A Novel ASNO Segmentation Technique for Segmenting the JUXTA Vascular Region

  • Authors

    • Arun B. Mathews
    • M M.K. Jeyakumar
    2018-08-15
    https://doi.org/10.14419/ijet.v7i3.27.17652
  • Lung tumour, improved linear iterative clustering, adjustable surface normal overlap, JUXTA vascular region.

  • Abstract

    In this modern era the clinical laboratory has greater attention to produce an accurate result for every test particularly in the area of lung tumour. The lung tumour is very essential to detect as well as to follow the treatment of many diseases like benign, malignant etc. This paper is focusing on the segmentation part to find the juxta vascular region. For finding the juxta vascular region in lung three stages are used. First stage is the image acquisition here input lung image is read and then resized. Second stage is the image pre-processing here improved linear iterative clustering technique is used .Third stage is the segmentation here the adjustable surface normal overlap is used. While using the above stages the output for juxta vascular region in the segmentation part is segmented clearly.

    The juxta vascular region is not clearly found in the previous paper. The research gap for this paper is to find the juxta vascular region in the lung. This juxta vascular region is present in the right side of the lung. Using the Adjustable Surface Normal Overlap (ASNO) segmentation the juxta vascular region is segmented clearly.

     

     

  • References

    1. [1] Kaur J, Garg N & Kaur D, “A survey of lung cancer detection techniques on CT scan imagesâ€, International Journal of Scientific & Engineering Research, Vol.5, No.6, (2014).

      [2] Kuhnigk JM, Hahn H, Hindennach M, Dicken V, Krass S & Peitgen HO, “Pulmonary lobe segmentation by anatomy-guided 3-DWatershed transformâ€, Proc. SPIE Med. Imag., Vol.5032, (2003), pp.1482–1490.

      [3] Zhou X, Hayashi T, Hara T, Fujita H, Yokoyama R, Kiryu T & Hoshi H, “Automatic recognition of pulmonary lobes and fissures frommulti-slice CT imagesâ€, Proc. SPIE Med. Imag., Vol.5370, (2004), pp.1629–1633.

      [4] Saita S, Kubo M, Kawata Y, Niki N, Ohmatsu H & Moriyama N, “An algorithm for the extraction of pulmonary fissures from low-dosemultislice CT imageâ€, Syst. Comput. Japan, Vol.37, (2006), pp.63–76.

      [5] Omnia E, Khaled M, Mohamed K & Hatem AK, “Automatic Detection of the Pulmonary Nodules from CT Imagesâ€, SAI Intelligent Systems Conference, (2015).

      [6] Smritikushwaha & Maya P, “Analysis of Lung Nodule and Classification based on SVM Classifier and Fuzzy C-Means Clustering for Effective Segmentationâ€, International Journal of Digital Application & Contemporary, Vol.4, No.8, (2016).

      [7] Golan R, Jacob C & Denzinger J, “Lung nodule detection in CT images using deep convolutional neural networksâ€, IEEE International Joint Conference on Neural Networks (IJCNN), (2016), pp.243-250.

      [8] Mangalakshmi A, Saranya G, Sobana Devi I & Syamala Devi V, “Lung Nodule Classification Using Deep Features in Ct Imagesâ€, International Journal of Advanced Research in Computer Science and Software Engineering, Vol.6, No.3, (2016).

      [9] Tao Z, Huiling L, Junjie Z & Hongbin S, “Pulmonary Nodule Detection Model Based on SVM and CT Image Feature-Level Fusion with Rough Setsâ€, Hindawi Publishing Corporation BioMed Research International, (2016), pp.1-13.

      [10] Sonali S & Mary SajinSanju I, “The detection of pulmonary nodules in CT images using heuristic approach segmentation and classificationâ€, ARPN Journal of Engineering and Applied Sciences, Vol.11, No.9, (2016), pp.5916 5920.

      [11] Naseem R, Alimgeer KS & Bashir T, “Recent trends in Computer Aided diagnosis of lung nodules in thorax CT scansâ€, IEEE International Conference on Innovations in Electrical Engineering and Computational Technologies (ICIEECT), (2017), pp.1-12.

      [12] Qaisar A, “Lung-Deep: A Computerized Tool for Detection of Lung Nodule Patterns using Deep Learning Algorithmsâ€, International Journal of Advanced Computer Science and Applications, Vol.8, No.10, (2017), pp.112-116.

      [13] Yim Y, Hong H, & Shin YG, “Hybrid lung segmentation in chest CT images for computer-aided diagnosisâ€, Proceedings of 7th International Workshop on Enterprise networking and Computing in Healthcare Industry, (2005), pp.378-383,

      [14] Pastorino U, “Early detection of lung cancerâ€, Respiration, Vol.73, No.1, (2006), pp.5-13.

      [15] Guy JA & Lehmann HP, “After detection: the improved accuracy of lung cancer assessment using radiologic computer-aided diagnosisâ€, Academic Radiology, Vol.23, (2016), pp.186–191.

      [16] Chan HP, Hadjiiski L, Zhou C & Sahiner B, “Computer-aided diagnosis of lung cancer and pulmonary embolism in computed tomography A reviewâ€, Academic Radiology, Vol.15, (2008), pp.535–555.

      [17] Theresa MM & Bharathi VS, “Computer aided diagnostic (CAD) for feature extraction of lungs in chest radiograph using different transform featureâ€, Biomedical Research, (2017), pp.1–8.

      [18] Way TW, Sahiner B, Chan HP, Hadjiiski L, Cascade PN & Chughtai A, “Computer-aided diagnosis of pulmonary nodules on CT scans: improvement of classification performance with nodule surface featuresâ€, Medical Physics, Vol.36, (2009), pp.3086–3098.

      [19] Messay T, Hardie RC & Rogers SK, “A new computationally efficient CAD system for pulmonary nodule detection in CT imageryâ€, Medical Image Analysis, Vol.14, (2010), pp.390–406.

      [20] Reeves A.P., Chan A.B., Yankelevitz D.F., Henschke C.I., Kressler B., Kostis W.J, “On measuring the change in size of pulmonary nodulesâ€, IEEE transactions on medical imaging, vol.25, pp.435–450, 2006.

      [21] De CarvalhoFilho AO, Silva AC, De Paiva AC, Nunes RA & Gattass M, “Computer-aided diagnosis system for lung nodules based on computed tomography using shape analysis, a genetic algorithm and SVMâ€, Medical & biological engineering & computing, (2016), pp.1–8.

      [22] Messay T, Hardie RC & Rogers SK, “A new computationally efï¬cient CAD system for pulmonary nodule detection in CT imageryâ€, Med. Image Anal., Vol.14, No.3, (2010), pp.390–406.

      [23] 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€, IEEETrans.Med.Imag.,Vol.20, No.7, (2001), pp.595–604.

      [24] Awai K, Murao K, Ozawa A, Komi M, Hayakawa H, Hori S & Nishimura Y, “Pulmonary nodules at chest CT: Effect of computer-aided diagnosis on radiologists detection performanceâ€, Radiology, Vol.230, No.2, (2004), pp.347-352.

      [25] Jiang CL, He SW, Zhang YD, Duan HX, Huang T, Huang YC, Li GF, Ma LJ, Zhou GB & Cao Y, “Air pollution and DNA methylation alterations in lung cancerâ€, Oncotarget, Vol.8, (2017), pp.1369-1391.

      [26] B Kassimbekova, G Tulekova, V Korvyakov (2018). Problems of development of aesthetic culture at teenagers by means of the Kazakh decorative and applied arts. Opción, Año 33. 170-186

      [27] Theresa MM & Bharathi VS, “Computer aided diagnostic (CAD) for feature extraction of lungs in chest radiograph using different transform featuresâ€, Biomedical Research, (2017), pp.1–8.

      [28] Shen W, Zhou M, Yang F, Yu D, Dong D, Yang C, Zang Y & Tian J, “Multi-crop convolutional neural networks for lung nodule malignancy suspiciousness classificationâ€, Pattern Recognition, Vol.61, (2017), pp.663–673.

      [29] Liu K & Kang G, “Multiview convolutional neural networks for lung nodule classificationâ€, International Journal of Imaging Systems and Technology, Vol.27, (2017), pp.12–22.

      [30] Wu L, Cheng JZ, Li S, Lei B, Wang T & Ni DF, “Fetal ultrasound image quality assessment with deep convolutional networksâ€, IEEE Transactions on Cybernetics, Vol.47, (2017), pp.1336–1349.

      [31] Koichi S & Hiroaki A, “Correlation between immune-related adverse events and efficacy in non small cell lung cancer treated with nivolumabâ€, Elsevier Lung Cancer, Vol.115, (2018), pp.71–74.

      [32] Shaohua C, Zhuoan C, Wenxin Q & Liyan J, “Exosomes as a liquid biopsy for lung cancerâ€, Elsevier, (2018), pp.46-51.

      [33] Daichi F, “Efficacy and safety of nivolumab in previously treated patients with nonsmall cell lung cancer: A multicenter retrospective cohort studyâ€, Elsevier, (2018), pp.14-20.

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  • How to Cite

    B. Mathews, A., & M.K. Jeyakumar, M. (2018). A Novel ASNO Segmentation Technique for Segmenting the JUXTA Vascular Region. International Journal of Engineering & Technology, 7(3.27), 47-52. https://doi.org/10.14419/ijet.v7i3.27.17652

    Received date: 2018-08-16

    Accepted date: 2018-08-16

    Published date: 2018-08-15