Variational Approach for Segmentation of Lung Nodules - CVIP Lab

2011 18th IEEE International Conference on Image Processing

VARIATIONAL APPROACH FOR SEGMENTATION OF LUNG NODULES Amal A. Faraga, Hossam Abdelmunimab, James Grahama , Aly A. Faraga , Salwa Elshazlya Sabry El-Mogycd ,Mohamed El-Mogyc , Robert Falk f, Sahar Al-Jafarye, Hani Mahdib, and Rebecca Milamg a

Computer Vision and Image Processing Laboratory (CVIP Lab), University of Louisville, Louisville, KY 40292 b

Computer & Systems Engineering Department, Faculty of Engineering, Ain Shams University, Cairo, Egypt c

School of Medicine, Mansoura University Egypt d

e

Mogy Scan, Mansoura, Egypt

School of Medicine, Ain Shams University, Cairo, Egypt f

g

Jewish Hospital and 3DR, Louisville, Kentucky

University of Louisville, Department of Radiology

ABSTRACT Lung nodules from low dose CT (LDCT) scans may be used for early detection of lung cancer. However, these nodules vary in size, shape, texture, location, and may suffer from occlusion within the tissue. This paper presents an approach for segmentation of lung nodules detected by a prior step. First, regions around the detected nodules are segmented; using automatic seed point placement levels sets. The outline of the nodule region is further improved using the curvature characteristics of the segmentation boundary. We illustrate the effectiveness of this method for automatic segmentation of the Juxta-pleural nodules. 1. INTRODUCTION Computer-Assisted Diagnosis (CAD) methods lend benefit to the radiologists in early detection and follow-up of doubtful nodules visible in the low dose CT. various machine learning methods have been used for automatic and semi-automatic nodule detection (e.g., [1-3]). Our proposed CAD framework for automatic detection and classification of lung nodules consists of four main steps: 1. Preprocessing of the scans (i.e. acquisition artifacts removal and noise filtering); 2. Segmentation to isolate the lung tissue from the rest of the chest region; 3. Nodule detection to isolate candidate nodules and 4. Nodule classification of the detected nodules into possible pathologies. In this paper we use the level set methods for modeling and segmentation of the lung nodules which appear in low dose computer tomography (LDCT) scans. A pulmonary nodule in radiology is defined as a mass in the lung usually spherical in shape; however, it can be distorted by surrounding anatomical structures such as the pleural surface. In our work, we use the classification by [4] which sorts the nodules into four categories: juxta-pleural where a significant portion of the nodule is connected to the pleural surface; vascularized where significant connection(s) to the neighboring vessels is seen from the nodule, while being located centrally in the lung tissue; wellcircumscribed are nodules centrally located in the lung without vasculature connection; and pleural-tail which is located near the pleural surface but connected by a thin structure. In all of these types there is no limitation on size

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or distribution in the lung tissue. Each of these types of nodules possesses shape characteristics that can be quantified and used in the energy function that controls the propagation of deformable models used to automatically outline the spatial support of the detected nodules. This paper uses mainly the curvature measures, and will use the Juxta-pleural nodules type as a case study. The closest related works to our applications are the following: In [5], automatic detection of lung nodules was performed in the signed distance field of the CT images. The main steps in this work were, first, linearly interpolating the CT images along the axial direction to form an isotropic data set, then a segmentation approach was applied to smooth the lung boundaries, and finally detection of candidate lung nodules by computing the local maximas of signed distances in each subvolume. In [6], a 2D multiscale filter was used to detect candidate nodules, then a region growing approach was used to distinguish between nodules and non-nodules, followed by false positive reduction step. In [7], we described three different methodologies for modeling the lung nodules and obtained templates for each nodule type, which we deployed for nodule detection using normalized cross-correlation (NCC). In [8], we examined false positive reduction using geometric feature descriptors. This paper deals with segmentation of nodules after the false positive step. By segmentation, we mean extraction of the spatial support of the nodules, starting from the positions of candidate nodules. These candidates may be random in a given region of the slice or 3D volume. Variational approaches provide a powerful mechanism for accomplishing our purpose, given prior knowledge about the shape characteristics of candidate nodules. In particular, the curvature properties are different in the four nodule classes under consideration (see Fig. 1). Variational level set approaches for segmentation is preferred over statistical methods followed by morphological operations [9]. The level set segmentation specifies the boundary between lung and background tissues. This boundary has a curvature which is maximized at the nodule region. This technique will be used to minimize false positives and hence enhance the detection rate.

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2011 18th IEEE International Conference on Image Processing

Fig. 1: An ensemble of 16 nodules from the well-circumscribed (upper left), vascular (upper right), juxta-pleural (lower left) and pleural-tail (lower right) nodule types [7].

2. VARIATIONAL LEVEL SET SEGMENTATION Assume a signed distance function that represents a 2D contour. The function inside the contour has a negative sign while outside the contour the sign is positive. Hence, the object is represented by the negative side of the function while background will be in the positive side. The segmentation problem is formulated as a minimization of an energy function. The energy depends on maximizing a posteriori probability of the region represented by the negative side of the level set function. Also, an extra term (the contour arc-length) is necessary to smooth the front evolution and avoid creating islands in the surface and the functional is written as:

where and are the prior probabilities of the object and background respectively. Also, represents the probability density function of the region. The constant is a blending coefficient. The use of the function is for numerical convenience. The first term is designed to maximize the posteriori probability for each class inside the region and at the same time for the other classes outside. Using the EulerLagrange with Gradient Descent flow, the evolution of the front is described in a straightforward manner as follows:

The implicit function for computing the curvature is as follows:

where is the signed distance function. The interpretation of equation 2 is: if a pixel belongs to the object class ( ), the front will expand at this

point otherwise it will shrink. This formulation coincides with the classification decision based on Baye’s rule. The function selects the narrow band points around the front. Solution of the PDEs requires numerical processing at each point of the image or volume which is a time consuming process. Actually the changes of the front are only considered, so that the solution is important at the points near the front. Estimation of the prior probability and the region probability density function is found in [9]. Compared with the work in [10], the proposed methodology depends on theoretical derivation of the problem. The term used in [10], is an empirical term that represents the other side of the function. In the proposed approach, prior probabilities are considered to be adaptive. The other method considers fixed and equal priors. The segmentation results provide two representations of the data, the first is an illumination boundary edge of the nodule on the original textured image and the second is the contour image, where the negative side of the function (i.e. the nodule) is white in color and the positive side of the function (i.e. the surrounding tissue) is black.

Fig. 2: Sample slices from the ELCAP database. The red circles depict where nodule positions.

3. EXPERIMENTAL RESULTS In this paper, we use the Early Lung Cancer Action Program (ELCAP) database [11] for lung nodule modeling. The database contains 50 sets of 1.25 low-dose CT lung scans taken at a single breath-hold with slice thickness 1.25 mm. The locations of the 397 nodules were provided by radiologists; we generated a database of lung nodules categorized into one of the four types described above (i.e. juxta-pleural. well-circumscribed, vascularized and pleuraltail). Since the assumption that the nodule region has already been detected, we use the ground truth marked nodules to avoid sources of errors due to automated detection. Fig. 2 shows example slices from the ELCAP database. The input to our CAD system is the original lung

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2011 18th IEEE International Conference on Image Processing

slices. Segmentation is employed to obtain the tissue which then undergoes template matching using data-driven templates in the detection step. A generic template matching technique using normalized cross-correlation (NCC) as the similarity measure is implemented to detect candidate nodules [7]. The candidate nodules are then segmented and the curvature is computed using the methodology proposed in this paper. In this section we will describe only the results obtained from using the 115 juxta-pleural nodules in our database. The juxta nodule poses a unique characteristic visible by examination, which is the maximum bending usually occurs where the nodule protrudes into the lung tissue, thus the maximum curvature should be within the nodule tip. This theory is examined by segmenting the lung nodule then computing the curvature of the segmented area and obtaining where the maximum curvature value occurs. For the variational segmentation process, level set initial seeds are automatically initialized using the stochastic expectation maximization as described in [9]. Contours then evolve to hit the boundaries between lung tissues and the background. A description of the process is shown in Fig. 3. It is clear that the background tissues have inhomogeneities in addition to noise. The approach is successful to separate lung tissues.

tip. This framework was executed on all 115 nodules. Of these nodules only one completely failed to be segmented. Thus the computations are over 114 nodules. Table 1 shows the capability to detect the maximum curvature point as truly the juxta nodule or not. Perfect detection implies that the maximum curvature computed fell within the tip section of the nodule, while semi-perfect detection refers to the maximum curvature being within the bending region of the nodule. Failures are the cases in which the maximum curvature did not detect the nodule accurately and the ―Good‖ cases are the combination of the perfect and semi-perfect cases. Fig. 5 is a sample of the results of the perfect and semiperfect cases while Fig.6 depicts sample failure results. The results show that overall the maximum curvature of the juxta-pleural nodule is in fact within the central shape bending and our method of segmentation and computation is able to identify and model this phenomenon.

Fig. 4: The variational and geometric methods framework implemented on an example juxta nodule. Table 1: Detection results after computing the curvature of the juxta-pleural nodule.

Fig. 3: Level sets segmentation process; four examples of the juxta-pleural are depicted. An outline box is placed around the contour images to emphasize the nodule region. The third column top image represents the final segmentation boundary edge, while the lower represents the final segmentation contour image.

Fig.4 depicts the process we employed in this paper. As can be seen in the depicted example the maximum curvature value for this slice does in fact occur where the tip of the juxta nodule is located within the tissue. The mesh of the curvature also illustrates that the highest value occurs in one location towards the center, which happens to be the nodule

Categories

Number of Nodules out of 114 total

Percentage

Perfect

63

54%

Semi-Perfect

18

16%

Failures

34

30%

Good

81

70%

The ELCAP data is very low resolution and contrast images which could be sources of errors. Other factors that can affect image quality and thus detection results are the noise artifacts, distortion within the images and overall accuracy of the scans. In the segmentation process if the nodule intensity is very similar to the lung tissue or surrounding pleural surface this can pose constraints on the capability of well-defined nodule segmentation. Also, the overall size of the nodules is

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2011 18th IEEE International Conference on Image Processing

relatively small, 2-5 mm in size which corresponds to approximately 5-10 pixels. Thus the spatial support is a small region in the entire slice.

an automatic technique that detects 70% of the juxta lung nodules via the shape curvature each nodule poses. Future directions are geared towards further experimenting with the other three lung nodules found in our data (i.e. well-circumscribed, pleural-tail and vascularized). A more defined computation of the results after the detection process will be examined. Also, expansion of our nodule database and examine our results on high resolution LDCT images. More prior information about different nodule types including but not limited to shape models, texture information, morphology, etc will be included in the processing. Acknowledgements: This research is funded by the Lung Cancer Research Program. The first author is supported by NASA Graduate Fellowship. REFERENCES

Fig. 5: Sample results of perfect and semi-perfect results. The maximum curvature point(s) is represented by the red circle.

Fig. 6: Sample results of failure results. The maximum curvature point(s) is represented by the red circle.

4. CONCLUSIONS AND FUTURE RESEARCH In this paper we presented a novel method for automatic segmentation and modeling of the lung nodules using variational methods and geometric models. The results presented in this paper were employed on the juxta-pleural nodule which possesses a shape where the maximum bending occurs within the lung tissue enabling the maximum curvature to depict the nodule location. The information obtained from our modeling method provides

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1. S.A. Elshazly Amal A. Farag, S.Y. Elhabian and A.A. Farag. ―Quantification of nodule detection in chest ct: A clinical investigation based on the ELCAP study‖. Proc. of Second International Workshop on Pulmonary Image Processing in conjunction with MICCAI-09. 2. M. Prokop I. Sluimer, A. Schilham and B. van Ginneken. Computer analysis of computed tomography scans of the lung: A survey. IEEE Transactions on Medical Imaging, 25(4):385–405, April 2006. 3. S. Lee, A. Kouzani, E. Hu. ―Automated detection of lung nodules in computed tomography images: a review‖. Machine Vision and Applications. 2010. 4. W. J. Kostis, A.P. Reeves, D. F. Yankelevitz and C. I. Henschke, Three dimensional segmentation and growth-rate estimation of small pulmonary nodules in helical CT images,‖ IEEE Transactions on Medical Imaging, Vol. 22, pp. 1259—1274, 2003. 5. J. Pu, B. Zheng, J. K. Leader, X. H.Wang, and D. Gur. ―An automated CT based lung nodule detection scheme using geometric analysis of signed distance field.‖ Med. Phys., vol. 35, no. 8, pp. 3453–3461, 2008. 6. P. Xiaomin, G. Hongyu, and D. Jianping. ― Computerized Detection of Lung Nodules in CT Images by Use of Multiscale Filters and Geometrical Constraint Region Growing.‖ Bioinformatics and Biomedical Engineering (iCBBE). Pp. 1-4. June 2010. 7. A. Farag, J. Graham, A. Farag, S. Elshazly, R. Falk. ―Parametric and Non-Parametric Nodule Models: Design and Evaluation‖. Proc. of Third International Workshop on Pulmonary Image Processing in conjunction with MICCAI-10, pp. 151-162, 2010. 8. Amal Farag, Shireen Elhabian, James Graham, Aly Farag and Robert Falk, ―Toward Precise Pulmonary Nodule Type Classification.‖ Proc. of the Int. Conference on Medical Image Computing and Computer-Assisted intervention (MICCAI’10), Beijing, China, September 20-25, 2010. 9. H. E. Abd El Munim and A. A. Farag. ―A Shape-Based Segmentation Approach: An Improved Technique Using Level Sets.‖ Tenth IEEE International Conference on Computer Vision (ICCV). Beijing, China. Oct. 17-20, 2005, pp. 930-935. 10.T. Brox and J. Weickert, ―Level Set Based Image Segmentation with Multiple Regions.‖ in Pattern Recognition. Springer LNCS 3175, pp. 415--423, Aug. 2004. 11.ELCAP public lung image database.