BT Görüntülerden Akciğerin Tespiti için Süper Piksel ve Yapay Sinir Ağı Tabanlı Bir Yöntem

Çağlar Kılıkçıer; Ersen Yılmaz

doi:10.18185/erzifbed.384268

Araştırma Makalesi

A Method based on Super Pixel and Artificial Neural Network for Lung Detection from CT images

Yıl 2018, Cilt: 11 Sayı: 2, 223 - 230, 31.08.2018

Çağlar Kılıkçıer Ersen Yılmaz

https://doi.org/10.18185/erzifbed.384268

Öz

Detecting tissues and
organs from medical images is an important topic in computer vision. In this
work 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 clinical
decision support system. The performance of the method is examined on the CT
images from the National Lung Screening Trial (NLST) database.

Anahtar Kelimeler

ANN, Medical Image, Super Pixel, Segmentation

Kaynakça

Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Susstrunk, S. 2010. SLIC Superpixels. EPFL Technical Report 149300, (June), 15.
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.
Enderle, J.D., Bronzino, J.D. 2011. Introduction to Biomedical Engineering. (3rd. ed.). Academic Press.
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.
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.
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.
Haykin, S. 1998. Neural Networks: A Comprehensive Foundation (2nd ed.). New Jersey: Prentice Hall.
Lê, M., Unkelbach, J., Ayache, N., Delingette, H. 2016. Sampling image segmentations for uncertainty quantification. Medical Image Analysis, 34, 42–51.
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).
Møller, M.F. 1993. A Scaled Conjugate Gradient Algorithm for Fast Supervised Learning Supervised Learning. Neural Networks, 6(November), 525–533.
Rojas, R. 1996. Neural networks: a systematic introduction. Neural Networks, 502.
Wang, X.Y., Wang, T., Bu, J. 2011. Color image segmentation using pixel wise support vector machine classification. Pattern Recognition, 44(4), 777–787.
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.
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.
Yang, H.Y., Zhang, X.J., Wang, X.Y. 2014. LS-SVM-based image segmentation using pixel color-texture descriptors.
Yilmaz, E. 2016. Fetal State Assessment from Cardiotocogram Data Using Artificial Neural Networks. Journal of Medical and Biological Engineering, 36(6), 820–832.
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).
Zhou, S.K. 2016. Medical Image Recognition, Segmentation and Parsing. Medical Image Recognition, Segmentation and Parsing. USA: Academic Press.