References
[1]. Rogowska. J (2000). “Overview and fundamentals of medical image segmentation,” in Handbook of Medical Imaging, Processing and Analysis, I. N. Bankman, Ed. New York, NY, USA: Academic, pp. 69– 85.
[2]. Hedlund. L.W Anderson. R P. Goulding. L J. Beck J.W Effmann. E.L and C. E. Putman, (1982). “Two methods for isolating the lung area of a CT scan for density information,” Radiology, vol. 144, pp. 353 357.
[3]. Qian. Y Zhang. C and Yang. X, (2008). “New method for quantification of pleural effusions from CT imaging,” in Proc. ISECS Int. Colloq. Comput. Commun. Control Manage, pp. 768–773.
[4]. Donohue. R Shearer. A and Bruzzi. J, (2009). “Constrained region-based segmentation of pleural effusion in thin-slice CT,” in Proc. IEEE 13th Int. Mach. Vis. Image Process. Conf., pp. 24–29.
[5]. Tran. B.Q Tajik. J.K Chiplunkar. R and Hoffman. E.A, (Oct 1996). “Lung volume control for quantitative X-ray CT,” Ann. Biomed. Eng., vol. 24, no. 1, p. S-66.
[6]. Cai. W Tabbara. M Takata. A Yoshida. H Harris. G.J Novelline R.A, (2009). “MDCT for automated detection and measurement of pneumothoraces in trauma patients,” Amer. J. Roentgenol., vol. 192, pp. 830–836.
[7]. Mergo. P.J Helmberger. T Didovic. J Cernigliaro. J Ros. P.S and Staab. E.V, (1999). “New formula for quantification of pleural effusions from computed tomography,” J. Thorac. Imag., vol. 14, pp. 122–125.
[8]. Lankisch. P and Becher. R, (1994). “Pleural effusions: A new negative prognostic parameter for acute pancreatitis,” Amer. J. Gastroenterol., vol. 89, pp. 1849–1851.
[9]. Fabijanska. A, (2009). Results of applying two-pass region growing algorithm for airway tree segmentation to MDCT chest scans from EXACT database. In Proc. of Second International Workshop on Pulmonary Image Analysis, pages 251–260.
[10]. Polak, M, Zhang, H. & PI, M. (2009). An evaluation metric for image segmentation of multiple objects. Image and Vision Computing, 27(8):1223-1227.
[11]. Rein, A, Hamida, A. B. & Benjelloun, M. (2009). An Optimal Unsupervised Satellite image Segmentation Approach Based on Pearson System and k-Means Clustering Algorithm Initialization, International Journal of Signal Processing.
[12]. Emerson Carlos Pedrino, José Hiroki Saito,Valentin Obac Roda, Marcelo Marinho. An FPGA-Based Region-Growing Architecture for Binary Images, IWSSIP 2010 - 17th International Conference on Systems, Signals and Image PA
[13]. Indra Kanta Maitra, Sanjay Nag and Prof. Samir K. Bandyopadhyay, (2011). Automated Digital Mammogram Segmentation For Detection Of Abnormal Masses Using Binary Homogeneity Enhancement Algorithm. Indian Journal of Computer Science and Engineering (IJCSE).
[14]. Jayant. N.S, (Sept. 1976). “Average and median-based smoothing techniques for improving digital speech quality in the presence of transmission errors,” IEEE Trans. Commun., vol. COM-24, pp. 1043-1045.
[15]. Porcel. J and Vives. M, (2003). “Etiology and pleural fluid characteristics of large and massive effusions,” Chest, vol. 124, pp. 978–983.
[16]. Arad. M Zlochiver. S Davidson. T Shoenfeld. Y Adunsky. A and Abboud. S, (2009). “The detection of pleural effusion using a parametric fit technique,” Physiol. Meas., vol. 30, pp. 421–428.
[17]. Zhang. L E. Hoffman. A and Reinhardt J.M, (Jan 2006). “Atlas-driven lung lobe segmentation in volumetric X-ray CT images,” IEEE Trans. Med. Imag., vol. 25, no. 1, pp. 1–16.
[18]. Zhou. X Hayashi. T Hara. T Fujita. H Yokoyama. R Kiryu. T and Hoshi. H, (2006). “Automatic segmentation and recognition of anatomical lung structures from high resolution chest CT images,” Comput. Med. Imag. Graph., vol. 30, pp. 299–313.
