Performance comparisons of the soft computing algorithms in lung segmentation and nodule identification

  • Authors

    • Afshan Khanum
    • S. Purushothaman
    • P. Rajeswari
    2017-12-21
    https://doi.org/10.14419/ijet.v7i1.1.9287
  • Lung Image, Segmentation, Nodule Identification, Back Propagation Algorithm, Fuzzy Logic.

  • This paper presents the implementation back propagation algorithm (BPA) and fuzzy logic(FL) in lung image segmentation and nodule identification. Lung image database consortium (LIDC) database images has been used. Features are extracted using statistical methods. These features are used for training the BPA and FL algorithms. Weights are stored in a file that is used for segmentation of the lung image. Subsequently, texture properties are used for nodule identification.

  • References

    1. [1] Giger ML, Chan HP & Boone J, “Anniversary paper: history and status of CAD and quantitative image analysis: the role of medical physics and AAPMâ€, Medical Physics, Vol.35, No.12, (2008), pp.5799–5820.

      [2] Golosio B, Masala GL, Piccioli A, Oliva P, Carpinelli M & Cataldo R, “A novel multi-threshold method for nodule detection in lung CTâ€, Medical Physics, Vol.36, No.8, (2009), pp.3607–3618.

      [3] Wang J & Guo H, “Automatic Approach for Lung Segmentation with Juxta-Pleural Nodules from Thoracic CT Based on Contour Tracing and Correctionâ€, Computational and mathematical methods in medicine, (2016).

      [4] Lee SLA, Kouzani AZ & Hu EJ, “Automated detection of lung nodules in computed tomography images: a reviewâ€, Machine Vision and Applications, Vol.23, No.1, (2012), pp.151-163.

      [5] Lee Y, Hara T, Fujita H, Itoh S & Ishigaki T, “Automated detection of pulmonary nodules in helical CT images based on an improved template-matching techniqueâ€, IEEE Trans. Med. Imaging, Vol.20, (2001), pp.595–604.

      [6] Lin DT, Yan CR & Chen WT, “Autonomous detection of pulmonary nodules on CT images with a neural network-based fuzzy systemâ€, Comput. Med. Imaging Graph, Vol.29, (2005), pp.447–458.

      [7] Mamdani EH, “Application of fuzzy logic to approximate reasoning using linguistic synthesisâ€, IEEE Trans. on Computers, Vol.26, No.12, (1977), pp.1182–1191.

      [8] Ochs RA, Goldin JG, Abtin F, Kim HJ, Brown K, Batra P, Roback D, McNitt-Gray MF & Brown MS, “Automated classification of lung bronchovascular anatomy in CT using Adaboostâ€, Med. Image Anal., Vol.11, (2007), pp.315–324.

      [9] Okada K, Comaniciu D & Krishnan A, “Robust Anisotropic Gaussian Fitting for Volumetric Characterization of Pulmonary Nodules in Multislice CTâ€, IEEE Trans. Med. Imaging, Vol.24, No.3, (2005), pp.409–423.

      [10] Osman O, Ozekes S & Ucan ON, “Lung nodule diagnosis using 3D template matchingâ€, Comput. Biol. Med, Vol.37, No.8, (2007), pp.1167–1172.

      [11] Ozekes S, Osman O & Ucan ON, “Nodule detection in a lung region that’s segmented with using genetic cellular neural networks and 3D template matching with fuzzy rule based thresholdingâ€, Korean J. Radiol., Vol.9, (2008), pp.1–9.

      [12] Ba˘gcı U, Bray M, Caban J, Yao J & Mollura DJ, “Computer-assisted detection of infectious lung diseases: A reviewâ€, Computerized Medical Imaging and Graphics, Vol.36, (2012), pp.72–84.

      [13] Millar Jr WT & Shah RM, “Isolated diffuse Ground–Glass opacity in Thoracic CT, causes and clinical presentationsâ€, American journal of Roentgenology, Vol.184, (2005), pp.2613-2622.

      [14] Cheng W, Ma L, Yang T, Liang J & Zhang Y, “Joint Lung CT Image Segmentation: A Hierarchical Bayesian Approachâ€, PLOS ONE, Vol.11, No.9, (2016), pp.1-13.

      [15] Yao J, Dwyer A, Summers RM & Mollura DJ, “Computer-aided diagnosis of pulmonary infections using texture analysis and support vector machine classificationâ€, Academic Radiology, Vol.18, No.3, (2011), pp.306–314.

      [16] Zadeh LA, “Fuzzy setsâ€, Information and Control, Vol.8, (1965), pp.338-353.

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  • How to Cite

    Khanum, A., Purushothaman, S., & Rajeswari, P. (2017). Performance comparisons of the soft computing algorithms in lung segmentation and nodule identification. International Journal of Engineering & Technology, 7(1.1), 189-192. https://doi.org/10.14419/ijet.v7i1.1.9287