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Early Stage Squamous Cell Lung Cancer Detection

  • Harish KuchulakantiEmail author
  • Chandrasekhar Paidimarry
Conference paper
Part of the Learning and Analytics in Intelligent Systems book series (LAIS, volume 3)

Abstract

Smoking and consuming alcohol leads to dangerous disease Squamous cell Lung Cancer (SqCLC). It is widespread all over the world today. The mortality rate of this cancer is on the higher side as it is diagnosed in stage III or IV. Small nodules are formed in the lungs in the starting stage, and gradually spreads in and around lung regions by a process of metastasis. Only few symptoms are seen in early stages of Cancer. Diagnosing Lung Cancer in the early stage is essential. The paper attempts to diagnose Lung Cancer in early stages by processing the Chest Computed Tomography (CT) image and segment the small lung nodules. The method uses the median filter to filter the noise and Watershed transform in combination with Morphology-based region of interest segmentation to fragment the nodules. Various metrics of the nodules in the image are calculated and the stage of Lung Cancer is determined.

Keywords

Lung cancer Lung nodule Watershed segmentation 

References

  1. 1.
    Hu S, Hoffman EA, Reinhardt JM (2001) Automatic lung segmentation for accurate quantification of volumetric X-ray CT images. IEEE Trans Med Imaging 20:490–498CrossRefGoogle Scholar
  2. 2.
    Mesanovic N, Huseinagic H, Males M, Grgic M, Skejic E, Smajlovic M (2011) Automatic CT image segmentation of the lungs with region growing algorithm IWSSIP-2011, June 2011Google Scholar
  3. 3.
    Hedlund LW, Anderson RF, Goulding PL, Beck JW, Effmann EL, Putman CE (1982) Two methods for isolating the lung area of a CT scan for density information. Radiology 144:353–357CrossRefGoogle Scholar
  4. 4.
    Sluimer I, Prokop M, van Ginneken B (2005) Toward automated segmentation of the pathological lung in CT. IEEE Trans Med Imaging 24(8):1025–1038CrossRefGoogle Scholar
  5. 5.
    Boykov Y, Jolly MP (2000) Interactive organ segmentation using graph cuts. In: Medical image computing and computer-assisted intervention: MICCAI 2000. Springer, Berlin, Germany, pp 276–286Google Scholar
  6. 6.
    Udupa JK, Samarasekera S (1996) Fuzzy connectedness and object definition: theory, algorithms, and applications in image segmentation. Graph Models Image Process 58(3):246–261CrossRefGoogle Scholar
  7. 7.
    Mangan AP, Whitaker RT (1999) Partitioning 3D surface meshes using watershed segmentation. IEEE Trans Vis Comput Graph 5(4):308–321CrossRefGoogle Scholar
  8. 8.
    Chen H, Mukundan R, Butler APH (2011) Automatic lung segmentation in HRCT images. IVCNZGoogle Scholar
  9. 9.
    Lung Image Database Consortium - Image Database Resource Initiative (LIDC-IDRA) database - The Cancer Imaging Archive (TCIA) public access. https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of BMEUniversity College of Engineering (A), Osmania UniversityHyderabadIndia
  2. 2.Department of ECEUniversity College of Engineering (A), Osmania UniversityHyderabadIndia

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