Intelligent Segmentation of Medical Images Using Fuzzy Bitplane Thresholding

  • 1 Anna University Chennai, Chennai-600025, India.
  • 2 Department of Information Science and Technology, Anna University Chennai, Chennai-600025, India.


The performance of assessment in medical image segmentation is highly correlated with the extraction of anatomic structures from them, and the major task is how to separate the regions of interests from the background and soft tissues successfully. This paper proposes a fuzzy logic based bitplane method to automatically segment the background of images and to locate the region of interest of medical images. This segmentation algorithm consists of three steps, namely identification, rule firing, and inference. In the first step, we begin by identifying the bitplanes that represent the lungs clearly. For this purpose, the intensity value of a pixel is separated into bitplanes. In the second step, the triple signum function assigns an optimum threshold based on the grayscale values for the anatomical structure present in the medical images. Fuzzy rules are formed based on the available bitplanes to form the membership table and are stored in a knowledge base. Finally, rules are fired to assign final segmentation values through the inference process. The proposed new metrics are used to measure the accuracy of the segmentation method. From the analysis, it is observed that the proposed metrics are more suitable for the estimation of segmentation accuracy. The results obtained from this work show that the proposed method performs segmentation effectively for the different classes of medical images.

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  • [1] Armato, S.G., Giger, M.L., Moran, C.J. (1999). Computerized detection of pulmonary nodules on CT scans. Radio Graphics, 19, 1303-1311.

  • [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.

  • [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.

  • [4] Pan, Z., Lu, J. (2007). A Bayes-based region-growing algorithm for medical image segmentation. Computing in Science & Engineering, 9 (4), 32-38.

  • [5] Mukhopadhyay, S., Chanda, B. (2003). Multiscale morphological segmentation of gray-scale images. IEEE Transactions on Image Processing, 12 (5), 533-549.

  • [6] Maulik, U. (2009). Medical image segmentation using genetic algorithms. IEEE Transactions on Information Technology in Biomedicine, 13 (2), 166-173.

  • [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.

  • [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.

  • [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] 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.

  • [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.

  • [12] Xu, L., Luo, S. (2010). A novel method for blood vessel detection from retinal images. BioMedical Engineering OnLine, 9 (1), 2-10.

  • [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.

  • [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.

  • [15] Jain, A.K. (1989). Fundamentals of Digital Image Processing. Prentice-Hall.

  • [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.

  • [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.

  • [18] Xie, X.L., Beni, G.A. (1991). Validity measure for fuzzy clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 3, 841-846.

  • [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.

  • [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.

  • [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.

  • [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.

  • [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.

  • [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.

  • [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.

  • [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.


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