An efficient brain tumor image classifier by combining multi-pathway cascaded deep neural network and handcrafted features in MR images (original) (raw)

References

1. Menze BH, Jakab A, Bauer S, Kalpathy-Cramer J, Farahani K, Kirby J, Burren Y, Porz N, Slotboom J, Wiest R et al (2015) The multimodal brain tumor image segmentation benchmark (brats). IEEE Trans Med Imaging 34(10):1993–2024
   Article Google Scholar
2. Bauer S, Wiest R, Nolte L. -P., Reyes M (2013) A survey of mri-based medical image analysis for brain tumor studies. Phys Med Biol 58(13):R97
   Article Google Scholar
3. Bal A, Banerjee M, Sharma P, Maitra M (2020) Gray matter segmentation and delineation from positron emission tomography (pet) image. In: Emerging technology in modelling and graphics. Springer, pp 359–372
4. Menze BH, Jakab A, Bauer S, Kalpathy-Cramer J, Farahani K, Kirby J, Burren Y, Porz N, Slotboom J, Wiest R et al (2014) The multimodal brain tumor image segmentation benchmark (brats). IEEE Trans Med Imaging 34(10):1993–2024
   Article Google Scholar
5. Lyksborg M, Puonti O, Agn M, Larsen R (2015) An ensemble of 2d convolutional neural networks for tumor segmentation. In: Scandinavian conference on image analysis. Springer, pp 201–211
6. Kleesiek J, Biller A, Urban G, Kothe U, Bendszus M, Hamprecht F (2014) Ilastik for multi-modal brain tumor segmentation. In: Proceedings MICCAI BraTS (Brain tumor segmentation challenge), pp 12–17
7. Dvořák P, Menze B (2015) Local structure prediction with convolutional neural networks for multimodal brain tumor segmentation. In: International MICCAI workshop on medical computer vision. Springer, pp 59–71
8. Bal A, Banerjee M, Chakrabarti A, Sharma P (2018) Mri brain tumor segmentation and analysis using rough-fuzzy c-means and shape based properties. Journal of King Saud University-Computer and Information Sciences
9. Bal A, Banerjee M, Sharma P, Maitra M (2018) Brain tumor segmentation on mr image using k-means and fuzzy-possibilistic clustering. In: 2018 2nd international conference on electronics, materials engineering & nano-technology (IEMENTech). IEEE, pp 1–8
10. Bal A, Banerjee M, Sharma P, Chaki R (2020) A multi-class image classifier for assisting in tumor detection of brain using deep convolutional neural network. In: Advanced computing and systems for security. Springer, pp 93–111
11. Hussain S, Anwar SM, Majid M (2017) Brain tumor segmentation using cascaded deep convolutional neural network. In: 2017 39th annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE, pp 1998– 2001
12. Pereira S, Pinto A, Alves V, Silva CA (2016) Brain tumor segmentation using convolutional neural networks in mri images. IEEE Trans Med Imaging 35(5):1240–1251
    Article Google Scholar
13. Havaei M, Davy A, Warde-Farley D, Biard A, Courville A, Bengio Y, Pal C, Jodoin P-M, Larochelle H (2017) Brain tumor segmentation with deep neural networks. Med Image Anal 35:18–31
    Article Google Scholar
14. Taheri S, Ong SH, Chong V (2010) Level-set segmentation of brain tumors using a threshold-based speed function. Image Vis Comput 28(1):26–37
    Article Google Scholar
15. Havaei M, Larochelle H, Poulin P, Jodoin P-M (2016) Within-brain classification for brain tumor segmentation. Int J Comput Assist Radiol Surg 11(5):777–788
    Article Google Scholar
16. Georgiadis P, Cavouras D, Kalatzis I, Daskalakis A, Kagadis GC, Sifaki K, Malamas M, Nikiforidis G, Solomou E (2008) Improving brain tumor characterization on mri by probabilistic neural networks and non-linear transformation of textural features. Comput Methods Programs Biomed 89(1):24–32
    Article Google Scholar
17. Wu M-N, Lin C-C, Chang C-C (2007) Brain tumor detection using color-based k-means clustering segmentation. In: Third international conference on intelligent information hiding and multimedia signal processing (IIH-MSP 2007), vol 2. IEEE, pp 245–250
18. Clark MC, Hall LO, Goldgof DB, Velthuizen R, Murtagh FR, Silbiger MS (1998) Automatic tumor segmentation using knowledge-based techniques. IEEE Trans Med Imaging 17(2):187– 201
    Article CAS Google Scholar
19. Dong H, Yang G, Liu F, Mo Y, Guo Y (2017) Automatic brain tumor detection and segmentation using u-net based fully convolutional networks. In: Annual conference on medical image understanding and analysis. Springer, pp 506–517
20. Sompong C, Wongthanavasu S (2016) Brain tumor segmentation using cellular automata-based fuzzy c-means. In: Computer science and software engineering (JCSSE), 2016 13th international joint conference on. IEEE, pp 1–6
21. Kwon D, Shinohara RT, Akbari H, Davatzikos C (2014) Combining generative models for multifocal glioma segmentation and registration. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 763–770
22. Tustison NJ, Shrinidhi K, Wintermark M, Durst CR, Kandel BM, Gee JC, Grossman MC, Avants BB (2015) Optimal symmetric multimodal templates and concatenated random forests for supervised brain tumor segmentation (simplified) with antsr. Neuroinformatics 13(2):209–225
    Article Google Scholar
23. Chen L-C, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2017) Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans Pattern Anal Machine Intell 40(4):834–848
    Article Google Scholar
24. Casamitjana A, Puch S, Aduriz A, Sayrol E, Vilaplana V (2016) 3d convolutional networks for brain tumor segmentation. In: Proceedings of the MICCAI challenge on multimodal brain tumor image segmentation (BRATS), pp 65–68
25. McKinley R, Meier R, Wiest R (2018) Ensembles of densely-connected cnns with label-uncertainty for brain tumor segmentation. In: International MICCAI brainlesion workshop. Springer, pp 456–465
26. Kamnitsas K, Ledig C, Newcombe VF, Simpson JP, Kane AD, Menon DK, Rueckert D, Glocker B (2017) Efficient multi-scale 3d cnn with fully connected crf for accurate brain lesion segmentation. Med Image Anal 36:61–78
    Article Google Scholar
27. Hussain S, Anwar SM, Majid M (2018) Segmentation of glioma tumors in brain using deep convolutional neural network. Neurocomputing 282:248–261
    Article Google Scholar
28. Chaplot S, Patnaik L, Jagannathan N (2006) Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network. Biomed Signal Process Control 1 (1):86–92
    Article Google Scholar
29. Pawlak Z (2012) Rough sets: Theoretical aspects of reasoning about data, vol 9. Springer Science & Business Media, New York
    Google Scholar
30. Saha R, Phophalia A, Mitra SK (2016) Brain tumor segmentation from multimodal mr images using rough sets. In: International conference on computer vision Graphics, and Image processing. Springer, pp 133–144
31. Maji P, Pal SK (2007) Rough set based generalized fuzzy _c_-means algorithm and quantitative indices. IEEE Trans Syst Man Cybern Part B (Cybernetics) 37(6):1529–1540
    Article Google Scholar
32. Maji P, Pal SK (2008) Maximum class separability for rough-fuzzy c-means based brain mr image segmentation. Trans Rough Sets 9:114–134
    Google Scholar
33. Hamamci A, Kucuk N, Karaman K, Engin K, Unal G (2012) Tumor-cut: Segmentation of brain tumors on contrast enhanced mr images for radiosurgery applications. IEEE Trans Med Imaging 31 (3):790–804
    Article Google Scholar
34. Görlitz L, Menze B, Weber M-A, Kelm B, Hamprecht F (2007) Semi-supervised tumor detection in magnetic resonance spectroscopic images using discriminative random fields. Pattern Recogn 224–233
35. Mohammed ZF, Abdulla AA (2020) Thresholding-based white blood cells segmentation from microscopic blood images. UHD J Sci Technol 4(1):9–17
    Article Google Scholar
36. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. nature 521(7553):436
    Article CAS Google Scholar
37. Khan H, Shah PM, Shah MA, ul Islam S, Rodrigues JJ (2020) Cascading handcrafted features and convolutional neural network for iot-enabled brain tumor segmentation, Computer Communications
38. Bao P, Zhang L, Wu X (2005) Canny edge detection enhancement by scale multiplication. IEEE Trans Pattern Anal Machine Intell 27(9):1485–1490
    Article Google Scholar
39. Gonzalez CI, Melin P, Castro JR, Mendoza O, Castillo O (2016) An improved sobel edge detection method based on generalized type-2 fuzzy logic. Soft Comput 20(2):773–784
    Article Google Scholar
40. Shrivakshan G, Chandrasekar C (2012) A comparison of various edge detection techniques used in image processing. Int J Comput Sci Issues (IJCSI) 9(5):269
    Google Scholar
41. Johnson R (1990) Contrast based edge detection. Pattern Recognit 23(3-4):311–318
    Article Google Scholar
42. Liu L, Zhao L, Long Y, Kuang G, Fieguth P (2012) Extended local binary patterns for texture classification. Image Vis Comput 30(2):86–99
    Article Google Scholar
43. Ilonen J, Kamarainen J-K, Paalanen P, Hamouz M, Kittler J, Kalviainen H (2008) Image feature localization by multiple hypothesis testing of gabor features. IEEE Trans Image Process 17(3):311–325
    Article Google Scholar
44. Liu Y, Liu S, Wang Z (2015) Multi-focus image fusion with dense sift. Information Fusion 23:139–155
    Article Google Scholar
45. Wang G, Li W, Vercauteren T, Ourselin S (2019) Automatic brain tumor segmentation based on cascaded convolutional neural networks with uncertainty estimation. Front Computat Neurosci 13:56
    Article Google Scholar
46. Myronenko A, Hatamizadeh A (2020) Robust semantic segmentation of brain tumor regions from 3d mris. In: International MICCAI brainlesion workshop. Springer, pp 82–89
47. Beers A, Chang K, Brown J, Sartor E, Mammen C, Gerstner E, Rosen B, Kalpathy-cramer J (2020) Sequential 3d u-nets for biologically-informed brain tumor segmentation, arXiv:1709.02967
48. Shen L, Anderson T (2020) Multimodal brain mri tumor segmentation via convolutional neural networks
49. Wu W, Li D, Du J, Gao X, Gu W, Zhao F, Feng X, Yan H (2020) An intelligent diagnosis method of brain mri tumor segmentation using deep convolutional neural network and svm algorithm. Computational and Mathematical Methods in Medicine
50. Zeineldin RA, Karar ME, Coburger J, Wirtz CR, Burgert O (2020) Deepseg: Deep neural network framework for automatic brain tumor segmentation using magnetic resonance flair images, International Journal Of Computer Assisted Radiology And Surgery
51. Isensee F, Kickingereder P, Wick W, Bendszus M, Maier-Hein KH (2017) Brain tumor segmentation and radiomics survival prediction: Contribution to the brats 2017 challenge. In: International MICCAI brainlesion workshop. Springer, pp 287–297
52. Soltaninejad M, Zhang L, Lambrou T, Allinson N, Ye X (2020) Multimodal mri brain tumor segmentation using random forests with features learned from fully convolutional neural network, arXiv:1704.08134
53. Zikic D, Ioannou Y, Brown M, Criminisi A (2014) Segmentation of brain tumor tissues with convolutional neural networks. Proceedings MICCAI-BRATS 36–39
54. Li C, Gore JC, Davatzikos C (2014) Multiplicative intrinsic component optimization (mico) for mri bias field estimation and tissue segmentation. Magnetic Resonance Imaging 32(7):913–923
    Article CAS Google Scholar
55. Nyúl LG, Udupa JK, Zhang X (2000) New variants of a method of mri scale standardization. IEEE Trans Med Imaging 19(2):143–150
    Article Google Scholar
56. Bal A, Banerjee M, Sharma P, Maitra M (2019) An efficient wavelet and curvelet-based pet image denoising technique. Med Biol Eng Comput 57(12):2567–2598
    Article Google Scholar
57. Bal A, Banerjee M, Chaki R, Sharma P (2020) An efficient method for pet image denoising by combining multi-scale transform and non-local means. Multimed Tools Appl 79(39):29087–29120
    Article Google Scholar
58. Wells WM, Grimson WEL, Kikinis R, Jolesz FA (1996) Adaptive segmentation of mri data. IEEE Trans Med Imaging 15(4):429–442
    Article CAS Google Scholar
59. Van Leemput K, Maes F, Vandermeulen D, Suetens P (1999) Automated model-based bias field correction of mr images of the brain. IEEE Trans Med Imaging 18(10):885–896
    Article Google Scholar
60. Pham DL, Prince JL (1999) Adaptive fuzzy segmentation of magnetic resonance images. IEEE Trans Med Imaging 18(9):737–752
    Article CAS Google Scholar
61. Ansari MA, Kurchaniya D, Dixit M (2017) A comprehensive analysis of image edge detection techniques. Int J Multimed Ubiquit Eng 12(11):1–12
    Article Google Scholar
62. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vision 60(2):91–110
    Article Google Scholar
63. Wong SC, Gatt A, Stamatescu V, McDonnell MD (2016) Understanding data augmentation for classification: when to warp?. In: Digital image computing: Techniques and applications (DICTA), 2016 international conference on. IEEE, pp 1–6
64. Fawzi A, Samulowitz H, Turaga D, Frossard P (2016) Adaptive data augmentation for image classification. In: Image processing (ICIP), 2016 IEEE international conference on. Ieee, pp 3688–3692
65. Wang J, Perez L (2017) The effectiveness of data augmentation in image classification using deep learning. Tech. rep. Technical report
66. Urban G, Bendszus M, Hamprecht F, Kleesiek J (2014) Multi-modal brain tumor segmentation using deep convolutional neural networks. In: MICCAI braTS (Brain Tumor Segmentation) Challenge Proceedings, winning contribution, pp 31–35
67. Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the thirteenth international conference on artificial intelligence and statistics, pp 249–256
68. Ye C, Zhao C, Yang Y, Fermüller C, Aloimonos Y (2016) Lightnet: a versatile, standalone matlab-based environment for deep learning. In: Proceedings of the 24th ACM international conference on multimedia. ACM, pp 1156–1159
69. Mallat S (2009) A wavelet tour of signal processing: The sparse way 3th edn
70. LeCun Y, Boser B, Denker JS, Henderson D, Howard RE, Hubbard W, Jackel LD (1989) Backpropagation applied to handwritten zip code recognition. Neural Comput 1(4):541–551
    Article Google Scholar
71. Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: A simple way to prevent neural networks from overfitting. J Machine Learn Res 15(1):1929–1958
    Google Scholar
72. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105
73. Maas AL, Hannun AY, Ng AY (2013) Rectifier nonlinearities improve neural network acoustic models. In: Proc. icml, vol 30, p 3
74. Zhao L, Jia K (2016) Multiscale cnns for brain tumor segmentation and diagnosis, Computational and mathematical methods in medicine
75. Gonzales RC, Woods RE (2002) Digital image processing
76. Bakas S, Akbari H, Sotiras A, Bilello M, Rozycki M, Kirby JS, Freymann JB, Farahani K, Davatzikos C (2017) Advancing the cancer genome atlas glioma mri collections with expert segmentation labels and radiomic features. Scientific data 4:170117
    Article Google Scholar
77. Bakas S, Akbari H, Sotiras A, Bilello M, Rozycki M, Kirby J, Freymann J, Farahani K, Davatzikos C (2020) Segmentation labels and radiomic features for the pre-operative scans of the tcga-lgg collection, The Cancer Imaging Archive 286

Download references