Copyright 2002 Society of Photo-Optical Instrumentation Engineers. This paper was (will be) published in The Proceedings of SPIE and is made available as an electronic reprint (preprint) with permission of SPIE. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes or modification of the content of this paper are prohibited.
Enhanced Lung Cancer Detection in Temporal Subtraction Chest Radiography Using Directional Edge Filtering Techniques Hui Zhaoa,b , Shih-Chung Ben Loa , Matthew T. Freedmana , and Yue Wangb a ISIS
Center, Radiology Department,Georgetown University Medical Center, Washington DC, 20007 b Electrical Engineering and Computer Science Department, The Catholic University of America, Washington DC, 20064 ABSTRACT
We have developed a series of directional edge enhancement and edge extraction methods that can accurately segment posterior and anterior ribs in chest radiography. These methods can also separate the lower and upper edges of ribs. The edges were first enhanced by two sets of proximate parabola curve models for left and right sides of the image. We used a directional edge filtering technique to remove low signals and noises on the edge enhanced image in the multiresolution domain. Finally, we employed a rib curve projection and reasoning method to reconstruct the rib edges and remove false edges for the upper and lower bound of the rib edges independently. A two-step registration, corresponding to global and local matching, is applied for current and prior images assisted by their corresponding edge images. The subtraction images were then processed by a rule-based CAD system. The FROC results were compared to that obtained by the original image using a CAD system consisting of rule-based and convolution neural network processing. The majority of lung cancer in temporal subtraction images were lit-up. The FROC results were significantly improved using the subtraction image with the rule-based CAD. Keywords: Directional edge filtering, temporal subtraction, radiography.
1. INTRODUCTION Lung cancer is the leading cause of cancer deaths in the United States in both men and women and is a leading cause of cancer deaths throughout the world. While preventive methods (smoking cessation and diminution) are having some success, the long (10-15 year) latency of deleterious effect that continue after smoking cessation and the failure of successful treatment of nicotine addiction in most smokers indicates that there will be a long term need for improved techniques for early detection. The most common detection techniques currently known Further author information: (Send correspondence to Hui Zhao or Shih-Chung B. Lo.) Hui Zhao: E-mail:
[email protected], Telephone: 202 687 5135 Shih-Chung B. Lo: E-mail:
[email protected], Telephone: 202 687 1659, Address: ISIS Center, Radiology Department, Georgetown University Medical Center, 2115 Wisconsin Ave. Suite 603, N.W. Washington, DC, 20007
1
include chest radiography, cytologic analysis of sputum samples, fiberoptic examination of bronchial airways, and computerized tomography (CT) scans. Among these, chest radiography remains the most cost-effective and widely used detection. We estimate that more than 90% of lung cancer detection now takes place on chest radiographs. The chest radiograph is non-invasive, relatively inexpensive, and routinely available. In the 1980s, Stitik and his colleagues found that a single radiologist did miss 32% of all lung nodules viewed retrospectively and that two radiologists working with an arbiter missed only 15%. Motivated by these clinical reports, we began to develop computer techniques to enhance the cancer visibility in the chest radiography. Specifically, we attempt to remove the lung structures such as ribs, and vessels. At the same time, the growing abnormalities including the lung cancer can be enhanced.
2. METHOD We first filtered the ribs and structures in the lung. The filter image would facilitate delineation of the lung field. In this study, our effort has been concentrated on the detection of rib and registration of ribs for temporal chest radiography. Approximately one hundred chest radiographs were randomly selected by the third author who is a senior radiologist. All the selected radiographs were 14” × 17” postero-anterior (PA) view films. These films were digitized by a Vidar film digitizer (Model: VXR-12 Plus) at 12 bits depth with a resolution at 150 pixels per inch. In this study, the images were averaged and resized to 525 × 637 pixels. The rib frame is the one of most prominent structures on chest radiographs. They sometimes are obstacles in clinical chest radiology. On the other hand, the ribs can facilitate position identification in the lung. Technically, upper and middle ribs are reliable landmarks for comparing temporal and contralateral lung images. Accurate rib delineation and registration would be a technical backbone for the development of automatic computer-aided tools to assist the radiologists in the detection of lung cancer, tuberculosis, interstitial lung diseases, etc. It would also make quantitative analysis of chest image possible. Vogelsang et al.1 and Sanada et al.2 have used model based segmentation methods to delineate the ribs on chest radiographs. In this study, we created an initial rib curve model and performed the directional filtering along the curves. The filtered image was processed by a directional rib extraction technique in multi-resolution domains. Based on these curves, broken segmented ribs could be reconstructed using a rib curve projection technique. Finally, an active line model was used to confirm and label the ribs. This whole process could be repeated twice, with the initial generic ribs model in the first run, and refined the ribs model for the second run. We could split our algorithm into three major parts, rib enhancement, rib extraction, and rib confirmation and labelling.
2.1. Directional filtering By visually inspecting the rib curves on many chest radiographs, we found that posterior ribs showed as the convex curves on the film. Since a generic edge detector would produce all edges, we selectively extracted ribs by applying directional filters along the rib curves. We started with a posterior rib modelling (i.e., Eq.(1). Two sets of proximate parabola functions were used to represent the left and right lung rib curvatures respectively. Rib orientations were modelled in such as way that the flat segment is on the central side of the lung and tilt region is on the lateral side of the lung. This quantitative description can be proximately expressed by: y − yo,k = ak,[y] (x − xo,k )p
(1)
where ak,[y] is a function of y and p = 2 ∼ 3. The norm orientations can be computed by taking first derivative of the curves, dy = pak,[y] (x − xo,k )p−1 (2) dx and the angle with respect to x axis is dy (3) θ(x, y) = arctan( ) dx We interpolated the norm orientation value of the ribs for the whole lung area and saved them to a map file. Then we picked up our edge detector as the first derivative of Gaussian(DoG). The DoG filter is a very
(A)
(B)
Figure 1. (A) The enhanced upper edge of ribs by directional filtering;and (B) The enhanced lower edge of ribs by directional filtering.
effective edge detector.3 It is natural weighted kernel, and easy to implement. Also it shows the symmetric in two-dimensional, we could modified the DoG as the function of the orientation, as DoG(θ, x, y)
=
