El proceso de segmentación automática de lesiones patológicas en imágenes de mamografías (página 3)

41. A. Hill, C.T., A. Brett, "A framework for automatic landmark identification using a new method of nonrigid correspondence", in IEEE Transactions on Pattern Analysis and Machine Intelligence. 2000. p. 241-251.

42. A. Hill, C.T. "Automatic Landmark Generation for Point Distribution Models". in Proceedings of the British MachineVision Conference. 1994.

43. T. Cootes, C.T., A. Lanitis. Active Shape Models: Evaluation of a Multi-Resolution Method for Improving Image Search. in Proceedings of the British Machine Vision Conference. BMVC1994. 1994.

44. A. Hill, C.T., "Model-Based Image Interpretation Using Genetic Algorithms", in Image and Vision Computing. 1992. p. 295-300.

45. R. Bowden, T.M., M. Sarhadi, Non-linear Statistical Models for the 3D Reconstruction of Human Pose and Motion from Monocular Image Sequences, in Image and Vision Computing. 2000. p. 729 – 737.

46. Mariño, J.T.C., Segmentación en imágenes de resonancia magnética del cerebro humano mediante la aplicación de Modelos Deformables, in Facultad de Ingeniería Informática. 2005, Universidad de Ciego de Ávila: Ciego de Ávila. p. 79.

47. Falcao A.X., U.J.K., Samarasekera S., Sharma,S., User-steered image segmentation paradigms:live wire and live lane, in Graphical Models and Image Processing. 1998. p. 233-260.

48. Falcao A.X., U.J.K. Segmentation of 3D objects using live wire. in SPIE on Medical Imaging 1997. 1997. Newport Beach, CA.

49. Falcao A.X., U.J.K., Miyazawa F.K., An ultra-fast user-steered segmentation paradigm: live-wire-on-the-fly, in IEEE Transactions on Medical Imaging. 2000. p. 55 – 62.

50. Falcao A.X., U.J.K., Samarasekera S., Hirsch B.E. User-steered image boundary segmentation. in Proceedings of SPIE on Medical Imaging. 1996. Newport Beach, CA.

51. Mortensen E.N., B.W.A., Interactive segmentation with intelligent scissors, in Graphical Models and Image Processing. 1998. p. 349 – 384.

52. Jianming Liang, T.M., Demetri Terzopoulos, United Snakes, in Medical Image Analysis. 2006. p. 215 – 233.

53. Haykin, S., Neural Networks. A Comprehensive Foundation. 1994: Prentice Hall.

54. P. P. V. D. S. Ben, J.A.K., An introduction to neural networks. 1993.

55. J. M. Anil, K.J., K.M. Mohiuddin., Artificial neural networks: A tutorial, in IEEE Computer. 1996.

56. Warren, S.S. Frequently Asked Questions. news://comp.ai.neural-nets. 1997 [cited.

57. Alejandro Díaz Sotolongo, Combinación de parámetros de forma y textura para el análisis del cáncer de mamas, in Facultad de Informática. 2010, Universidad de Ciego de Ávila: Ciego de Ávila.

58. L. Tabár, P. B. Dean, Atlas de mamogrfía. Ed. Doyma.

59. Yosvany López Alvarez, Andra Novoa Velazquez, Herramientas de procesamiento digital de imágenes e inteligencia artificial como apoyo al diagnóstico del cáncer de mamas a partir de imágenes de mamografías., in Facultad de Ingeniería Informática. 2007, Universidad de Ciego de Ávila: Ciego de Ávila. p. 102.

60. Miguel A. Guevara, A.S., Helena Oliveira, Maria de Lourdes Pereira, Fernando Morgado. Segmentation and Morphometry of Histological Sections Using Deformable Models: A New Tool for Evaluating Testicular Histopathology. in CIARP 2003, LNCS 2905. 2003. Havana,Cuba.

61. Artur Chodorowski, U.M., Morgan Langille, Ghassan Hamarneh, Color Lesion Boundary Detection Using Live Wire, in Medical Imaging. 2005.

62. Ghassan Hamarneh, J.H.D. Segmentation using DT-Snakes and DT-Livewire. in IEEE International Symposium on Signal Processing and Information Technology. 2006.

63. Kelvin Poon, G.H., Rafeef Abugharbieh, Segmentation of Complex Objects with Non-Spherical Topologies from Volumetric Medical Images using 3D Livewire. 2006.

64. Michiel G J Kallenberg, Mari¨ette Lokate, Carla H van Gils and Nico Karssemeijer. Automatic breast density segmentation: an integration of different approaches, PHYSICS IN MEDICINE AND BIOLOGY, 56 (2011) 2715–2729, 2011.

65. Yusbel Chávez Castilla, Nueva propuesta para la segmentación automática de núcleos de células cervicales, in Facultad de Ingeniería. 2010, Universidad de Ciego de Ávila: Ciego de Ávila.

66. Karsten Rodenacker, E.B., A feature set for cytometry on digitized microscopic images, in Analytical Cellular Pathology. 2003. p. 1 – 36.

67. Sepehr M.H.Jamarani, G.R.-r., Hamid Behnam. A Novel Method for Breast Cancer Prognosis Using Wavelet Packet Based Neural Network. in Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference. 2005. Shanghai, China.

68. Ping Zhang, B.V., Kuldeep Kumar, Neural vs. statistical classifier in conjunction with genetic algorithm based feature selection, in Pattern Recognition Letters. 2004. p. 1 – 11.

69. Lúcio F.A. Campos, A.C.S., Allan Kardec Barros. Diagnosis of Breast Cancer in Digital Mammograms Using Independent Component Analysis and Neural Networks. in CIARP 2005, LNCS 3773. 2005. Havana, Cuba.

70. Dawid, A.P., "Application of general propagation algorithm for probabilistic expert systems", in Statistics and Computing. 1992. p. 25 – 36.

71. Howard, J.E.M.R.A., Readings on the principles and applications of decision theory, J.E.M. R. A. H. , Ed. : Strategic Decision Group, Editor. 1983. p. 719 – 762.

72. Lauritzen, D.J.S.S.L., Local computations with probabilities on graphical structures and their application to expert systems. Journal of the Royal Statistical Society, 1988. 50: p. 240-265.

73. Heckerman, D., A tutorial on learning with Bayesian networks. 1996: WA: Microsoft Research, Redmond, Tech. Rep. No. MSR-TR-95-06.

74. Haralick, L.S.R., Computer and Robot Vision. 1993: Addison-Wesley.

75. Kohavi, R., A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection, in International Joint Conference on Artificial Intelligence (IJCAI). 1995.

76. José Salvado, B.R. Detection of Calcifications in Digital Mammograms using Wavelet Analysis and Contrast Enhancement. 2005. Faro, Portugal.

Anexos

Anexo # 1: (a) Representación de una imagen en escala de intensidades (b) Su equivalente superficie potencial (c) Gradiente de la imagen (d) Su equivalente superficie

Anexo # 2: Red Neuronal Artificial Bayesiana

Anexo # 3: Modelo no lineal de una neurona artificial

Anexo # 4: Vector de características de la patología

Anexo # 5: Clases de anormalidades presentes en el cáncer de mamas

Autor:

MSc. Yuniel Olivares Martínez*

MS. c Arnaldo Faustino**

MSc. Andra Novoa Velázquez

* Universidad de Ciego de Ávila "Máximo Gómez Báez" Facultad de Ingeniería

**Centro de Educación Pre – universitaria de Longonjo-Huambo e Investigador