Abstract
The aim of this paper is to evaluate and compare the performance of three convergence index (CI) filters when applied to the enhancement of chest radiographs, aiming at the detection of lung nodules. One of these filters, the sliding band filter (SBF), is the proposal of an innovative operator, while the other two CI class members, the iris filter (IF) and the adaptive ring filter (ARF), are already known in this application. To demonstrate the adequacy of the new filter for the enhancement of chest x-ray images, we calculated several figures of merit with the goal of comparing (i) the contrast enhancement capability of the filters, and (ii) the behavior of the filters for the detection of lung nodules. The results obtained for 154 images with nodules of the JSRT database show that the SBF outperforms both the IF and ARF. The proposed filter demonstrated to be a promising enhancement method, thus justifying its use in the first stage of a computer-aided diagnosis system for the detection of lung nodules.
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References
Kobatake, H., Murakami, M.: Adaptive filter to detect rounded convex regions: Iris filter. In: Int. Conf. Pattern Recognition, vol. II, pp. 340–344 (1996)
Kobatake, H., Hashimoto, S.: Convergence index filter for vector fields. IEEE Transactions on Image Processing 8(8) (1999)
Wei, J., Hagihara, Y., Kobatake, H.: Detection of cancerous tumors on chest X-ray images - Candidate detection filter and its evaluation. In: ICIP 1999, vol. 27AP4.2 (1999)
Kobatake, H., Wei, J., Yoshinaga, Y., Hagihara, Y., Shimizo, A.: Nonlinear Adaptine Convergence Index Filters and Their Characteristics. In: Int. Conf. Pattern Recognition, pp. 522–525 (2000)
Shiraishi, J., Katsuragawa, S., Kezoe, S., Matsumoto, T., Kobayashi, T., Komatsu, K., Matsui, M., Fujita, M., Kodera, Y., Doi, K.: Development of a Digital Image Database for Chest Radiographs With and Without a Lung Nodule: Receiver Operating Characteristic Analysis of Radiologists’ Detection of Pulmonary Nodules. American Journal of Roentgenology 174, 71–74 (2000)
Keserci, B., Yoshida, H.: Computerized detection of pulmonary nodules in chest radiographs based on morphological features and wavelet snake model. Medical Image Analysis 6, 431–447 (2002)
Song, X., Pogue, B., Jiang, S., Doyley, M., Dehghani, H., Tosteson, T., Paulsen, K.: Automated region detection based on the contrast-to-noise ratio in near-infrared tomography. Applied Optics 43(5), 1053–1062 (2004)
Singh, S., Bovis, K.: An evaluation of contrast enhancement techniques for mammographic breast masses. IEEE Transactions On Information Technology in Biomedicine 9(1), 109–119 (2005)
Wei, J., Hagihara, Y., Shimizu, A., Kobatake, H.: Optimal image feature set for detecting lung nodules on chest X-ray images. In: CARS 2002 (2002)
Suzuki, K., Shiraishi, J., Abe, H., MacMahon, H., Doi, K.: False-positive reduction in computer-aided diagnostic scheme for detecting nodules in chest radiographs by means of massive training artificial neural network. Academic Radiology 12, 191–201 (2005)
Pereira, C., Fernandes, H., Mendonça, A., Campilho, A.: Detection of lung nodule candidates in chest radiographs. In: Martí, J., et al. (eds.) IbPRIA 2007. LNCS, vol. 4478, pp. 170–177. Springer, Heidelberg (2007)
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Pereira, C.S., Mendonça, A.M., Campilho, A. (2007). Evaluation of Contrast Enhancement Filters for Lung Nodule Detection. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2007. Lecture Notes in Computer Science, vol 4633. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74260-9_78
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DOI: https://doi.org/10.1007/978-3-540-74260-9_78
Publisher Name: Springer, Berlin, Heidelberg
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