Volumetric analysis framework for accurate segmentation and classification (VAF-ASC) of lung tumor from CT images

Abstract

Lung tumor can be typically stated as the abnormal cell growth in lungs that may cause severe threat to patient health, since lung is a significant organ which comprises associated network of blood veins and lymphatic canals. The earlier detection and classification of lung tumor creates a greater impact on increasing the survival rate of patients. For analysis, the Computed Tomography (CT) lung images are broadly used, since it gives information about the various lung regions. The prediction of tumor contour, position, and volume plays an imperative role in accurate segmentation and classification of tumor cells. This will aid in successful tumor stage detection and treatment phases. With that concern, this paper develops a Volumetric Analysis Framework for Accurate Segmentation and Classification of lung tumors. The volumetric analysis framework comprises the estimation of length, thickness, and height of the detected tumor cell for achieving précised results. Though there are many models for tumor detection from 2D CT inputs, it is very important to develop a method for lung nodule separation from noisy background. For that, this paper connectivity and locality features of the lung image pixels. Moreover, morphological processing techniques are incorporated for removing the additional noises and airways. Tumor segmentation has been accomplished by the k-means clustering approach. Tumor Nodule Metastasis classification based-volumetric analysis is performed for accurate results. The Volumetric Analysis Framework provides better results with respect to factors such as accuracy rate of tumor diagnosis, reduced computation time, and appropriate tumor stage classification.

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References

  1. Ada RK (2013) Early detection and prediction of lung cancer survival using neural network classifier. IJAIEM 2(6):375–383

    Google Scholar 

  2. Afshar P, Ahmadi A, Zarandi MHF (2016) Lung tumor area recognition in CT images based on Gustafson–Kessel clustering. In: IEEE international conference on fuzzy systems (FUZZ-IEEE), pp 2302–2308

  3. Amutha A, Wahidabanu R (2013) Lung tumor detection and diagnosis in CT scan images. In: International conference on communication and signal processing

  4. Armato SG III, Hadjiiski L, Tourassi GD, Drukker K, Giger ML, Li F, Redmond G, Farahani K, Kirby JS, Clarke LP (2015) SPIE-AAPM-NCI lung nodule classification 411 challenge dataset. Cancer Imaging Arch 26(6):1045–1057

    Google Scholar 

  5. Dandil E et al (2014) Artificial neural network-based classification system for lung nodules on computed tomography scans. In: 6th international conference of soft computing and pattern recognition (SoCPaR), pp 382–386

  6. Dougherty L, Asmuth JC, Gefter WB (2003) Alignment of CT lung volumes with an opticalflow method. Acad Radiol 10(3):249–254

    Article  Google Scholar 

  7. Kavitha MS, Shanthini J, Sabitha R (2019) ECM-CSD: an efficient classification model for cancer stage diagnosis in CT lung images using FCM and SVM techniques. J Med Syst. https://doi.org/10.1007/s10916-019-1190-z

    Article  Google Scholar 

  8. Kavitha MS, Shanthini J, Bhavadharini RM (2020) ECIDS-enhanced cancer image diagnosis and segmentation using artificial neural networks and active contour modelling. J Med Imaging Health Inform 10(2):428–434. https://doi.org/10.1166/jmihi.2020.2976

    Article  Google Scholar 

  9. Lee Y, Hara T, Fujita H, Itoh S, Ishigaki T (2001) Automated detection of pulmonary nodules in helical CT images based on an improved template-matching technique. IEEE Trans Med Imaging 20:595–604

    Article  Google Scholar 

  10. Makaju S, Prasad PWC, Alsadoon A, Singh AK (2018) Lung cancer detection using CT scan images. Proc Comput Sci 125:107–114

    Article  Google Scholar 

  11. Mughal MN, Ikram W (2004) Early lung cancer detection by classifying chest CT images: a survey. In: 8th international multitopic conference, 2004. Proceedings of INMIC 2004

  12. Panpaliya N, Tadas N, Bobade S, Aglawe R, Gudadhe A (2015) A survey on early detection and prediction of lung cancer. Int J Comput Sci Mob Comput IJCSMC 4(1):175–184

    Google Scholar 

  13. Penedo MG, Carreira MJ, Mosquera A, Cabello D (19980 Computer-aided diagnosis: a neural-network-based approach to lung nodule detection. In: IEEE transactions on medical imaging, pp 872–880

  14. Punithavathy K, Ramya MM, Poobal S (2015) Analysis of statistical texture features for automatic lung cancer detection in PET/CT images. In: International conference on robotics, automation, control and embedded systems—RACE 2015, 18–20, February 2015

  15. Rikxoort EMV, Hoop BD, Viergever MA, Prokop M, Ginneken BV (2009) Automatic lung segmentation from thoracic computed tomography scans using a hybrid approach with error detection. Med Phys 36(7):2934–2947

    Article  Google Scholar 

  16. Sabitha R, Karthik S, Shanthini J (2016) Breast cancer detection using enhanced descriptive approach. J Med Imaging Health Inform 6(1887–1892):2016

    Google Scholar 

  17. Schnabel P, Junker K (2015) Pulmonary neuroendocrine tumors in the new WHO 2015 classification. Start of breaking new grounds. Pathologe 36:283–292

    Article  Google Scholar 

  18. Senthil Kumar K, Venkatalakshmi K, Karthikeyan K (2019) Lung cancer detection using image segmentation by means of various evolutionary algorithms. Comput Math Methods Med. https://doi.org/10.1007/s11063-020-10192-0

    MATH  Article  Google Scholar 

  19. Sowmiya T, Gopi M (2014) New begin L. Thomas Robinson “Optimization of lung cancer using modern data mining techniques”. Int J Eng Res 3(5):3139–3149

    Article  Google Scholar 

  20. Suzuki K, Abe H, MacMahon H, Doi K (2006) Image-processing technique for suppressing ribs in chest radiographs by means of massive training artificial neural network (MTANN). IEEE Trans Med Imaging 25(4):406–416

    Article  Google Scholar 

  21. Taher F, Werghi N, Al-Ahmad H, Sammouda R (2012) Lung cancer detection using artificial neural network and fuzzy clustering methods. Am J Biomed Eng 2(3):136–142

    Article  Google Scholar 

  22. Tariq A, Usman Akram M, Younus Javed M (2013) Lung nodule detection in CT images using neuro fuzzy classifier. In: Fourth international workshop on computational intelligence in medical imaging (CIMI)

  23. Thiyagarajan M (2016) A survey on computer-aided diagnosis systems for lung cancer detection. Int Res J Eng Technol IRJET 03(05):1562–1570

    Google Scholar 

  24. Tiwari AK (2016) Prediction of lung cancer using image processing techniques: a review. Adv Comput Intell Int J ACII 3(1):1–9

    Google Scholar 

  25. Wang J et al (2016) Prediction of malignant and benign of lung tumor using a quantitative radiomic method. In: Proceedings of international conference of the IEEE engineering in medicine and biology society (EMBC), pp 1272–1275

  26. Ye X, Lin X, Dehmeshki J, Slabaugh G, Beddoe G (2009) Shape-based computer-aided detection of lung nodules in thoracic CT images. IEEE Trans Biomed Eng 56(7):1810–1820

    Article  Google Scholar 

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Correspondence to M. S. Kavitha.

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Kavitha, M.S., Shanthini, J. & Karthikeyan, N. Volumetric analysis framework for accurate segmentation and classification (VAF-ASC) of lung tumor from CT images. Soft Comput 24, 18489–18497 (2020). https://doi.org/10.1007/s00500-020-05081-6

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Keywords

  • Volumetric analysis
  • Tumor nodule metastasis (TNM)
  • Segmentation
  • Classification
  • k-Means clustering