The Effect of Segmentation Method on the Performance of Point Based Registration of Intra-Ultrasound with Pre-MR Images

Transcription

1 The Effect of Segmentation Method on the Performance of Point Based Registration of Intra-Ultrasound with Pre-MR Images P.Farnia, A. Ahmadian,*, A. Khoshnevisan Tehran University of Medical Sciences, Biomedical Systems & Medical Physics Group, & Research Center for Biomedical Technology and Robotics (RCBTR), Tehran, Iran Tehran University of Medical Sciences & Brain and Spinal Injuries Repair Research Center (BASIR), Tehran, Iran. * Corresponding author; ahmadian@sina.tums.ac.ir Abstract - Intra-operative Ultrasound imaging as a real time imaging device is found very applicable for intraoperative updates of patient data in neurosurgery. One of the main challenges here is the accuracy of intra and preoperative image registration which is highly influenced by the level of speckle reduction and the accuracy of segmentation method. In this paper the effect of segmentation method on the accuracy of point-based registration of intraoperative US image with preoperative MR image using Iterative Closest Point (ICP) algorithm and Coherent Point Drift (CPD) are considered. To perform this study, a Poly Vinyl Alcohol-Cryogel brain phantom is made that allows simulating the brain deformation. As the results showed CPD algorithm is found more robust than ICP in the presence of outliers. In addition, the role of Chan-Vese as a nonparametric active contour based segmentation of US images has shown a reasonable improvement in the performance of the conventional ICP and CPD in order of 45% and 3%, respectively. Keywords: Brain shift, intra-operative ultrasound, Iterative Closest points, coherent point drift and Chan-Vese segmentation. Introduction In recent years image guided surgery (IGS) system has become a must for conducting complex surgical procedures such as brain surgeries. The key point here is the accuracy of intra-operative and pre-operative images registration which has direct impact on the final target registration error over anatomical point. A major source of error in IGS system is tissue movement and deformation such as brain shift which invalidates the pre-operative image coordinates. This deformation in brain is a consequence of various combined factors: gravity, leakage of cerebro-spinal fluid (CSF), retraction and resection of tissue, edema, swelling of brain structures, and administration of drugs [-3]. A cure to this problem would be to obtain the new deformed coordination of patient image dataset using intraoperative imaging system such as MRI, CT and ultrasound. Intra-operative MRI scanners can provide the surgeon with updated anatomical images several times during a surgery procedure. It can produce excellent images of the brain s anatomical structures and is being used to study brain deformation. Therefore intra-operative MRI can be a useful tool for correction of brain shift, but it has its limitations. A dedicated intra-operative MRI system requires a substantial investment for the scanner and equipping the operating room (OR) with MR-compatible instruments [4, 5]. The CT images suffer from lower soft tissue contrast than MRI, and are therefore less useful for brain surgery. Other disadvantages of intra-operative CT imaging are the radiation dose to the patient which limits the number and duration of the scans, and also the physical space occupied by the scanner in the OR [6]. In recent years intra-operative ultrasound imaging has found very applicable in minimally invasive surgeries. It is being as non- ionized, costless, OR equipment compatibility, real time and portable system [7-9]. Unfortunately, the quality of ultrasound images is highly affected by speckle noise and outliers. The main goal of this paper is to consider the effect of applying suitable segmentation method on the accuracy of registration of intra-operative US image with pre-operative MRI data set to be used directly to measure the brain deformation. Brain deformation is a non-rigid problem and many algorithms exist for this non-linear and multimodal registration. Intensity based registration uses similarity measures like Mutual Information which has been extensively used in multimodal registration. But due to speckle noises, scale differentiation between MR and US images and their different resolution, this type of similarity measures is not suitable for US-MRI registration. In this work we have concentrated on feature-based approaches leading to point based registration of multimodal images that are suitable for nonrigid registration []. Extraction of features, transforms gray scale image into dense sets of discrete points. A point set can be extracted from an image based on the locations or orientation of corners, boundary points, edge points or salient regions. These

2 points can represent geometric and intensity properties of an image. Point based registration iteratively find correspondences between points and then estimate the transformation parameters based on these correspondences [-3]. However, it should be noted that point matching problem can be quite difficult because of various factors. The two main factors are as follows:. Existence of noise, outliers, and missing points in point set of image due to image acquisition and specially feature extraction.. High dimensionality of point sets. To deal with the first problem, it is proposed to apply a denoising filter before feature extraction followed by segmentation can be useful for reducing outliers and missing points. First, ultrasound images were de-noised with SRAD filter as a diffusion filter. Then, Chan-Vese (CV) method as a nonparametric active contour was used for segmentation of denoised US images before applying canny edge detector. Prefiltering the image with CV led to extra features not being detected by the proceeding canny applied. There are many algorithms in the literature that have been used for point based registration [, 4]. One of the most common algorithms is the Iterative Closet Point (ICP) algorithm. ICP is the most popular among others due to its simplicity and low computational complexity. Despite high speed convergence of ICP algorithm its performance is highly sensitive to the initial relative. To overcome such limitations of conventional ICP many point based algorithms have been introduced since the introduction of ICP by Besl and McKay [5]. One of the most Precise non-rigid algorithms is the well known probabilistic registration method so called Coherent Point Drift (CPD) [4]. In this study we compared the performance of ICP and CPD for brain shift calculation applied on a brain phantom. The results are compared in terms of the registration accuracy. Then, the effect of segmentation methods using Canny with and without CV method on the performance of the conventional and improved ICP is studied. The paper is organized as follows. In Section and. describes the designed phantom and its data acquisition. Section. and.3 are dealt with de-noising method and segmentation of US images. Section.4 describes the improved ICP. Section 3 is dedicated to experimental results and conclusions are written in Section 4. Method and Material. Phantom Preparation and Imaging To evaluate and validate the registration and de-noising techniques in a condition close to a real clinical setting, a phantom study is carried out in this work. The phantom was made of Poly vinyl alcohol Cryogel (PVA-C) [6]. This material is presented as a tissue-mimicking material and suitable for application in MR and ultrasound imaging. In our phantom we used PVA-C % for brain tissue and PVA-C 5% for the ventricle simulation. We placed some tubes with 3mm diameter for mimicking vessels and 3Foley catheter inserted in different depth and direction. This catheter can be sued to deform the phantom in different manner (Fig.). The phantom was then scanned using a Siemens 3T scanner using a standard T weighted protocol with TR=ms, TE=4.9 ms and mm slice thickness. The plastic tubes in phantom were first filled with water to acquire MR images. Then the phantom was deformed by filling balloons of Foley catheters. Each balloon of 3 Foley catheters was filled once with ml water and again with ml water, thereby scanning the phantom in 6 situations. The phantom can be deformed in an elastic non-linear manner by inflating the catheter balloon (Fig). Ultrasound images were acquired using HS-, Honda Medical Systems ultrasound machine with a multi-frequency linear and convex probe with central frequency 5 and 3.5 MHZ, respectively. Fig. The phantom that was made from PVA-C (c) Fig.. MR image before deformation in coronal view. Corresponding MRI images after deformation. (c) Corresponding US image after deformation

3 . US Image De-noising Despite ultrasound advantages as a diagnostic imaging system, it suffers from speckle noise that restricts the quality of ultrasound images. Speckles are pseudo noise changes in contrast of images which are known as noise. These noises appear as an intrinsic phenomenon, due to random constructive and destructive interference between coherent echoes. There have been a variety of techniques for speckle reduction in literature. For noisy images with speckles, the gradient of the noise is almost comparable to gradient of the features. So it seems filters amplify the noise of image or increase the stair effect near the smoothed edges sould be rejected. In this paper, we focused on single scale methods in time domain and used Speckle Reduction Anisotropic Diffusion (SRAD) filter. This filter is designed sensitive to edges with an instantaneous coefficient of variation [7]. it is an adaptive method and it does not utilize hard thresholding to alter its performance in homogeneous regions. Also It not only preserves edges but also enhances edges by inhibiting diffusion across edges and allowing diffusion on either side of the edge. This filter is found efficient for different level of speckle noise and it is modeled with below diffusion coefficients: () c( q) = q q + q ( + q ) I I ( ) ( ) () q = I 4 I I ( + ) 4 I q t) = q exp( ρ ) (3) ( t.3 5BUS Image Segmentation After de-noising US images for correct extraction of features an efficient segmentation method is required. In recent decade non-parametric or geometric active contour based on level set function concept has been used extensively for image segmentation. In this method a curve is represented as level sets of two dimensional scalar functions. Level set function evolves until zero level set of it close to real boundaries. Level sets do not require any parameterization of the evolving contour and also it does not need initial boundary which is including our objects. The CV method [8] is known as a useful non-parametric active contour technique without edge. This model is not based on an edge function to stop the evolving curve on the desired boundary. Also it can detect objects whose boundaries are not necessarily defined by gradient. The considerable point in CV is that the position of the initial curve can be anywhere in the image, and it does not necessarily surround the desired objects to be detected. This method can be detected more than one object and boundaries in an image. Therefore it is useful for this application. The CV method was modeled as below: inf c, c, C + λ F( c, c I c + λ I Regarding to fixed φ and minimum F, c,c were acquired as below: I H ( ϕ) I ( H ( ϕ)) (5) c( ϕ) =, c ( ϕ) = H ( ϕ) ( H ( ϕ)) (4) We used a medium circular mask, and set to one and the default value of µ and ʋ is. and zero, respectively. After implementation of CV method we used canny edge detector for edge detection of US images. This method was compared with using canny method alone..4 6BICP, C) = µ C + ν Area( inside( C)) inside( C) outside( C) The ICP algorithm is used for the alignment of two set of points in two spaces. This algorithm iteratively assigns correspondences between points of two clouds based on the closest distance criterion to find the least-square transformation relating the two point sets. If the exact correspondences of the two data sets are known, then the exact transformation can be found. The algorithm will converge rapidly but it may converge towards local minima instead of the global minimum. The ICP requires that the initial pose of the two point sets are adequately close, which is not always possible, especially in non-rigid transformation. The error can be expressed as below equation: E = N i= R + T (6) p q i i.5 7BCoherent Point Drift (CPD) The CPD method was introduced as a robust probabilistic multidimensional method for non-rigid point set registration. It considers the alignment of two point sets as probability density estimation, with motion coherence constraint. There is no explicit assumption about transformation model. Motion coherence theory was proposed by Yuiile [9]. The concept of this issue is that points close to another tend to move coherently as group which preserves their topological structure. In this method one point set represents the Gaussian Mixture Model (GMM) centroids and the other represents the data points. GMM is fitted to the source point set, whose c λ λ

4 Gaussian centroids are initialized from the points in the T target point set that expressed as y = ( y,..., ). This algorithm iteratively fits the GMM centroids by maximizing the likelihood and it finds the posterior probabilities of centroids. Y is the initial centroid position and it defines a continuous velocity function υ for the template point set such that the current position of centroids is defined as: Y= υ (Y ) + Y (7) The Bayes theorem is used to find the parameters Y by maximizing the posteriori probability, or minimizing the energy function: E CPD y m N M n m λ σ ( Y ) = log e + φ( Y ) (8) n= m= x y ϕ(y) is a function that is related to the smoothness of the motion. The process of adapting the GMMs from their initial positions to their final positions as a temporal motion process was considered instead of model. Also motion coherence constraint penalizes derivatives of all orders of the velocity while the models such as thin-plate spline models only penalize the second order derivative. Three parameters are applied in non-rigid CPD registration. The best parameters of CPD method were obtained and proposed for this application. One of them reflects our assumption about the amount of noise in the point sets which is set about.3 in this work. The two other parameters reflect the amount of smoothness of regularization. One of them defines the model of smoothness regularizer which is known as the width of smoothing Gaussian filter and it is set to. Another Parameter represents the trade-off between the goodness of maximum likelihood fit and regularization and set to 3. Therefore, CPD method simultaneously finds both the transformation and the correspondence between two point sets without making any prior assumption on the non-rigid transformation model except motion coherence constraint. Finally, we used the fast CPD implementation using Fast Gauss Transform (FGT) to reduce the computational complexity of the method as low as linear. Fig.3. a) Sagittal view of original Us image, b) de-noised US image with SRAD filter Fig.4. a) Sagittal view of de-noised US image, b) segmented US image with canny edge detector In Fig.5 the same US image was segmented with CV algorithm and then by Canny edge detector. For evaluation of point-based registration, we used the RMSE between the corresponding points after the registration as an error measure in two algorithms. 3. Results As shown in Fig.3, SRAD filter was applied for de-noising of US images. As it shows SRAD preserves main edges and reduces speckle noise in US image. In Fig.4 de-noised US image was segmented by canny edge detector. As expected, extra features were detected by canny method. (c) (d) Fig.5. a) Sagittal view of de-noised US image, b) Selected mask of CV method, c) Segmented image with CV method, d) Segmented US image with CV+ Canny edge detector

5 Table.. The effect of segmentation method on the ICP and CPD Segmented image with Canny Segmented image with CV+ Canny Method ICP CPD ICP CPD RMSE (mean± std) mm.77±..3±.7.5±.6.9±.4 We considered ICP and CPD experiments, separately. As shown in Table., The ICP and the CPD are compared to each other in terms of RMS error. Number of iterations in ICP and CPD were and, respectively. Also to evaluate the effect of segmentation method on the performance of point based methods, we applied the registration algorithms on segmented images with canny edge detector with and without using CV method. After using suitable de-noising and segmentation method such as SRAD and CV, the result in point based algorithms was improved in the accuracy of registration. It should be noted that we used 3 data of phantom and conducting 5 runs for registration algorithms. 3 Conclusion The utilization of ultrasound images for intra-operative localization has become very applicable for image guided surgery system. In this work it was shown that the outliers presented in US images can impact the accuracy of registration algorithm as a challenging problem in point to pint registration methods. As a cure, SRAD filter was recommended and implemented as an efficient de-noising filter for US images. This filter proved to be a good filter in simultaneous enhancement of edges and noise reduction o due to considering direction of edges. As the main goal of this paper the effect of segmentation method on the accuracy of registration algorithms were considered here. The non-parametric active contour Chan- Vese method was implemented before canny edge detector and the result was compared to canny without using CV. Pre-filtering the image with CV, followed by applying canny filter proved to perform better than using canny filter alone. This is due to the property of CV to reduce outliers by transforming the image into a binary image. This in turn results extra features not being detected by the proceeding canny applied. For point based registration of US and MR images ICP and CPD algorithms were used. Using Canny with CV instead of Canny itself improves the performance of the ICP and CPD about 45% and 3%, respectively. Also the CPD method in terms of accuracy of registration was improved into ICP about 4%. In CPD, the uniform distribution is used to account noise and outliers which led CPD to be more robust than ICP in the presence of noise and outliers. Therefore suitable segmentation method has more impacts on ICP performance. References [] D. W. Roberts, et al., "Intraoperative brain shift and deformation: a quantitative analysis of cortical displacement in 8 cases," Neurosurgery, vol. 43, p. 749, 998. [] A. Roche, et al., "Generalized correlation ratio for rigid registration of 3D ultrasound with MR images,", pp. 3-. [3] I. Reinertsen, et al., "Validation of vessel-based registration for correction of brain shift," Medical Image Analysis, vol., pp , 7. [4] T. Moriarty, et al., "Magnetic resonance imaging therapy. Intraoperative MR imaging," Neurosurgery Clinics of North America, vol. 7, p. 33, 996. [5] T. HERE, "Superconducting Open-Configuration MR Imaging System for Image-guided Therapy," Radiology, vol. 95, pp , 995. [6] P. Grunert, et al., "Basic principles and clinical applications of neuronavigation and intraoperative computed tomography," Computer Aided Surgery, vol. 3, pp , 998. [7] L. Auer and V. Van Velthoven, "Intraoperative ultrasound (US) imaging. Comparison of pathomorphological findings in US and CT," Acta neurochirurgica, vol. 4, pp , 99. [8] R. M. Comeau, et al., "Intraoperative ultrasound for guidance and tissue shift correction in imageguided neurosurgery," Medical Physics, vol. 7, p. 787,. [9] J. Sutcliffe, "The value of intraoperative ultrasound in neurosurgery," British journal of neurosurgery, vol. 5, pp , 99. [] R. McLaughlin, et al., "A comparison of D-3D intensity-based registration and feature-based registration for neurointerventions," Medical Image Computing and Computer-Assisted Intervention MICCAI, pp ,.

6 [] M. A. Audette, et al., "An algorithmic overview of surface registration techniques for medical imaging," Medical Image Analysis, vol. 4, pp. - 7,. [] G. L. Scott "An algorithm for associating the features of two images," Proceedings: Biological Sciences, pp. -6, 99. [3] N. A. Parra, "Rigid and non-rigid point-based medical image registration," 9. [4] A. Myronenko and X. Song, "Point set registration: coherent point drift," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 3, pp. 6-75,. [5] P. J. Besl and N. D. McKay, "A method for registration of 3-D shapes," IEEE Transactions on pattern analysis and machine intelligence, vol. 4, pp , 99. [6] H. M. Kjer and J. Wilm, "Evaluation of surface registration algorithms for PET motion correction,". [7] Y. Yu and S. T. Acton, "Speckle reducing anisotropic diffusion," Image Processing, IEEE Transactions on, vol., pp. 6-7,. [8] Chan, T. F. and L. A. Vese (). "Active contours without edges." Image Processing, IEEE Transactions on (): [9] A. L. Yuille and N. M. Grzywacz, "The motion coherence theory," 988, pp