Segmentation of Lungs, Fissures, Lobes from Chest CT Images and Analysis

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1 Segmentation of Lungs, Fissures, Lobes from Chest CT Images and Analysis Lavanya. K Department of Instrumentation Technology S.J. College of Engineering Mysore, INDIA M.S.Mallikarjuna Swamy Department of Instrumentation Technology S.J. College of Engineering Mysore, INDIA Abstract Pulmonary Segmentation of lungs from chest CT images is useful in diagnosis of abnormalities and surgery planning. Lung is segmented from chest CT using iterative thresholding method. Further processing is carried out to visualize fissures characteristics and each segment inside the lung. Lung fissures are extracted using canny edge detection. Morphological operations are applied to smooth the lung borders. Lobe segmentation is carried out by edge tracking and region filling algorithms to differentiate the segments. The method is evaluated on chest CT images of normal and abnormal cases. Confusion matrix is computed for determination of pulmonary segmentation accuracy. The segmentation method is automatic and shown good accuracy. Keywords- Automatic Lung Segmentation; Computed Tomography (CT); Fissure Enhancement; Lobe Segmentation. I. INTRODUCTION The lungs are the essential organs of respiration; they are two in number, placed one on either side within the thorax, and separated from each other by the heart and other contents of the mediastinum. The lungs are divided into lobes and the physical boundaries between the lobes are the lobar fissures. The lobes are separately supplied by the first subdivisions of the bronchial tree after the main bronchi. The lobes function relatively independently within the lungs. The lobar fissures are often incomplete, in which case two lobes are (partly) connected [1]. The lobes are further subdivided into segments. Fissure is a physical boundary between two segments. Lung borders are inferior border, posterior border and anterior border. Like borders surfaces are divided into two, they are costal surface and mediastinal surface. The left lung is divided into two lobes, an upper lobe and a lower lobe. In the same way right lung is divided into three lobes, upper, middle and lower lobes. Fig.1 shows anatomy of lungs. Figure 1. Lung Anatomy Computer Tomography (CT) is one of the most efficient medical diagnostic methods and has currently a widespread usage. This imaging modality provides detailed cross sectional images of thin slices of the human body [2]. Computed tomographic scanning can detect early stage asymptomatic lung cancer, lung CT scan lowered the risk of dying of lung cancer. Many lungs diseases requiring radiological support for diagnostic purpose that including tuberculosis, emphysema and lungs cancer could be affectively diagnosed using computer aid. Lung segmentation is an important operation in the analysis and disease detection of lungs [3]. It can be very challenging in circumstances where image artifacts and abnormalities deteriorate the lung boundaries, therefore hindering the lung delineation process. Lungs segmentation is a vital step for computer aided diagnosis. The segmentation can provide richer information than that which exists in the original medical images alone. Some diseases are affected to only one or many segments in a lung. It is ease the identification of that abnormal part in a lung from lung segmentation. This imaging modality provides detailed cross sectional images of thin slices of the human body. Pulmonary nodules are the characterization of the early stage of the lung cancer [4]. In this work automatic lung segmentation for high resolution CT image is carried out. Manual observation of the lobe in CT volume data is not feasible due to the large number ISSN: IJECCT 208

2 of slices in a typical CT scan. Therefore automatic lung segmentation technique is helpful for diagnosis. The pulmonary segmentation accuracy is computed using confusion matrices. A confusion matrix contains information about actual and predicted classifications. The data in confusion matrix is useful in evaluating pulmonary segmentation accuracy. II. SEGMENTATION TECHNIQUES J.M. Kuhnigk et. al. [5] proposed lobe segmentation using watershed algorithm assumes the presence of a segmentation mask for each lung. Lung segmentation procedure is fully automated and therefore it avoids the user input necessary for the lobar segmentation. Mithun N. et. al. [6] used an adaptive thresholding method which performs the segmentation of the lungs by comparing the curvature of the lung boundary to that of the ribs. This technique improves the fixed threshold based approaches to include lung boundary curvature features. Sluimer et. al. [7] used the boundary curvature to estimate in a wide variety of image segmentation and analysis. Clustering algorithms essentially perform without the use of training data. Hebert T. J. et. al. [8] and Priebe C. E. et. al. [9] introduced clustering methods train themselves, using the available data. Fosgate C.H. et. al. [10] used a various clustering algorithms that do not directly incorporate spatial modeling and are sensitive to noise and intensity in homogeneities. This lack of spatial modeling, however, can provide significant advantages for fast computation. Li S. Z. et. al. [11] used Markov random model for spatial interactions between neighboring or nearby pixels. These local correlations provide a mechanism for modeling a variety of image properties. In lung segmentation, they are typically used because most pixels belong to the same class as their neighboring pixels. This property is useful for fissure enhancement in lung segmentation. Kubo M. et al. [12] used a fissure sweep technique which finds the fissure regions in the preprocessed CT lung images. The lobar fissures separate the lung lobes where no major vascular or bronchial trees cross. This signifies that in the pre-processed binary images, fissures are represented by large amounts of air or space extending from the middle to the lateral side of the lungs. The fissure sweep technique uses this knowledge to coarsely define void regions where fissures could be present. III. PROPOSED METHOD Data set description: The clinical lung scan CT images are obtained from hospitals. The Chest CT image sizes of 677 x 598 pixels are processed. Chest CT images are segmented to using Iterative threshold to obtain lung. Further fissures are enhanced using canny edge detection followed by morphological operations. Lobe segmentation is carried out using edge tracking and region filling operations. Confusion matrix is computed. Finally based on the values obtained in confusion matrix the accuracy of pulmonary segmentation is analyzed. The image processing steps are shown in Fig. 2. Figure 2. Steps of Lung Segmentation and Analysis A. Lung Segmentation Lung segmentation consists of three algorithms like iterative threshold; labeling and image crop Iterative threshold is applied in lung segmentation for finding the region of interest in the image. From trachea extract the lung part from this method. Iterative threshold algorithm [13] consists of following steps: 1. An initial threshold (T) is chosen; this can be done randomly or according to any other method desired. 2. The image is segmented into object and background pixels as described above, creating two sets G 1 = {f (m, n): f (m, n) >T} (object pixels) G 2 = {f (m, n): f (m, n) T} (background pixels) Where f (m, n) is the value of the pixel located in the mth column, nth row. 3. The average of each set is computed m 1 = average value of G 1 m 2 = average value of G 2 4. A new threshold is created that is the average of m 1 and m 2 T = (m 1 +m 2 )/2 Chest CT Image Lung Segmentation Fissure Enhancement Lobe Segmentation Compute Confusion matrix Accuracy Analysis 5. Go back to step two, now using the new threshold computed in step four, keep repeating until the new threshold matches the one before it (i.e. until convergence has been reached). The normal and abnormal chest CT images used as input are shown in Fig. 3(a) and (b). Fig. 3 shows chest CT images. ISSN: IJECCT 209

3 namely dilation, closing and thinning. The Canny edge detector [14] is an edge detection operator that uses a multi-stage algorithm to detect a wide range of edges in images. The purpose of edge detection in general is to significantly reduce the amount of data in an image, while preserving the structural properties to be used for further image processing. Edge detected image is shown in Fig. 5. (a) Normal Figure 5. Edge Detected Image Fissure enhanced image shown in Fig. 6. Thinning is used to remove selected foreground pixels from binary images. (b) Abnormal Figure 3. Chest CT images Figure 6. Fissure Enhanced Image Figure 4. Lung Segmented Image After iterative threshoding algorithm, labeling is done. Labeling produces only left and right lung from trachea. Binary image is obtained using logical operation. This binary image is used to mask the background portion of the input image. Finally, Image crop operation removes the background and retains the pixel intensity of lung portion as shown Fig. 4. B. Fissure Enhancement Fissure enhancement consists of canny edge detection algorithm and followed by three morphological operations Dilation is one of the basic operators in morphology [15]. It is typically applied to binary images; the basic effect of the operator on a binary image is to gradually enlarge the boundaries of regions of foreground pixels. Thus areas of foreground pixels grow in size while holes within those regions become smaller. Closing tends to enlarge the boundaries of foreground regions in an image. Lobe segmentation consists of two algorithms; one is edge tracking and another one is region filling. Edges are good features for tracking. Their long extent means that they can be matched between frames with good reliability, and can provide a more accurate estimate of the motion than corner features. Along each edge in the region segmentation, tracking nodes are initialized at 10 pixel intervals [16]. Edge tracked image is shown in Fig. 7. ISSN: IJECCT 210

4 Figure 7. Edge Tracked Image Figure 8. Pulmonary Segmented Lung Image (Normal) The visualization of segments in edge tracked image is further improved with region filling. Region filling algorithm smoothes link of structures across holes. Their approach has a clear advantage in that it is designed to connect curved structures by the explicit generation of subjective contours, over which textural structures are propagated. IV. RESULT The pulmonary segments are a reference system for radiologists, pulmonologists, and surgeons to indicate the position of lesions in the lungs. This allows a lobe based CT parameter extraction and thus a more accurate prediction of post operative lung function in case of a lobar resection, which is the standard treatment for early stage lung cancer. Pulmonary segmented output images for normal and diseased image shown in Fig. 8 and Fig. 9 respectively. Compute confusion matrix by comparing normal lung image with diseased lung image. The different rows of matrix represent the following The accuracy (AC) is the proportion of the total number of predictions that were correct. The true positive rate (TP) is the proportion of positive cases that were correctly identified. The false positive rate (FP) is the proportion of negatives cases that were incorrectly classified as positive. The true negative rate (TN) is defined as the proportion of negatives cases that were classified correctly. The false negative rate (FN) is the proportion of positives cases that were incorrectly classified as negative. Figure 9. Pulmonary Segmented Lung Image(Diseased) A confusion matrix for the normal and diseased lung images are computed and shown in Table I and Table II. The rows represent the ground truth and a column represents the number of segments in the lung. In table, 0 represents the particular segment is not considered and 1 represents the consideration of the segment. TABLE I. CONFUSION MATRIX FOR NORMAL RIGHT LUNG IMAGE FP FN TP TN TABLE II. CONFUSION MATRIX FOR DISEASED RIGHT LUNG IMAGE FP FN TP TN ISSN: IJECCT 211

5 Automatic segmentation accuracy is determined using the values in confusion matrix. The accuracy for pulmonary segmentation of two output images obtained is calculated using equation: TABLE III. AC = TP + TN 100. (1) (TP + TN + FP + FN) ACCURACY OF PULMONARY SEGMENTATION FOR DIFFERENT CASES case Accuracy % % % % % Pulmonary segmentation accuracy calculated for different diseased lung subjects is shown in Table III. It is found that accuracy is more than 80%. V. CONCLUSION Automatic lung segmentation starts from different combined image segmentation and processing techniques. It starts with iterative thresholding and then enhances the fissure inside the lungs. The accuracy of this automatic pulmonary segmentation is computed and indicated using confusion matrices. Fissure enhancement is useful for finding airway obstruction and fissure characteristics for treatment of certain lung diseases. The lobe segmentation is useful for finding certain abnormalities in the lung. Some pathologic diseases are affected to only one or several segment. In such cases this image processing technique helpful in identification of diseased part of the lung. This information is also useful for surgery planning. The limitation of this technique is, fissure enhancement is accurate for high resolution CT image but results are not satisfactory with low resolution CT images. Automatic pulmonary lung segmentation is useful in diagnosis and treatment lung disease. Achenbach, New tools for computer assistance in thoracic CT part 1. Functional analysis of lungs, lung lobes and bronchopulmonary segments, RadioGraphics, vol. 25, pp , [6] Mithun N. Prasad, Matthew S. Brown, Shama Ahmad, Fereidoun Abtin, Jared Allen, Irene da Costa, Hyun J. Kim, Michael F. McNitt-Gray, Jonathan G. Goldin, Automatic segmentation of lung parenchyma in the presence of diseases based on curvature of ribs, Academic Radiology, vol. 15, No 9, pp , September [7] C. Sluimer, M. Prokop, and B. Van Ginneken, Toward automated segmentation of the pathological lung in CT, IEEE Tran. on Medical Imaging, 24(8), pp , [8] Hebert T. J., Fast iterative segmentation of high resolution medical images, IEEE Tran. Nuclear Science, 44, pp , [9] Priebe C E, Marchette D J, Rogers G W, Segmentation of random fields via borrowed strength density estimation, IEEE Trans. Pattern Analysis Machine Intelligence, vol. 19, pp , [10] Fosgate C H, Krim H, Irving W W, Karl W C, Willsky AS., Multiscale segmentation and anomaly enhancement of SAR imagery, IEEE Tran. Image Processing, vol. 6, pp 7 20, [11] Stan Z. Li, Markov Random Field Modeling in Computer Vision, Springer-Verlag New York Berlin, [12] Kubo M., Niki N. Eguchi K,.Kaneko M, and Kusumoto M., Moriyama, N.; Omatsu, H.; Kakinuma, R.; Nishiyama, H.; Mori, K.; Yamaguchi N., Extraction of pulmonary fissures from thin-section CT images using calculation of surface-curvatures and morphology filters, Proc. IEEE Int. Conf. Image Processing,2000, pp [13] Amir Beck and Marc Teboulle, A fast iterative shrinkage-thresholding algorithm for linear inverse problems, SIAM Journal on Imaging Sciences, vol. 2, No. 1, pp , Jan 2009 [14] John Canny, A computational approach to edge detection, IEEE Tran. on Pattern Analysis and Machine Intelligence, vol. 8, pp , Nov [15] R. M. Haralick and L.G. Shapiro, Computer and Robot Vision, vol 1, Addison Wesley Publishing Company, [16] P. Dempster, N. M. Laird and D. B. Rdin, Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society, vol. 39, pp 1 38, REFERENCES [1] Mathias Prokop, and Michael Galanski, Spiral and Multislice Computed Tomography of the Body, New York, NY: Thieme, ISBN [2] Eva M. van Rikxoort, Bartjan de Hoop, Saskia van de Vorst, Mathias Prokop, and Bram van Ginneken, Automatic Segmentation of pulmonary segments from volumetric chest CT scans, IEEE Tran. on Medical Imaging, vol. 28, No. 4, pp , April [3] C. D. Laros, J. M. Van den Bosch, C. J. Westermann, P. G. Bergstein, R. G. J. Vanderschueren, and P.J. Knaepen, Resection of more than 10 lung segments; a 30-year survey of 30 bronchiectatic patients, Journal of Thoracic Cardiovascular Surgery., vol. 95, no. 1, pp , [4] Brutsche M. H, Spiliopoulos A, Bolliger CT, Licker M, Frey JG, Tschopp JM, Exercise capacity and extent of resection as predictors of surgical risk in lung cancer, European Respiratory Journal, 15, pp , [5] J.M. Kuhnigk, V. Dicken, S. Zidowitz, L. Bornemann, B. Kuemmerlen, S. Krass, H.O. Peitgen, S.Yuval, H.H. Jend, W. S. Rau and T. ISSN: IJECCT 212