Segmental HOG: New descriptor for glomerulus detection in kidney microscopy image

BMC Bioinformatics

Volumn 16, Issue 1, 2015, Pages

  Kato, Tsuyoshi ^a,b   Relator, Raissa ^a   Ngouv, Hayliang ^a   Hirohashi, Yoshihiro ^a   Takaki, Osamu ^c   Kakimoto, Tetsuhiro ^d   Okada, Kinya ^d  

^a GUNMA UNIVERSITY   (Japan)

^b OCHANOMIZU UNIVERSITY   (Japan)

^c GUNMA UNIVERSITY HOSPITAL   (Japan)

^d MITSUBISHI TANABE PHARMA CORPORATION   (Japan)

Author keywords

Computer vision; Desmin immunostaining; Dynamic programming; Glomerular injury marker; Glomerulus detection; Microscopy image analysis; Support vector machine

Indexed keywords

COMPUTER PROGRAMMING; COMPUTER VISION; DYNAMIC PROGRAMMING; SUPPORT VECTOR MACHINES;

DETECTION PERFORMANCE; GLOMERULAR INJURY MARKER; HIGH-QUALITY SEGMENTATION; HISTOGRAM OF ORIENTED GRADIENTS; IMMUNOSTAINING; MICROSCOPY IMAGE ANALYSIS; REAL-WORLD IMAGE DATA; SEGMENTATION TECHNIQUES;

OBJECT DETECTION;

DESMIN;

ALGORITHM; GLOMERULUS; HUMAN; METABOLISM; MICROSCOPY; PATHOLOGY; PROCEDURES; SUPPORT VECTOR MACHINE;

ALGORITHMS; DESMIN; HUMANS; KIDNEY GLOMERULUS; MICROSCOPY; SUPPORT VECTOR MACHINE;

EID: 84942572607     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/s12859-015-0739-1     Document Type: Article

References (28)

1. Zhang J, Hu J. Glomerulus extraction by optimizing the fitting curve. In: ISCID, vol. 1. IEEE: 2008. p. 169-72.
2. Ma J, Zhang J, Hu J. Glomerulus extraction by using genetic algorithm for edge patching. In: IEEE CEC. Trondheim, Norway: IEEE: 2009. p. 2474-9.
3. Hughson MD, Puelles VG, Hoy WE, Douglas-Denton RN, Mott SA, Bertram JF. Hypertension, glomerular hypertrophy and nephrosclerosis: the effect of race. Nephrol Dial Transplant. 2013; 29(7):1399-409.
4. Rasch R, Lauszus F, Thomsen JS, Flyvbjerg A. Glomerular structural changes in pregnant, diabetic, and pregnant-diabetic rats. APMIS. 2005; 113(7-8):465-72.
5. Munder S, Gavrila DM. An experimental study on pedestrian classification. IEEE Trans Pattern Anal Mach Intell. 2006; 28(11):1863-8.
6. Maji S, Berg A, Malik J. Classification using intersection kernel support vector machines is efficient. In: IEEE CVPR. Anchorage, AK: IEEE: 2008. p. 1-8.
7. Papageorgiou C, Poggio T. A trainable system for object detection. Int J Comput Vision. 2000; 38(1):15-33.
8. Viola P, Jones MJ. Robust real-time face detection. Int J Comput Vision. 2004; 57(2):137-54.
9. Dalal N, Triggs B. Histograms of oriented gradients for human detection. In: IEEE CVPR, vol. 1. Washington, DC, USA: IEEE Computer Society: 2005. p. 886-93.
10. Hirohashi Y, Relator R, Kakimoto T, Saito R, Horai Y, Fukunari A, et al. Automated quantitative image analysis of glomerular desmin immunostaining as a sensitive injury marker in spontaneously diabtic torii rats. J Biomed Image Process. 2014; 1(1):20-8.
11. Kakimoto T, Okada K, Hirohashi Y, Relator R, Kawai M, Iguchi T, et al. Automated image analysis of a glomerular injury marker desmin in SDT rats treated with losartan. J Endocrinol. 2014; 222(1):43-51.
12. Kakimoto T, Okada K, Fujitaka K, Nishio M, Kato T, Fukunari A, et al. Quantitative analysis of markers of podocyte injury in the rat puromycin aminonucleoside nephropathy model. Exp Toxicol Pathol. 2015; 67(2):171-7.
13. Lowe DG. Distinctive image features from scale-invariant keypoints. Int J Comput Vision. 2004; 60(2):91-110.
14. Ahonen T, Hadid A, Pietikainen M. Face description with local binary patterns: application to face recognition. IEEE Trans Pattern Anal Mach Intell. 2006; 28(12):2037-41.
15. Jiang H, He B, Fang D, Ma Z, Yang B, Zhang L. A region growing vessel segmentation algorithm based on spectrum information. Comput Math Methods Med. 2013. doi: 10.1155/2013/743870.
16. Cremers D, Rousson M, Deriche R. A review of statistical approaches to level set segmentation: Integrating color, texture, motion and shape. Int J Comput Vision. 2007; 72(2):195-215.
17. Kass M, Witkin A, Terzopoulos D. Snakes: Active contour models. Int J Comput Vision. 1988; 1(4):321-31.
18. Wang T, Cheng I, Basu A. Fluid vector flow and applications in brain tumor segmentation. IEEE Trans Biomed Eng. 2009; 56(3):781-9.
19. Xie X. Active contouring based on gradient vector interaction and constrained level set diffusion. IEEE Trans Image Process. 2010; 19(1):154-64.
20. Ciresan D, Giusti A, Gambardella LM, Schmidhuber J. Deep neural networks segment neuronal membranes in electron microscopy images In: Pereira F, Burges CJC, Bottou L, Weinberger KQ, editors. NIPS. Curran Associates, Inc: 2012. p. 2852-60.
21. Kvarnström M, Logg K, Diez A, Bodvard K, Käll M. Image analysis algorithms for cell contour recognition in budding yeast. Opt Express. 2008; 16(17):12943-57.
22. Rozanov JA, Elson CM. Markov random fields. New York: Springer; 1982.
23. Viterbi A. Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Trans Inf Theor. 2006; 13(2):260-269.
24. Yanover C, Meltzer T, Weiss Y. Linear programming relaxations and belief propagation - an empirical study. J Mach Learn Res. 2006; 7:1887-907.
25. Weiss Y, Freeman WT. On the optimality of solutions of the max-product belief-propagation algorithm in arbitrary graphs. IEEE Trans Inf Theor. 2006; 47(2):736-44.
26. Globerson A, Jaakkola TS. Fixing max-product: Convergent message passing algorithms for MAP lp-relaxations. In: Advances in Neural Information Processing Systems 20, Proceedings of the Twenty-First Annual Conference on Neural Information Processing Systems, Vancouver, British Columbia, Canada, December 3-6, 2007. Cambridge MA: MIT Press: 2007. p. 553-60.
27. Schölkopf B, Smola AJ. Learning with Kernels. Cambridge, MA: MIT Press; 2002.
28. Shawe-Taylor J, Cristianini N. Kernel Methods for Pattern Analysis. Cambridge, MA: Cambridge University Press; 2004.