Intelligent Segmentation of Medical Images Using Fuzzy Bitplane Thresholding

[1] Armato, S.G., Giger, M.L., Moran, C.J. (1999). Computerized detection of pulmonary nodules on CT scans. Radio Graphics, 19, 1303-1311.10.1148/radiographics.19.5.g99se18130310489181Search in Google Scholar

[2] Hu, S., Huffman, E.A., Reinhardt, J.M. (2001). Automatic lung segmentation for accurate quantification of volumetric X-Ray CT images. IEEE Transactions on Medical Imging, 20 (6), 490-498.10.1109/42.92961511437109Search in Google Scholar

[3] Grau, V., Mewes, A.U., Alcaniz, M., Kikinis, R., Warfield, S.K. (2004). Improved watershed transform for medical image segmentation using prior information. IEEE Transactions on Medical Imaging, 23 (4), 447-458.10.1109/TMI.2004.82422415084070Search in Google Scholar

[4] Pan, Z., Lu, J. (2007). A Bayes-based region-growing algorithm for medical image segmentation. Computing in Science & Engineering, 9 (4), 32-38.10.1109/MCSE.2007.67Search in Google Scholar

[5] Mukhopadhyay, S., Chanda, B. (2003). Multiscale morphological segmentation of gray-scale images. IEEE Transactions on Image Processing, 12 (5), 533-549.10.1109/TIP.2003.81075718237930Search in Google Scholar

[6] Maulik, U. (2009). Medical image segmentation using genetic algorithms. IEEE Transactions on Information Technology in Biomedicine, 13 (2), 166-173.10.1109/TITB.2008.200730119272859Search in Google Scholar

[7] Holtzman-Gazit, M., Kimmel, R., Peled, N., Goldsher, D. (2006). Segmentation of thin structures in volumetric medical images. IEEE Transactions on Image Processing, 15 (2), 354-363.10.1109/TIP.2005.86062416479805Search in Google Scholar

[8] Antonelli, M., Lazzerini, B., Marcelloni, F. (2005). Segmentation and reconstruction of the lung volume in CT images. In ACM Symposium on Applied Computing, 13-17 March 2005. ACM, 255-259.10.1145/1066677.1066738Search in Google Scholar

[9] Pu, J., Roos, J., Yi, C.A., Napel, S., Rubin, G.D., Paik, D.S. (2008). Adaptive border marching algorithm: Automatic lung segmentation on chest CT images. Computerized Medical Imaging and Graphics, 32 (6), 452-462.10.1016/j.compmedimag.2008.04.005253665518515044Search in Google Scholar

[10] Prasad, M.N., Brown, M.S., Ahmad, S., Abtin, F., Allen, J., Da Costa, I., Kim, H.J., McNitt-Gray, M.F., Goldin, J.G. (2008). Automatic segmentation of lung parenchyma in the presence of diseases based on curvature of ribs. Academic Radiology, 15 (9), 1173-1180.10.1016/j.acra.2008.02.00418692759Search in Google Scholar

[11] Meng, X., Qiang, Y., Zhu, S., Fuhrman, C., Siegfried, J.M., Pu, J. (2012) Illustration of the obstacles in computerized lung segmentation using examples. Medical Physics, 39 (8), 4984-4991.10.1118/1.4737023341687922894423Search in Google Scholar

[12] Xu, L., Luo, S. (2010). A novel method for blood vessel detection from retinal images. BioMedical Engineering OnLine, 9 (1), 2-10.10.1186/1475-925X-9-14283889820187975Search in Google Scholar

[13] Gunadi, W.N., Shamsuddin, S.M., Alias, R.A., Sap, M.N. (2003). Selection of defuzzification method to obtain crisp value for representing uncertain data in a modified sweep algorithm. Journal of Computer Science & Technology, 3 (2), 22-28.Search in Google Scholar

[14] Lopes, N.V., Mogadouro do Couto, P.A., Bustince, H., Melo-Pinto, P. (2010). Automatic histogram threshold using fuzzy measures. IEEE Transactions on Image Processing, 19 (1), 199-204.10.1109/TIP.2009.203234919758860Search in Google Scholar

[15] Jain, A.K. (1989). Fundamentals of Digital Image Processing. Prentice-Hall.Search in Google Scholar

[16] Beevi, Z., Sathik, M. (2012). A robust segmentation approach for noisy medical images using fuzzy clustering with spatial probability. The International Arab Journal of Information Technology, 9 (1), 74-83.Search in Google Scholar

[17] Kalender, W.A., Fichte, H., Bautz, W., Skalej, M. (1991). Semiautomatic evaluation procedures for quantitative CT of the lung. Journal of Computer Assisted Tomography, 15, 248-255.10.1097/00004728-199103000-000112002103Search in Google Scholar

[18] Xie, X.L., Beni, G.A. (1991). Validity measure for fuzzy clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 3, 841-846.10.1109/34.85677Search in Google Scholar

[19] Chu, C., Oda, M., Kitasaka, T., Misawa, K., Fujiwara, M., Hayashi, Y., Nimura, Y., Rueckert, D., Mori, K. (2013) Multi-organ segmentation based on spatially-divided probabilistic atlas from 3D abdominal CT images. In Medical Image Computing and Computer-Assisted Intervention. Springer, LNCS 8150, 165-172.Search in Google Scholar

[20] Suzuki, M., Linguraru, M.G., Okada, K. (2012) Multiorgan segmentation with missing organs in abdominal CT images. In Medical Image Computing and Computer-Assisted Intervention. Springer, LNCS 7512, 418-425.Search in Google Scholar

[21] Xinge You, Qinmu Peng, Yuan Yuan, Yiu-ming Cheung, Jiajia Lei. (2011). Segmentation of retinal blood vessels using the radial projection and semisupervised approach. Pattern Recognition, 44 (10-11), 2314-2324.10.1016/j.patcog.2011.01.007Search in Google Scholar

[22] Smirg, O., Liberda, O., Smekal, Z., Sprlakova-Pukova, A. (2012). MRI slice segmentation and 3D modelling of temporomandibular joint measured by microscopic coil. Measurement Science Review, 12 (3), 74-81.10.2478/v10048-012-0018-5Search in Google Scholar

[23] Linguraru, M.G., Pura, J.A., Chowdhury, A.S., Summers, R.M. (2010). Multi-organ segmentation from multi-phase abdominal CT via 4D graphs using enhancement, shape and location optimization. In Medical Image Computing and Computer-Assisted Intervention. Springer, LNCS 6363, 89-96.Search in Google Scholar

[24] Zheng, Y., Stambolian, D., O’Brien, J., Gee, J.C. (2013). Optic disc and cup segmentation from color fundus photograph using graph cut with priors. In Medical Image Computing and Computer-Assisted Intervention. Springer, LNCS 8150, 75-82.Search in Google Scholar

[25] Marin, D., Aquino, A., Gegundez-Arias, M.E., Bravo, J.M. (2011). A new supervised method for blood vessel segmentation in retinal images by using graylevel and moment invariants-based features. IEEE Transactions on Medical Imaging, 30 (1), 146-158.10.1109/TMI.2010.206433320699207Search in Google Scholar

[26] Fraz, M.M., Remagnino, P., Hoppe, A., Uyyanonvara, B., Rudnicka, A.R., Owen, C.G., Barman, S.A. (2012). An ensemble classification-based approach applied to retinal blood vessel segmentation. IEEE Transactions on Biomedical Engineering, 59 (9), 2538-2548.10.1109/TBME.2012.220568722736688Search in Google Scholar