Deep Convolutional Level Set Method for Image Segmentation
DOI:
https://doi.org/10.5614/itbj.ict.res.appl.2017.11.3.5Keywords:
computer vision, convolutional neural network, deep learning, image processing, image segmentation, level set, machine learning, pattern recognition.Abstract
Level Set Method is a popular method for image segmentation. One of the problems in Level Set Method is finding the right initial surface parameter, which implicitly affects the curve evolution and ultimately the segmentation result. By setting the initial curve too far away from the target object, Level Set Method could potentially miss the target altogether, whereas by setting the initial curve as general as possible "? i.e. capturing the whole image "? makes Level Set Method susceptible to noise. Recently, deep-learning methods, especially Convolutional Neural Network (CNN), have been proven to achieve state-of-the-art performance in many computer vision tasks such as image classification and detection. In this paper, a new method is proposed, called Deep Convolutional Level Set Method (DCLSM). The idea is to use the CNN object detector as a prior for Level Set Method segmentation. Using DCLSM it is possible to significantly improve the segmentation accuracy and precision of the classic Level Set Method. It was also found that the prior used in the proposed method is the lower and upper bound for DCLSM's precision and recall, respectively.Downloads
References
Szeliski, R., Computer Vision: Algorithms and Applications, Springer Science & Business Media, 2010.
Ros, G., Ramos, S., Granados, M., Bakhtiary, A., Vazquez, D. & Lopez, A.M., Vision-based Offline-online Perception Paradigm for Autonomous driving, in 2015 IEEE Winter Conference on Applications of Computer Vision, pp. 231-238, 2015.
Ngo, T.A. & Carneiro, G., Left Ventricle Segmentation from Cardiac MRI Combining Level Set Methods with Deep Belief Networks, IEEE International Conference on Image Processing, pp. 695-699, 2013.
Long, J., Shelhamer, E. & Darrell, T., Fully Convolutional Networks for Semantic Segmentation, IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431-3440, 2015.
Sethian, J.A., A Fast Marching Level Set Method for Monotonically Advancing Fronts, Proceedings of the National Academy of Sciences 93(4), pp. 1591-1595, 1996.
Chan, T.F. & Vese, L.A., Active Contours without Edges, IEEE Transactions on image processing 10(2), pp. 266-277, 2001.
Samson, C., Blanc-Feraud, L., Aubert, G. & Zerubia, J., A Variational Model for Image Classification & Restoration, IEEE Transactions on Pattern Analysis & Machine Intelligence 22(5), pp. 460-472, 2000.
Pinto, N., Cox, D.D. & J. J. DiCarlo, Why is Real-world Visual Object Recognition Hard?, PLoS Comput Biol 4(1), pp. e27, 2008.
Krizhevsky, A., Sutskever, I. & Hinton, G.E., Imagenet Classification with Deep Convolutional Neural Networks, in Advances in neural information processing systems, pp. 1097-1105, 2012.
Simonyan, K. & Zisserman, A., Very deep convolutional networks for large-scale image recognition, arXiv preprint arXiv:1409.1556, 2014.
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V. & Rabinovich, A., Going Deeper with Convolutions, IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-9, 2015.
He, K., Zhang, X., Ren, S. & Sun J., Deep Residual Learning for Image Recognition, arXiv preprint arXiv:1512.03385, 2015.
Karpathy, A. & Fei-Fei, L., Deep Visual-semantic Alignments for Generating Image Descriptions, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3128-3137, 2015.
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C. & Fei-Fei, L., ImageNet Large Scale Visual Recognition Challenge, International Journal of Computer Vision (IJCV) 115(3), pp. 211-252, 2015.
Duan, C., Liang, Z., Bao, S., Zhu, H., Wang, S., Zhang, G., Chen, J.J. & Lu, H., A Coupled Level Set Framework for Bladder Wall Segmentation with Application to MR Cystography, IEEE transactions on medical imaging 29(3), pp. 903-915, 2010.
Vese, L.A. & Chan, T.F., A Multiphase Level Set Framework for Image Segmentation using the Mumford and Shah Model, International journal of computer vision 50(3), pp. 271-293, 2002.
Liu, S. & Li, J., Automatic Medical Image Segmentation using Gradient and Intensity Combined Level Set Method, in Engineering in Medicine and Biology Society, pp. 3118-3121, 2006.
Airouche, M., Bentabet, L. & Zelmat, M., Image Segmentation using Active Contour Model and Level Set Method Applied to Detect Oil Spills, in Proceedings of the World Congress on Engineering, Vol. 1, pp. 1-3, 2009.
Wang, T., Han, B. & Collomosse, J., Touchcut: Fast Image and Video Segmentation using Single-touch Interaction, Computer Vision and Image Understanding 120, pp. 14-30, 2014.
Rousson, M. & Paragios, N., Shape Priors for Level Set Representations, European Conference on Computer Vision, pp. 78-92, 2002.
Cha, K.H., Hadjiiski, L., Samala, R.K., Chan, H.P., Caoili, E.M. & Cohan, R.H., Urinary Bladder Segmentation in CT Urography using Deep-learning Convolutional Neural Network and Level Sets, Medical physics 43(4), pp. 1882-1896, 2016.
Li, S., Fevens, T., Krzyzak, A. & Li, S., Automatic Clinical Image Segmentation using Pathological Modeling, PCA and SVM, Engineering Applications of Artificial Intelligence 19(4), pp. 403-410, 2006.
Pawar, V. & Talbar, S., Object Recognition Technique based on Level Set Method and Neural Network, idea 40(2), 2012.
Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J. & Zisserman, A., The pascal visual object classes (voc) challenge, International Journal of Computer Vision 88, pp. 303-338, Jun. 2010.
Ren, S., He, K., Girshick, R. & Sun, J., Faster R-CNN: Towards Real-time Object Detection with Region Proposal Networks, in Advances in neural information processing systems, pp. 91-99, 2015.
Caselles, V., Kimmel, R. & Sapiro, G., Geodesic Active Contours, International journal of computer vision 22(1), pp. 61-79, 1997.
Kingma, D. & Ba, J., Adam: A Method for Stochastic Optimization, arXiv preprint arXiv:1412.6980, 2014.
Li, C., Xu, C., Gui, C. & Fox, M.D., Distance Regularized Level Set Evolution and Its Application to Image Segmentation, IEEE Transactions on Image Processing 19(12), pp. 3243-3254, 2010.
