TY - JOUR T1 - BT Görüntülerden Akciğerin Tespiti için Süper Piksel ve Yapay Sinir Ağı Tabanlı Bir Yöntem TT - A Method based on Super Pixel and Artificial Neural Network for Lung Detection from CT images AU - Kılıkçıer, Çağlar AU - Yılmaz, Ersen PY - 2018 DA - August DO - 10.18185/erzifbed.384268 JF - Erzincan University Journal of Science and Technology PB - Erzincan Binali Yildirim University WT - DergiPark SN - 2149-4584 SP - 223 EP - 230 VL - 11 IS - 2 LA - tr AB - Tıbbi görüntülerden doku veya organlarınotomatik olarak tespit edilmesi bilgisayarlı görü alanının önemli çalışmakonularından birisidir. Bu çalışmadabilgisayarlı tomografi (BT) görüntülerinden akciğerin otomatik olarak tespitiiçin bir yöntem önerilmiştir. Önerilen yöntem süper pikselleri kullanan yapaysinir ağları (YSA) üzerinde temellendirilmiştir ve klinik karar desteksistemleri için ilk aşama olarak kullanılması hedeflenmektedir. Yönteminbaşarım incelemesi National Lung Screening Trial (NLST) veri tabanındaki BTgörüntüleri üzerinde gerçekleştirilmiştir. KW - YSA KW - Süper Piksel KW - Bölütleme KW - Tıbbi görüntü KW - YSA N2 - Detecting tissues andorgans from medical images is an important topic in computer vision. In thiswork a method is proposed for automatic lung detection from computer tomography(CT) images. The proposed method is based on artificial neural networks (ANN)using super pixels and it is aimed to use as the first stage of a clinicaldecision support system. The performance of the method is examined on the CTimages from the National Lung Screening Trial (NLST) database. CR - Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Susstrunk, S. 2010. SLIC Superpixels. EPFL Technical Report 149300, (June), 15. CR - Chu, J., Min, H., Liu, L., Lu, W. 2015. A novel computer aided breast mass detection scheme based on morphological enhancement and SLIC superpixel segmentation. Medical Physics, 42(7), 3859–69. CR - Enderle, J.D., Bronzino, J.D. 2011. Introduction to Biomedical Engineering. (3rd. ed.). Academic Press. CR - Fan, X., Zhang, G., Xia, X. 2008. Performance Evaluation of SVM in Image Segmentation. In 2008 IEEE International Workshop on Semantic Computing and Applications (160–165). IEEE. CR - Faust, O., Acharya, U.R., Tamura, T. 2012. Formal design methods for reliable computer-aided diagnosis: A review. IEEE Reviews in Biomedical Engineering, 5, 15–28. CR - Haas, S., Donner, R., Burner, A., Holzer, M., Langs, G. 2012. Superpixel-Based Interest Points for Effective Bags of Visual Words Medical Image Retrieval (58–68). Springer, Berlin, Heidelberg. CR - Haykin, S. 1998. Neural Networks: A Comprehensive Foundation (2nd ed.). New Jersey: Prentice Hall. CR - Lê, M., Unkelbach, J., Ayache, N., Delingette, H. 2016. Sampling image segmentations for uncertainty quantification. Medical Image Analysis, 34, 42–51. CR - Liao, X., Zhao, J., Jiao, C., Lei, L., Qiang, Y., Cui, Q. 2016. A segmentation method for lung parenchyma image sequences based on superpixels and a self-generating neural forest. PLoS ONE, 11(8). CR - Møller, M.F. 1993. A Scaled Conjugate Gradient Algorithm for Fast Supervised Learning Supervised Learning. Neural Networks, 6(November), 525–533. CR - Rojas, R. 1996. Neural networks: a systematic introduction. Neural Networks, 502. CR - Wang, X.Y., Wang, T., Bu, J. 2011. Color image segmentation using pixel wise support vector machine classification. Pattern Recognition, 44(4), 777–787. CR - Wang, X., Peng, Ma, Zhao J. 2016. Brain tumor CT image segmentation based on SLIC0 superpixels. In 2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) (427–431). IEEE. CR - Xu, J., Ishikawa, H., Wollstein, G., Schuman, J.S. 2011. 3D optical coherence tomography super pixel with machine classifier analysis for glaucoma detection. In 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (3395–3398). IEEE. CR - Yang, H.Y., Zhang, X.J., Wang, X.Y. 2014. LS-SVM-based image segmentation using pixel color-texture descriptors. CR - Yilmaz, E. 2016. Fetal State Assessment from Cardiotocogram Data Using Artificial Neural Networks. Journal of Medical and Biological Engineering, 36(6), 820–832. CR - Zhang, W., Zhang X., Zhao J., Qiang Y., Tian Q., Tang X. 2017. A segmentation method for lung nodule image sequences based on superpixels and density-based spatial clustering of applications with noise. PLoS ONE, 12(9). CR - Zhou, S.K. 2016. Medical Image Recognition, Segmentation and Parsing. Medical Image Recognition, Segmentation and Parsing. USA: Academic Press. UR - https://doi.org/10.18185/erzifbed.384268 L1 - https://dergipark.org.tr/en/download/article-file/529043 ER -