Detection and Measurement of Hilar Region in Chest Radiograph

Transcription

1 Detection and Measurement of Hilar Region in Chest Radiograph Mira Park 1, Jesse S. Jin 1,2 and Laurence S. Wilson 3 1. School of Computer Science and Engineering, University of NSW 2. University of Sydney, School of Information Technologies, 3. Telecommunications and Industrial Physics, CSIRO mirap@cse.unsw.edu.au Abstract This paper presents a method to detect and measure the hilar region of chest radiographs. The reason why there is no suggested method to detect hilar region is that the hilar region does not have a clear shape or specific texture. We use a very simple but very effective method to detect the location and size of the hilar region of chest radiographs. Key words: Chest Xray, Hila, 4 ways with 10 neighbours connectivity, Hemi_spherical cavity 1 Introduction. Medical image analysis is a complex task in which a human expert makes extensive use of the knowledge of anatomy and imaging techniques. Specially, the automatic segmentation of chest radiographs is challenging problem from a computer vision point of view. Because there are large anatomical variations from person to person and the most important problem is that radiographs are projection images and thus contain superimposed structures. To interpret the chest radiographs, the radiologists often employ local properties like perceived intensity, uniformity, roughness, regularity, directionality, coarseness, smoothness and granulation (Anne 2000). For detection and characterization of these properties in radiological images, computer-aided diagnosis (CAD) schemes have been developed. CAD have usually included: Extraction of organs such as lung, heart, diaphragm, mediastinal, etc., using pixel classification (Fu et al 1975, Kim et al 1989), rule-based (Bram and Bart 2000) and knowledge-based (Matthew et al 1998, Park et al, 2001) methods. Detection of the rib cage using Hough transformation (Tetsuo et al 1995), a method based on modeling correspondence (Wechsler and Sklansky 1975) and a method based on the gradient gray_level (Zhanjun et al 1995) Searching for isolated abnormal patterns (Zhu et al 1996, Tsuneo et al 1991, Takeshi 1997, Matthew and Lawrence 1998, Chen et al 1988, Doi et al 1997) However, there is no current method to detect the hila region since it does not have any shape or specific texture. It is important to detect and measure the hilar region because enlargement of the hilar regions may be due to hilar gland lymphadenopathy, most commonly related to viral pneumonia or chronic infection e.g., in cystic fibrosis (Arthur 2000). In this paper, we present a method to detect and measure the hilar region of a chest radiograph using very simple methods including threshold and semi-circle. The layout of this paper is as follows. In section 2, we will show the lung extraction module. In section 3, we will show the detection of the clavicle. In section 4, we will explain the detection and measurement of the hilar region and in section 5 we will detail our experiments. Section 6 is our conclusion. Figure1. Extraction of lung field Copyright 2003, Australian Computer Society, Inc. This paper appeared at the Pan-Sydney Area Workshop on Visual Information Processing (VIP2002), Sydney, Australia. Conferences in Research and Practice in Information Technology, Vol. 22. J. S. Jin, P. Eades, D. D. Feng, H. Yan, Eds. Reproduction for academic, not-for profit purposes permitted provided this text is included. 2 Extraction of lung field The lung field is extracted to limit the region to be processed. The knowledge based lung field extraction method, developed by Matthew et al (1998) and extended by Park et al (2001), is applied to extract the lung field (see Figure 1). The system is a pixel-based feature extraction with a powerful, object-centered, knowledgebase inferencing system based on anatomical structures

2 v(x i,y j ) ry (x 0,y 0 ) rx Inspiration Chest Xray V(x,y) = 0 hemi_spherical cavity 3D view hemi_spherical cavity 2D view Expiration Chest Xray Figure 2. Hemi-spherical cavity and 3D view for inspiration and expiration chest xray and relationships. An outline of the algorithm is as follows: Calculate edge points, with their associated gradient magnitude and phase (direction), eg., using Canny or LoG Apply magnitude and phase constraints, retaining only those points which satisfy the constrains Form edge fragments from continuous sets of points Subdivide (split) edge fragments at points of high curvature Calculate all possible linkages of edge fragments to form candidates. A linkage can occur if the end points of the fragments are close enough, and the angle between them is within the orientation constraint range Discard all candidates which do not satisfy the length constraints Select the best candidate for matching to a frame on the basis of confidence scores. 3 Detection of a clavicle A clavicle will be used as a land mark to start an algorithm to detect a hilar region. We used the method introduced in Park et al (2002). The x-ray technologist will ask the patient to be still and to take a deep breath and hold it. This not only reduces the possibility of a blurred image but also enhances the quality of the image since air-filled lungs are easier to see on x-ray film. Inspiration causes low density in the middle of lung field. That means, ribs in the middle of lung field have lower density than the other parts of the lung field. That is why the edge detection algorithm often fails to detect ribs. Therefore to make rib edges clear we try to produce an expiration lung field. Based on the expiration lung field, we use a new connectivity method, called 4 ways with 10 neighbours connectivity, to detect clavicles and ribs. The clavicles will be landmarks to register the ribs. field. The lung field is inflated, so we deflate the lung field using a hemi_spherical cavity (see Figure 2). First, the middle point (x 0, y 0 ) of the lung, the first component(ry) and the second component(rx) can be obtained from the segmented lung and the hemi_spherical cavity (v) is made using these parameters. (1) denotes the expression of v(x i, y j ). v(x i,y j ) = (f(x 0,y 0 )-((x i -x 0 )^2/rx)-((y j -y 0 )^2/ry)) (1) where f is the chest xray image, i is 1 to the image width and j is 1 to the image height. We now then can produce the expiration chest xray image (f!) using the following rule (2). if v(x i,y j ) >= 0 (2) else f! (x i,y j ) = f(x i,y j ) + v(x i,y j ); f! (x i,y j ) = f(x i,y j ); A canny edge detect algorithm is applied on f! image and we track the edges using 4 ways with 10 neighbours shown in Figure 3. Since the clavicles have a certain gradient, length and location, these properties should be considered to produce clavicle edge candidates. In this sense, for the left clavicle edges, only 4 ways can be linked with the current pixel such as; a direct left, right, left down, and right up neighbours. It is important to consider the rib edges which are often intervened by other structures, so the neighbour s neighbour should be check to be linked but the gradient also should be maintained. We select a minimum length to accept as an edge and we empirically selected min_length = 25. Once the edge candidates are formed, our system selects the best candidates using knowledge-based constraints such as a gradient, length and location (see Figure 3(d)(e)). This research is a part of the Chest Xray Interpretation project, so our method applies to the segmented lung

3 For left clavicle and right ribs For right clavicle and left ribs (a) (b) (c) (d) (e) Figure 3. (a) edges for inspiration image (b) edges for expiration image (c) 4 ways with 10 neighbours connectivity (d) clavicle candidates (e) best candidate for clavicle marked by black lines 4 Detection and measurement of Hilar region A hilar region is a depression or fissure where vessels or nerves or ducts enter an organ. This region is not easily found because it is superimposed with other structures of the lung creating a complex structure which is difficult to define. We investigated how the radiologists find the region and what features they need about the region s size and location. A radiologist a. First searches from just below the aorta of the right lung. b. Imagines a semi-circle, with a radius size over one third of the right lung width, from the bottom of the aorta c. Then checks the density of the region. If the dense region is wide enough around the semicircle or within the semi-circle, it means the hilar region is considered big and vice versa d. Moves to the left lung and searches about 3 cm down from the right hilar region starting point e. Repeats b. and c. for the left lung. Our system to detect and measure the hilar region follows the above steps except the starting point we used is a clavicle location instead an aorta. The method is as follows. a. Draw a semi-circle with radius one third of the width of the lung at the end of the clavicle. b. Calculate the intensity within the semi-circle c. Move the semi-circle one pixel down d. Repeat from a. to c. until reaching the diaphragm e. Select the region which has maximum intensity value After a system detects the hilar region, the measurement of the hilar region should be followed. The most important feature a radiologist needs is the size of the region. Therefore, our system compares the intensity value of the hilar region with the average intensity value of the whole lung. The size can be measured using a fuzzy function (see Figure 5). If the value v, which is I(lung) / I(hila) where I(lung) is the average intensity of the lung and I(hila) is the average intensity of the hilar region, is s, the hila region is norma l. If v is less than s, the intensity of the hilar region is high, so the hilar region can be considered big. Our system checks the fuzzy function with the value v to report whether the hilar region is big or small. We empirically selected s = Performance analysis Our system extracted the lung field from the input radiograph and located the left and right clavicles. It then detected the hilar region for the right and left lung. Our system reported the size of the hilar region using natural language normal, big, and small. We tested 5 radiographs and our system detected the same positions that were indicated by a radiologist. Our system measured the size of the hilar region and a radiologist agreed with the measurements except for one case. Our system reported normal for the dis2 while the radiologist reported it is small (see table 1).

4 Figure 4. Processing to detect the hilar region on the left lung of chest xray dis01 dis02 dis03 dis04 dis05 System big normal normal big normal Radiologist big small normal big normal Table 1. Measument of the hilar region 6 Conclusion In this paper, we present a simple method to detect and measure the hilar region. We limited our experiment to the selected radiographs because if the lung has many abnormalities around the boundary of the lung or the texture within the lung field, our system is not able to detect the hilar region. Even though this method can only be applied to limited chest radiographs, it is valuable as a start in considering the hilar region from a radiologist s point of view. This research can be extended to manage more complicated chest radiographs. Acknowedgements CF 1 0 (Certainty factor) Big Normal Small I(lung)/I(hila) Figure 5. Fuzzy function graph for a hilar region We thank Dr. Bruce Doust in St. Vincent Hospital in Sydney for supplying all testing images and valuable medical verification advice. Reference Anne, M. F., Arivid, L., Michael, B. and Lothar, R. S. (2000): 8 th Meeting of the ISMRM, /amf_arvid_madrid99/ismrm2000.pdf Arthur R (2000): Interpretation of the Paediatric Chest X- ray, Paediatric respiratory Reviews, Bram G. and Bart M., (2000): Automatic segmentation of lung fields in chest Radiographs, Medical Physics, 27(10): Chen X, Hasegawea J. and Toriwaki J. (1988): Quantitative diagnosis of pneumoconiosis based on recognition of small rounded opacities in chest x-ray images, IEEE transactions on medical imaging, Doi K., Hever M., Shigehiko K., and Robertt M., (1997): Computer-aided diagnosis in radiology: Potential and pitfalls, European Joural of Radiology, 31: Fu K., Chien Y., and Persoon E., (1975): Computer Processing of Chest X-ray Images, TR-EE 75-38, Purdue Uni, Kim Y., Jeong K., and Lee K., (1989): A new algorithm for detection of lung boundary, IEEE engineering in medicine & biology society, 2770(6) Matthew C., and Lawrence O., (1998): Automatic Tumor Segmentation using knowledge-based techniques, IEEE transactions on medical imaging, 17(2): Matthew B., Laurence W., Bruce D., Robert G., and Sun C., (1998): Knowledge-based method for segmentation

5 and analysis of lung boundaries in chest X-ray images, Computerized medical imaging and graphics, 22: Park M., Wilson L., and Jin J., (2001): Automatic Extraction of Lung Boundaries by a Knowledge-Based Method, Visual Information Processing, 2:14-19 Park, M., Jin, J., and Wilson, L., (2002): A New Method to Detect and Label Ribs on Expiration Chest Xrays, The Physics of Medical Imaging conference at SPIE, (in press) Takeshi H., Hiroshi F., and Jing X., (1997): Development of automated detection system for lung nodules in chest radiograms, IEEE transaction on medical imaging, Tetsuo S., Yoshio Y. and Naozo S., (1995): Construction of Structural Edge Map on Chest Radiograph Using Hough Trasfromation and Line connection, Systems and Computers, 26(6):71-78 Tsuneo M., Hitoshi Y., Doi K.., and Maryellen G., (1991): Image feature analysis of false-positive diagnoses produced by automated detection of lung nodules, Wechsler H., and Sklansky J., (1975): Automatic detection of rib contours in chest radiographs, International Joing Conference on Artificial Intelligence, Zhanjun Y., Ardeshir G., and Laurens V., (1995): Automatic Detection of Rib Borders in Chest Radiographs, IEEE Transactions on Medical Imaging, 14(3): Zhu X., Lee K., Levin D., Wong C., and Huang K., (1996): Temporal image database design for outcome analysis of lung nodule, Computerized medical imaging and graphics, 20(4):