biswas, P. Bhattacharya, S.P. Maity / Procedia Computer Science 00 (2018) 000–000 Ankur Biswas et al. / Procedia Computer Science 133 (2018) 561–568
(a)
(b)
(c)
(d)
Fig. 5. Segmentation results on T2_spc_flair_irprep_ sag_p2_iso dicom images and T1 weighted nrrd images (a) original image on load, (b) boundaries detection of lateral ventricles after preprocessing, (c) segmentation of lateral ventricles, (d) final 3d segmented output
4.1. Evaluation of methodology We assess the performance of proposed method using the quality metrics based on guidelines and the evaluation software by [17] that are tabulated in table 1 and presented in fig. 6. The segmentation results for the data of lateral ventricle are compared to combined manual segmentation to obtain reference data that is closer to the ever elusive "ground truth" and given a score for each case. In this paper, two approaches to combining input from multiple observations, majority vote and the Simultaneous Truth and Performance Level Estimation (STAPLE) [18] are utilised.
4.2. Time complexity analysis The proposed method was executed on a Windows PC with an Intel i5-CPU (2.20 GHz) with 4 GB RAM utilising IPython environment and ITK-SNAP. The average segmentation time is 40-50 seconds in addition to 5-10 seconds required for initialisation the pre-segmentation parameters that result in a total 3 dimensional segmentation time of approx 1 minute for two ventricles which is significantly a reduced amount of time compared to 30-40 minutes for manual delineation in 2d.
Table 1. Comparison of different Quantitative segmentation results of the ventricle on MR Image of human brain Type
overlap_
jaccard
dice
percentage
volume_
false_
false_
similarity
negative
positive
Manual1
0.928
0.883
0.938
-0.046
0.083
0.04
Manual2
0.960
0.942
0.97
-0.02
0.039
0.02
Proposed
0.945
0.915
0.955
0.048
0.021
0.067
Ankur Biswas et al. / Procedia Computer Science 133 (2018) 561–568 Ankur Biswas, P. Bhattacharya, S.P. Maity / Procedia Computer Science 00 (2018) 000–000
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Fig. 6. Quantitative segmentation results
5. Conclusion
In this paper, an energy minimised technique is proposed that is able to detect MR images containing lateral ventricles on two different image sets (separately). The accuracy of the algorithm in ventricle segmentation in concert with its low computational complexity demonstrates the efficiency of the proposed method. Another main advantage is its independence from post processing stage as clean up step followed in low level segmentation algorithm. In addition, despite some other methods’ need for initial assumptions, such as number of iterations, multiplier, initial neighborhood radius, our method does not require any such input. However some preprocessing step requires user input. Our result produces a 3D output that will make diagnosis of acute obstructive hydrocephalus and other diseases related to cerebral ventricles that can detected in terms of size and appearance more efficient. This makes the proposed technique much more robust and general than other methods. As an additional study, we also compared the capability and efficacy of two different feature sets – T1-weighted and FLAIR dicom images in segmentation of lateral ventricles. The assessment results specify that the approach considerably out-performs the approach of region growing that require initial parameters to set and guide to higher computational costs. These observations seem to prove that our approach is sufficiently adequate to discriminate all the ventricles from other regions in T2_spc_flair dicom images and T1 weighted nrrd images.
6. Future Scope The proposed methodology is designed for single stage segmentation of lateral ventricle of human brain. The multi-stage process of the proposed method can be extended to segment the entire ventricular system with other anatomical structure of the brain. The methodology can be extended to 3d US to assess the brain structure and other anatomical structure of the body that will help structural diagnosis of Computer aided diseases. The methodology is suitable for detecting tumors in human body that requires 3d view for treatment evaluation and further course of action.
References [1] [2]
[3] [4]
Brant, W.E., Helms, C.A. (2007) “Fundamentals of Diagnostic Radiology”, in Lippincott, Williams & Wilkins (3rd eds), ISBN: 0781761352 Qiu, W., Yuan, J., Kishimoto, J., Ukwatta, E., Fenster, A. (2013) “Lateral Ventricle Segmentation of 3D Pre-term Neonates US Using Convex Optimization”, in Mori K., Sakuma I., Sato Y., Barillot C., Navab N. (eds), Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013. MICCAI 2013. Lecture Notes in Computer Science: 8151. Springer, Berlin, Heidelberg. Ray, S., Hagge, R., Gillen, M., Cerejo, M., Shakeri, S., Beckett, L., Greasby, T., Badawi, R. D. (2008) “Comparison of two-dimensional and three-dimensional iterative watershed segmentation methods in hepatic tumor volumetrics” Medical Physics. 35(12): 5869--5881 Abdul-Khaliq, H., Vogel, M., Lange, P. (2000) “Feasibility of brain volumetric analysis and reconstruction of images by transfontanel three-dimensional ultrasound”, Journal of NeuroImaging: 10(3): 147–150
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[5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15]
[16] [17] [18]
Ankur Biswas et al. / Procedia Computer Science 133 (2018) 561–568 Ankur Biswas, P. Bhattacharya, S.P. Maity / Procedia Computer Science 00 (2018) 000–000
McLean, G., Coombs, P., Sehgal, A., Paul, E., Zamani, L., Gilbertson, T., Ptasznik, R. (2012) “Measurement of the lateral ventricles in the neonatal head: Comparison of 2-d and 3-d techniques”, Ultrasound in Medicine & Biology. Liu, T., Xu, H, Jin, W, et al. (2014) “Medical image segmentation based on a hybrid region-based active contour model, Computational and Mathematical Methods in Medicine”:1-10. Sergi Valverde, Arnau Oliver, et al. (2017) “Automated tissue segmentation of MR brain images in the presence of white matter lesions”, Medical Image Analysis. 35: 446–457 Kass M., Witkin A, and Terzopoulos D. Snakes (1988) “Active contour models”, International Journal of ComputerVision, 1(4):321–331 Osher, Stanley and Sethian, J.A. (1988) “Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations”, Journal of Computational Physics: 79:12–49. Adalsteinsson, D. and. Sethian, J. A. (1995) “A fast level set method for propagating interfaces”. Journal of Computational Physics. 118(2): 269–277. Caselles, V., Kimmel, R., and Sapiro, G. (1997) “Geodesic active contours”, IJCV. 22(1):61–79. Goldenberg, R., Kimmel, R., Rivlin, E. and Rudzsky, M. (2001) “Fast geodesic active contours”, IEEE Trans. on Image Processing, 10(10):1467–1475. Zhukov, L., Bao, Z., Guskov. I., Wood, J., and Breen, D.( 2002) “Dynamic deformable models for 3D MRI heart segmentation”. In Proceedings of SPIE Medical Imaging : 1398–1405 Caselles, V., Kimmel, R., and Sapiro, G. (1997) “Geodesic active contours”, International Journal of Computer Vision. 22(1): 61–79. Paul, A. Yushkevich, Joseph Piven, Heather Cody Hazlett, Rachel Gimpel Smith, Sean Ho, James C. Gee, and Guido Gerig (2006) “User guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability”, Neuroimage, 31(3): 1116-28 Johnson, H.J., Matthew, M. McCormick, Luis Ibanez, and the Insight Software Consortium (2017) “The ITK Software Guide Book 2: Design and Functionality Fourth Edition” Taha, A. and Hanbury, A. (2015) “Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool”, BMC Medical Imaging. Warfield, Simon K., Zou, Kelly H., and William, M. Wells. (2004) “Simultaneous Truth and Performance Level Estimation (STAPLE): An Algorithm for the Validation of Image Segmentation”, IEEE Trans Med Imaging. 23(7): 903–921.
ScienceDirect ScienceDirect Procedia Computer Science 00 (2018) 000–000 Procedia Computer Science 00 (2018) 000–000
Available online at www.sciencedirect.com
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Procedia Computer Science 133 (2018) 561–568
International Conference on Robotics and Smart Manufacturing (RoSMa2018) International Conference on Robotics and Smart Manufacturing (RoSMa2018)
An efficient volumetric segmentation of cerebral lateral ventricles An efficient volumetric segmentation of cerebral lateral ventricles a a
Ankur Biswasaa*, P. Bhattacharyabb, S.P. Maitycc Ankur Biswas *, P. Bhattacharya , S.P. Maity
Department of Computer Science & Engineering, Tripura Institute of Technology, Agartala, Tripura (W), India b Departament ofScience Mathematics, National Institute of Technology, Agartala,Agartala, Tripura(W), India(W), India Department of Computer & Engineering, Tripura Institute of Technology, Tripura c b of I.T., IIEST, Institute Shibpur,ofHowrah, West Agartala, Bengal, India Departament Department of Mathematics, National Technology, Tripura(W), India c Department of I.T., IIEST, Shibpur, Howrah, West Bengal, India
Abstract Abstract
Human brain is a set of four communicating network of ventricles heaving with cerebrospinal fluid (CSF) which is Human brain a setbrain of fourparenchyma. communicating ventricles heaving with cerebrospinal fluid (CSF) is located insideis the An network efficientofsegmentation of cerebral lateral ventricles one which in each located inside braintheparenchyma. An efficient segmentation of cerebral lateral ventricles one inIneach hemisphere can the support study of efficient pathologies for successful conclusion of various diseases. this hemisphere can support theenergy study optimised of efficienttechnique pathologies for successful conclusionofoflateral various diseases. In MR this paper, an efficient and fast for volumetric segmentation ventricles from paper, optimised technique forgeodesic volumetric segmentation lateral ventricles from MR imagesanofefficient human and brainfast is energy proposed which is based on active contours of using level set method. The images of approach human brain is proposed which basedstages: on geodesic active contours level set method.stage, The proposed consists of mainly fouris main 1. Preprocessing stage,using 2. Presegmentation proposed of mainly four main 1. Preprocessing stage, 2. on Presegmentation 3. Contourapproach Evolutionconsists with Energy optimisation stage,stages: 4. Termination stage. Experiments multislice MRIstage, data 3. Contour Evolution with optimisation stage,of4.0.915 Termination Experiments on multislice MRI data obtained dice coefficient of Energy 0.955, jaccard coefficient and otherstage. surface distance measures demonstrate the obtained dice of approach 0.955, jaccard coefficient 0.915 and other surface distance measures demonstrate the advantages of coefficient the proposed in both accuracy of and efficiency. advantages of the proposed approach in both accuracy and efficiency. © 2018 The Authors. Published by Elsevier Ltd. © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) © 2018 The Authors. Published by Elsevier Ltd.committee This is an open access article under thescientific CC BY-NC-ND license Peer-review under responsibility of the of the(https://creativecommons.org/licenses/by-nc-nd/4.0/). International Conference on Robotics and Smart Manufacturing. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/). Keywords: Lateral Ventricle; MR Images; Volumetric; Geodesic active contour and level set methods Keywords: Lateral Ventricle; MR Images; Volumetric; Geodesic active contour and level set methods
1. Introduction 1. Introduction Lateral ventricle segmentation of MRI with no inoculation of any agent and no radiation harm facilitates Lateral aided ventricle segmentation of MRI with noasinoculation agent and no radiation harm facilitates computer disease diagnosis structurally, such narrownessofofany the cerebral aqueduct and foramina implying computer aided disease as narrowness ofAs thecerebrospinal cerebral aqueduct and foramina produced implying they can become blockeddiagnosis by bloodstructurally, following a such haemorrhagic stroke. fluid is continually theythe can become blocked by blood followinga ablockage haemorrhagic stroke. As cerebrospinal fluid continually produced by choroid plexus within the ventricles, of outflow leads to increasingly highis pressure in the lateral by the choroid plexus within the ventricles, a blockage of outflow leads to increasingly high pressure in the lateral
* Corresponding author. Tel.: +91-9436168323. E-mail address:author. [email protected] * Corresponding Tel.: +91-9436168323. E-mail address: [email protected] 1877-0509© 2018 The Authors. Published by Elsevier Ltd. This is an open access under the CC by BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/). 1877-0509© 2018 Thearticle Authors. Published Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/). 1877-0509 © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the International Conference on Robotics and Smart Manufacturing. 10.1016/j.procs.2018.07.084
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Ankur Biswas et al. / Procedia Computer Science 133 (2018) 561–568 Ankur Biswas, P. Bhattacharya, S.P. Maity / Procedia Computer Science 00 (2018) 000–000
ventricles. As a consequence, this commonly leads in turn to acute obstructive hydrocephalus. Features that are supportive in making the conclusion of acute obstructive hydrocephalus includes [1]. Enlargement of the temporal horns (best indicator), Transependymal oedema, or periventricular oozing, may be visible as high T2 signal on MRI or low-density change on CT around the margins of the ventricles. Irritation of the membranes (meningitis) and of the ventricles (ventriculitis) which is caused by infection or the preamble of blood causing haemorrhage can be presumed from lateral ventricle segmentation. In addition, Intraventricular hemorrhage (IVH) in preterm neonates born at less than 35 weeks gestational age and weight less than 1500 gm is a common disease with an occurrence of 12-20% [2] can be monitored by volumetric ventricle segmentation which is more sensitive to longitudinal changes in ventricular volume. It is shown that volumetric measurement offers the best manifestation of the anatomical structure of interest in medical image out of one-dimensional, two-dimensional and volumetric measurements [3] Manual segmentation done in earlier studies in order to quantify 3D ventricle volumes [4,5], is too strenuous and time consuming to make it clinically feasible. It is also exigent due to its irregular shape and indistinct boundaries, even for experts. There is a major contribution of work in favour of segmentation of the left ventricle of human heart, but contribution is still required for the joint segmentation of the both the lateral ventricles of human brain. Semi automatic as well as Region growing algorithms offers an effective result for MR image segmentation [6,7]. Classical active contours such as snakes [8] and level sets [9,10] also proved capable in variety of medical image segmentation problems. Although geodesic active contours has been exposed to be simple, efficient and relatively accurate [11, 12] but has limitations to leak through gaps in object edges in presence of noise or unclear boundaries. 3D snake applied on MRI to segment the left ventricle of heart [13] involve a high level of user interaction to avoid the contour leakage. Thus, accurate and efficient automatic or semi-automatic ventricle segmentation is highly desired in clinical practice. In this paper, we propose an energy optimized technique for volumetric delineating lateral ventricles from MR images of human brain based on geodesic active contours using level set method for the ease of implementation. We show that the introduced challenging energy optimization of the geodesic active contours problem can be globally optimized in Caselles’s formulation to achieve a substantial speed-up in computation. Moreover, we introduce a noise removal technique to prevent the tendency of contours to leak through the gaps. We compare the proposed technique with other region growing algorithms and claim that the proposed technique does not require any post processing stage like clean up step as followed in region growing segmentation algorithms that require filling holes and removing small connected components which reduces the computational complexity to a large extent.
2. Previous Work This section describes the different methods of segmentation of lateral ventricle of human brain structure in MR images using region growing algorithms. One of the region growing methods is Connected Threshold that uses flood fill iterator algorithm in order to decide the criterion for a pixel is to be included in the current region or not from visiting the neighboring pixels. The criterion used by the filter is based on an interval of intensity values provided by the user, lower threshold, Ilower and upper threshold Iupper. The region-growing algorithm includes those pixels whose intensities are within the interval.
I ∈ [ Ilower , Iupper ]
(1)
Where I (x ) is the image and X is the position of the particular neighbor pixel being considered for inclusion in the region. The result of this algorithm is shown in fig.1(b). Second method is the Confidence Connected filter that does not require user input as previous case; instead it computes the mean, µ and standard deviation, σ of intensity values for total pixels currently included in the region. The standard deviation is multiplied with the user input factor ’c’ and is used to define a range around the mean,
Ankur Biswas et al. / Procedia Computer Science 133 (2018) 561–568 Ankur Biswas, P. Bhattacharya, S.P. Maity / Procedia Computer Science 00 (2018) 000–000
(a)
(b)
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(c)
Fig. 1. Segmentation results for cerebral ventricle using a. original image; b. ConnectedThreshold; c. Confidence Connected
µ ± cσ. Neighbor pixels whose intensity values are within the range are included in the region. The iterative process is completed when no more neighbor pixels are left to satisfy the criterion or maximum number of iterations is reached. As a drawback it requires three parameters as user input: multiplier, initial neighbourhood radius and Iterations. The following equation illustrates the inclusion criterion used by this filter,
I ( x ) ∈ [ µ − cσ , µ + cσ ]
(2)
Where I (x ) is the image and X is the position of the particular neighbor pixel being considered for inclusion in the region. The result of this algorithm is shown in fig.1(c).
3. Proposed Methodology This section describes the different stages of the proposed approach. The methodology utilizes the MR image extracted from MIDAS datasets in order the segment the lateral ventricles of human brain using energy optimization in Caselles’s formulation. The overall workflow of the proposed methodology is shown in fig.2. It consists of four main stages:
3.1. Preprocessing. The 3D region of interest (ROI) i.e., the area that contains the lateral ventricle is selected and alienated from the image in order to minimize the computational workload. The alienated ROI is resampled (supersampled) by a fixed factor (2 in proposed method) in x, y and z voxels using Linear interpolation (for better quality). The re-sampling is carried out using Curvature anisotropic diffusion algorithm for reducing the noise from the image and enhancing the boundaries of the ventricles to avoid its tendency to leak out.
Fig. 2. Work flow of the proposed methodology.
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Ankur Biswas, P. Bhattacharya, S.P. Maity / Procedia Computer Science 00 (2018) 000–000 Ankur Biswas et al. / Procedia Computer Science 133 (2018) 561–568
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3.2. Presegmentation In this stage the pre- segmentation parameter Clustering approach is set that requires input for number of cluster and foreground clusters depicting the Cluster probability density plot as shown in fig. 3.
3.3. Contour Evolution with Energy optimization. To initiate the final segmentation task, geodesic contours with optimized energy, region competition force to push the contour inwards or outwards and curvature force to formulate the contour boundary smoother and can prevent from leaking into narrow objects are set on the image. The stiffness of the contour is also taken into consideration. Level set method eases the actual implementation of the contours. The contours are moved by image driven forces to the boundaries of the desired objects. The initialization and execution of it requires the energy function (the weighted combination of internal and external forces) to be optimized. The curve energy, E is given by the following function where C represents the evolving curve, and I represent the image data
E=
∫
(3)
f ( I ) ds
c(s)
where f is decreasing function of the image data. Energy of the curve can be defined by a functional, 1
1
1
0
0
0
E [( C ( s )] = α ∫ E internal ( C ( s )) ds + β ∫ E image ( C ( s )) ds + γ ∫ E external ( C ( s )) ds
(4)
Curve with minimal geodesic length is searched in Caselles’s formulation. 1
Emin ( C ) = ∫ g (| ∇Gσ * I (C ( s )) |) | C ′( s ) | ds
(5)
0
where g : [0,∞] →R+ is a strictly decreasing function such that g(r) →0 as r →∞, e.g.,
g (| ∇ G σ * I ( x , y ) |) =
1 1 + ∇ Gσ * I ( x, y )
(6)
The curve will be attracted towards the region where the weight g (| ∇ G σ * I ( C ( s ) |) is low i.e, the edges of the image. The method is implemented by 3D geodesic active contour method developed by [14] using ITK-SNAP [15]. The segmentation terminates when the entire region of the lateral ventricles are covered fully.
Fig. 3. Cluster Probability density plot as Clustering presegmentation parameters.
Ankur Biswas et al. / Procedia Computer Science 133 (2018) 561–568 Ankur Biswas, P. Bhattacharya, S.P. Maity / Procedia Computer Science 00 (2018) 000–000
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3.4. Termination The segmentation terminates when the entire region of the lateral ventricles are covered fully and pixels values are replaced as new level set. The overall workflow of the entire methodology is depicted in fig. 3 and algorithm used is given below in Algorithm 1.
Algorithm 1 ( Proposed Methodology) 1. Preprocess I(x) Set ROI Position(91,70,72); ROI Size(69,24,39) Resample ROI(69,24,39) ROI(138,48,78) 2. Pre-segmentation (Clustering) No_of_cluster=5; Foreground_cluster=1 3. Initialize Geodesic Active Contour Region_Competition_Force=1.0 Curvature_Force=0.2 function v = GAC(I, v) 4. while( !converged ){ for( i = 0; i < number_of_points; i++ ) { Econt= α* contourDerivative(v, 2); Ecurv= β* contourDerivative(v, 4); Eimg= interp2 (Eimage, v(:,2), v(:,1)); Eimg_norm = weight.k * Eimg / norm (Eimg); Econ = inputForces(); Einternal = Econt + weight.external * Ecurv; Eexternal = weight.external * (Eimg + Econ); v = updateGCM(v, Einternal, Eexternal);} } 5. Terminate.
4. Experimental Results In this section we present the results of proposed methodology and other variants of region growing algorithms in SITK [16]. The image is loaded and preselected seed point (132,142,96) in the left ventricle is utilised. Result of segmentation using three region growing algorithms: 1. Connected Threshold utilises the thresholding values of [100,170], 2. Confidence Connected computes the mean and standard deviation using constant multiplier of 2, and 3. Vector Confidence Connected combines T1 and T2 image and utilises multiplier of 4. Results obtained utilising the parameters are shown in Fig. 4 and the intermediate results of segmentation obtained using proposed methodology are shown in fig. 5.
(a)
(b)
(c)
(d)
(e)
Fig. 4. Comparison of segmentation results of using different scheme. a. original image; b. Connected Threshold; c. Confidence Connected; d. Vector Confidence Connected; e. Proposed method)
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Ankur Biswas, P. Bhattacharya, S.P. Maity / Procedia Computer Science 00 (2018) 000–000 Ankur Biswas et al. / Procedia Computer Science 133 (2018) 561–568
(a)
(b)
(c)
(d)
Fig. 5. Segmentation results on T2_spc_flair_irprep_ sag_p2_iso dicom images and T1 weighted nrrd images (a) original image on load, (b) boundaries detection of lateral ventricles after preprocessing, (c) segmentation of lateral ventricles, (d) final 3d segmented output
4.1. Evaluation of methodology We assess the performance of proposed method using the quality metrics based on guidelines and the evaluation software by [17] that are tabulated in table 1 and presented in fig. 6. The segmentation results for the data of lateral ventricle are compared to combined manual segmentation to obtain reference data that is closer to the ever elusive "ground truth" and given a score for each case. In this paper, two approaches to combining input from multiple observations, majority vote and the Simultaneous Truth and Performance Level Estimation (STAPLE) [18] are utilised.
4.2. Time complexity analysis The proposed method was executed on a Windows PC with an Intel i5-CPU (2.20 GHz) with 4 GB RAM utilising IPython environment and ITK-SNAP. The average segmentation time is 40-50 seconds in addition to 5-10 seconds required for initialisation the pre-segmentation parameters that result in a total 3 dimensional segmentation time of approx 1 minute for two ventricles which is significantly a reduced amount of time compared to 30-40 minutes for manual delineation in 2d.
Table 1. Comparison of different Quantitative segmentation results of the ventricle on MR Image of human brain Type
overlap_
jaccard
dice
percentage
volume_
false_
false_
similarity
negative
positive
Manual1
0.928
0.883
0.938
-0.046
0.083
0.04
Manual2
0.960
0.942
0.97
-0.02
0.039
0.02
Proposed
0.945
0.915
0.955
0.048
0.021
0.067
Ankur Biswas et al. / Procedia Computer Science 133 (2018) 561–568 Ankur Biswas, P. Bhattacharya, S.P. Maity / Procedia Computer Science 00 (2018) 000–000
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Fig. 6. Quantitative segmentation results
5. Conclusion
In this paper, an energy minimised technique is proposed that is able to detect MR images containing lateral ventricles on two different image sets (separately). The accuracy of the algorithm in ventricle segmentation in concert with its low computational complexity demonstrates the efficiency of the proposed method. Another main advantage is its independence from post processing stage as clean up step followed in low level segmentation algorithm. In addition, despite some other methods’ need for initial assumptions, such as number of iterations, multiplier, initial neighborhood radius, our method does not require any such input. However some preprocessing step requires user input. Our result produces a 3D output that will make diagnosis of acute obstructive hydrocephalus and other diseases related to cerebral ventricles that can detected in terms of size and appearance more efficient. This makes the proposed technique much more robust and general than other methods. As an additional study, we also compared the capability and efficacy of two different feature sets – T1-weighted and FLAIR dicom images in segmentation of lateral ventricles. The assessment results specify that the approach considerably out-performs the approach of region growing that require initial parameters to set and guide to higher computational costs. These observations seem to prove that our approach is sufficiently adequate to discriminate all the ventricles from other regions in T2_spc_flair dicom images and T1 weighted nrrd images.
6. Future Scope The proposed methodology is designed for single stage segmentation of lateral ventricle of human brain. The multi-stage process of the proposed method can be extended to segment the entire ventricular system with other anatomical structure of the brain. The methodology can be extended to 3d US to assess the brain structure and other anatomical structure of the body that will help structural diagnosis of Computer aided diseases. The methodology is suitable for detecting tumors in human body that requires 3d view for treatment evaluation and further course of action.
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