Asian Pacific Journal of Cancer Prevention

Asian Pacific Journal of Cancer Prevention (아시아태평양암예방학회)
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Advances in Optimal Detection of Cancer by Image Processing; Experience with Lung and Breast Cancers

  • Mohammadzadeh, Zeinab (Health Information Management Department, School of Allied Medical Sciences, Tehran University of Medical Sciences) ;
  • Safdari, Reza (Health Information Management Department, School of Allied Medical Sciences, Tehran University of Medical Sciences) ;
  • Ghazisaeidi, Marjan (Health Information Management Department, School of Allied Medical Sciences, Tehran University of Medical Sciences) ;
  • Davoodi, Somayeh (Health Information Management Department, School of Allied Medical Sciences, Tehran University of Medical Sciences) ;
  • Azadmanjir, Zahra (Health Information Management Department, School of Allied Medical Sciences, Tehran University of Medical Sciences)
  • Published : 2015.09.02
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Abstract

Clinicians should looking for techniques that helps to early diagnosis of cancer, because early cancer detection is critical to increase survival and cost effectiveness of treatment, and as a result decrease mortality rate. Medical images are the most important tools to provide assistance. However, medical images have some limitations for optimal detection of some neoplasias, originating either from the imaging techniques themselves, or from human visual or intellectual capacity. Image processing techniques are allowing earlier detection of abnormalities and treatment monitoring. Because the time is a very important factor in cancer treatment, especially in cancers such as the lung and breast, imaging techniques are used to accelerate diagnosis more than with other cancers. In this paper, we outline experience in use of image processing techniques for lung and breast cancer diagnosis. Looking at the experience gained will help specialists to choose the appropriate technique for optimization of diagnosis through medical imaging.

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References

  1. Abdel-Qader I, Shen L, Jacobs C, et al (2006). Unsupervised detection of suspicious tissue using data modeling and PCA. Int J Biomed Imaging, 2006, 57850
  2. Abdulbaki AS (2012). Skin cancer image segmentation & detection by using unsupervised neural networks (UNN).
  3. Abramoff MD, Magalhaes PJ, Ram SJ (2004). Image processing with Image. J Bio Int, 11, 36-43.
  4. Al-Fahoum AS, Jaber EB, Al-Jarrah MA (2014). Automated detection of lung cancer using statistical and morphological image processing techniques. J Biomedical Graphics Comput, 4, 33.
  5. Al-Kadi OS, Watson D (2008). Texture analysis of aggressive and nonaggressive lung tumor CE CT images. Biomedical Engineering, IEEE Transactions On, 55, 1822-30.
  6. Al-Tarawneh MS (2012). Lung cancer detection using image processing techniques. leonardo electronic. J Pract Technol, 11, 147-58.
  7. Alhadidi B, Zu'bi MH, Suleiman HN (2007). Mammogram breast cancer image detection using image processing functions. Informat Technol J, 6, 217-21. https://doi.org/10.3923/itj.2007.217.221
  8. Aoyama M, Li Q, Katsuragawa S, et al (2002). Automated computerized scheme for distinction between benign and malignant solitary pulmonary nodules on chest images. Med Phys, 29, 701-8. https://doi.org/10.1118/1.1469630
  9. Arzhaeva Y, Prokop M, Tax DM, et al (2007). Computer-aided detection of interstitial abnormalities in chest radiographs using a reference standard based on computed tomography. Med phy, 34, 4798-809. https://doi.org/10.1118/1.2795672
  10. Azadmanjir Z, Safdari R, Ghazisaeidi M (2015). From self-care for healthy people to self-management for cancer patients with cancer portals. Asian Pac J Cancer Prev, 16, 1321-5. https://doi.org/10.7314/APJCP.2015.16.4.1321
  11. Bae KT, Kim JS, Na YH, et al (2005). Pulmonary nodules: automated detection on ct images with morphologic matching algorithm-preliminary results 1. Radiol, 236, 286-93. https://doi.org/10.1148/radiol.2361041286
  12. Chen S, Zhao M, Wu G, et al (2012). Recent advances in morphological cell image analysis. Comput Math Methods Med, 2012, 101536
  13. Chen Y, Huang X, Shi H, et al (2011). A novel and cost-effective method for early lung cancer detection in immunized serum. Asian Pac J Cancer Prev, 12, 3009-12.
  14. Choudhari G, Swain D, Thakur D, et al (2012). Colorography: an adaptive approach to classify and detect the breast cancer using image processing. Int J Comp Appl, 45.
  15. Demir C, Yener B (2005). Automated cancer diagnosis based on histopathological images: a systematic survey. Rensselaer Polytechnic Institute Tech Rep [Epub ahead of print].
  16. Dobrescu R, Dobrescu M, Mocanu S, et al (2010). Medical images classification for skin cancer diagnosis based on combined texture and fractal analysis. WISEAS Trans on Biol Biomedicine, 7, 223-32.
  17. Fallahzadeh H, Momayyezi M, Akhundzardeini R, et al (2014). five year survival of women with breast cancer in Yazd. Asian Pac J Cancer Prev, 15, 6597-601. https://doi.org/10.7314/APJCP.2014.15.16.6597
  18. Freer TW, Ulissey MJ (2001). Screening mammography with computer-aided detection: prospective study of 12,860 patients in a community breast center 1. Radiol, 220, 781-6. https://doi.org/10.1148/radiol.2203001282
  19. G AK (2014). Analysis of medical image processing and its applications in healthcare industry Int. J Comput Technol Appl, 5, 851-60.
  20. Ganesan K, Acharya U, Chua CK, et al (2013). Computer-aided breast cancer detection using mammograms: A review. Biomedical Engineering, IEEE Rev In, 6, 77-98. https://doi.org/10.1109/RBME.2012.2232289
  21. Gao M, Bridgman P, Kumar S (2003). Computer-aided prostrate cancer diagnosis using image enhancement and jpeg2000. optical science and technology, SPIE's 48th Annual Meeting. Int Society Optics Photonic, 323-34.
  22. Giger ML (2004). Computerized analysis of images in the detection and diagnosis of breast cancer. Semin Ultrasound CT MR, 25, 411-8. https://doi.org/10.1053/j.sult.2004.07.003
  23. Globocan (2012a). All Cancers (excluding non-melanoma skin cancer) estimated incidence, mortality and prevalence Worldwide in 2012 [Online].
  24. Globocan (2012b). Globocan 2012. all cancers (excluding nonmelanoma skin cancer) estimated incidence, mortality and prevalence Worldwide in 2012 [Online].
  25. Gucuk S, Uyeturk U (2013). Effect of direct education on breast self examination awareness and practice among women in Bolu, Turkey. Asian Pac J Cancer Prev, 14, 7707-11. https://doi.org/10.7314/APJCP.2013.14.12.7707
  26. Guo Y (2010). Computer-aided detection of breast cancer using ultrasound images. All Graduate Theses Dissertations, 635.
  27. Guzman-Cabrera R, Guzman-Sepulveda J, Torres-Cisneros M, et al (2013). Digital image processing technique for breast cancer detection. Int J Thermophys, 34, 1519-31. https://doi.org/10.1007/s10765-012-1328-4
  28. Hasanabadi H, Zabihi M, Mirsharif Q (2014). Detection of pulmonary nodules in CT images using template matching and neural classifier. J Adv Comput Res, 5, 19-28. https://doi.org/10.1016/j.jare.2012.10.001
  29. He L, Long LR, Antani S, et al (2012). Histology image analysis for carcinoma detection and grading. Comput Method Programs Biom, 107, 538-56. https://doi.org/10.1016/j.cmpb.2011.12.007
  30. Jain VK, Vijay R (2013). Lungs cancer detection from mri image using image processing technique. Int J Comput Technol Appl, 14.
  31. Kannadhasan S, Ahamed NB, RajeshBaba M (2013). Cancer diagonsis with the help digital image processing using ZIGBEE Technology. Int J Emerging Trends Electrical Electronics, 1, 8-10.
  32. Kapoor P, Prasad S, Bhayana E (2010). Real time intelligent thermal analysis approach for early diagnosis of breast cancer. Int J Comput Appl, 1, 22-4.
  33. Karabatak M, Ince MC (2009). An expert system for detection of breast cancer based on association rules and neural network. Expert Systems Appl, 36, 3465-9. https://doi.org/10.1016/j.eswa.2008.02.064
  34. Kato N, Fukui M, Isozaki T (2009). Bag-of-features approach for improvement of lung tissue classification in diffuse lung disease. SPIE Medical Imaging. Int Society Optics Photon, [Epub ahead of print].
  35. Kaur J, Garg N, Kaur D (2014). A survey of lung cancer detection techniques on CT scan Images. Int J Scientific Engineering Res, 5, 377-80.
  36. Kazerouni IA, Zadeh HG, Haddadnia J (2014). A novel model for smart breast cancer detection in thermogram images. Asian Pac J Cancer Prev, 15, 10573.
  37. Kekre H, Sarode T, Raut K (2010). Detection of tumor in MRI using vector quantization segmentation. Int J Eng Sci Technol, 2, 3753-7.
  38. Kemal Y, Yucel I, Ekiz K, et al (2014). Elevated serum neutrophil to lymphocyte and platelet to lymphocyte ratios could be useful in lung cancer diagnosis. Asian Pac J Cancer Prev, 15, 2651-4. https://doi.org/10.7314/APJCP.2014.15.6.2651
  39. Kulakci H, Ayyildiz TK, Yildirim N, et al (2015). Effects of breast cancer fatalism on breast cancer awareness among nursing students in Turkey. Asian Pac J Cancer Prev, 16, 3565. https://doi.org/10.7314/APJCP.2015.16.8.3565
  40. Lee N, Laine AF, Marquez G, et al (2009). Potential of computeraided diagnosis to improve CT lung cancer screening. Biomedical Engineering, IEEE Rev Biomed Eng, 2, 136-46. https://doi.org/10.4236/jbise.2009.23024
  41. Leela G, Kumari HV (2014). Morphological approach for the detection of brain tumour and cancer Cells. J Electron Comput Eng Res, 2, 7-12.
  42. Lingayat NS, Tarambale MR (2013). A computer based feature extraction of lung nodule in chest x-ray image. Int J Bioscience Biochem Bioinformat, 3, 624-9.
  43. Loukas CG, Wilson GD, Vojnovic B, et al (2003). An image analysis-based approach for automated counting of cancer cell nuclei in tissue sections. Cytometry Part A, 55, 30-42.
  44. Mencattini A, Salmeri M, Lojacono R, et al (2008). Mammographic images enhancement and denoising for breast cancer detection using dyadic wavelet processing. IEEE Trans Med Imaging, 57, 1422-30.
  45. Mohaghegh P, Yavari P, Akbari ME, et al (2015). Associations of demographic and socioeconomic factors with stage at diagnosis of breast cancer. Asian Pac J Cancer Prev, 16, 1627-31. https://doi.org/10.7314/APJCP.2015.16.4.1627
  46. Mohammadzadeh N, Safdari R (2014). Robotic surgery in cancer care: opportunities and challenges. Asian Pac J Cancer Prev, 15, 1081-3. https://doi.org/10.7314/APJCP.2014.15.3.1081
  47. Mukti MZR, Ahmed F (2013). Early detection of lung cancer risk using data mining. Asian Pac J Cancer Prev, 14, 595-8. https://doi.org/10.7314/APJCP.2013.14.1.595
  48. Nagaraj H, Paga P, Lamichhane K (2014). Early breast cancer detection using statistical parameters. Int J Res Engineer Technolo, 2, 31-6.
  49. Najmabadi KM, Azarkish F, Latifnejadroudsari R, et al (2014). Self-disclosure of breast cancer diagnosis by Iranian women to friends and colleagues. Asian Pac J Cancer Prev, 15, 2879-82. https://doi.org/10.7314/APJCP.2014.15.6.2879
  50. Patil BG, Jain SN (2014). Cancer cells detection using digital image processing methods. Intl J Latest Trends Engineer Technolo, 3, 45-49.
  51. Patil SN, Kulhalli K, Patil SS (2014). Endometrial cancer detection using image processing techniques. Intl J Software Hardware Res Engineer, 2, 20-3
  52. Polakowski WE, Cournoyer DA, Rogers SK, et al (1997). Computer-aided breast cancer detection and diagnosis of masses using difference of Gaussians and derivative-based feature saliency. IEEE Trans Med Imaging, 16, 811-9. https://doi.org/10.1109/42.650877
  53. Rajyalakshmi U, Satya Prasad K, Koteswara Rao S (2014). Tissue processing, staining and image processing of pathological cancer images: A Review. Int J Eng Adv Technol, 3, 10-5.
  54. Ramteke NS, Jain SV (2013). Analysis of skin cancer using fuzzy and wavelet technique-review & proposed new algorithm. Int J Engineer Trend Technol, 4.
  55. Reddy L, Reddy R, Madhu C, et al (2010). A novel image segmentation technique for detection of breast cancer. Int J Inf Technol Knowl Manag, 2, 201-4.
  56. Remenyi A, Szenasi S, Bandi I, et al (2011). Parallel biomedical image processing with GPGPUs in cancer research. Logistics and Industrial Informatics (LINDI), 2011 3rd IEEE international symposium on, IEEE, 245-8.
  57. Sampat MP, Markey MK, Bovik AC (2005). Computer-aided detection and diagnosis in mammography. Handbook Image Video Proces, 2, 1195-217.
  58. Sankar K, Prabakaran M (2014). An efficient template matching algorithm for lung cancer detection using multi resolution histogram based image segmentation. Int J Enhanced Res in Sci Technolo Engineer, 3, 71-5.
  59. Santosh A, Sadashivappa G (2014). Skin cancer detection and diagnosis using image processing and Implementation using neural networks and ABCD parameters. Int J Elect, Communic Instrument Eng Res Develop, 4, 85-96.
  60. Shanthi S, Bhaskaran VM (2012). Computer aided system for detection and classification of breast cancer. Int J Inf Technol Control Autom, 2, 87-98.
  61. Shareef SR (2014). Breast cancer detection based on watershed transformation. Int J Computer Sci, 11, 237-45.
  62. Sharma D, Jindal G (2011). Identifying lung cancer using image processing techniques. Int Conf Comput Techniques Artificial Intelligence, 2011, 872-80.
  63. Shridhar K, Dey S, Bhan CM, et al (2015). Cancer detection rates in a population-based, opportunistic screening model, New Delhi, India. Asian Pac J Cancer Prev, 16.
  64. Shriwas RS, Dikondawar AD (2015). Lung cancer detection and prediction by using neural network. Int J Electronics Communicat, 3, 17-21.
  65. Smith AP, Hall PA, Marcello DM (2004). Emerging technologies in breast cancer detection. Radiol Manage, 26, 16-27.
  66. Strickland RN 2002. Image-processing techniques for tumor detection, CRC Press.
  67. Sundari KS, Vanaselvi P, Vishakai T (2014). Diagnosis of rectal cancer through images. Int J Advanced Networking and Applications, 6, 2329-33.
  68. Upadhyay Y, Wasson V (2014). Analysis of Liver MR Images for Cancer Detection using Genetic Algorithm. Int J Engineer Res General Sci, 2, 730-7.
  69. Usman M, Shoaib M, Rahal M (2013). Multi-resolution analysis technique for lung cancer detection in computed tomograpic images. Session 4AK, 1455.
  70. Vemuri RC, Jarecha R, Hwi KK, et al (2014). Importance of volumetric measurement processes in oncology imaging trials for screening and evaluation of tumors as per response evaluation criteria in solid tumors. Asian Pac J Cancer Prev, 15, 2375. https://doi.org/10.7314/APJCP.2014.15.5.2375
  71. Vithana PC, Ariyaratne M, Jayawardana P (2015). Educational intervention on breast cancer early detection: effectiveness among target group women in the district of gampaha, sri lanka. Asian Pac J Cancer Prev, 16, 2547-53. https://doi.org/10.7314/APJCP.2015.16.6.2547
  72. Zadeh HG, Haddadnia J, Hashemian M, et al (2012). Diagnosis of breast cancer using a combination of genetic algorithm and artificial neural network in medical infrared thermal imaging. Iranian J Med Phy, 9, 265-74.
  73. Zadeh HG, Janianpour S, Haddadnia J (2009). Recognition and classification of the cancer cells by using image processing and Lab VIEW. Int J Computer Theory Eng, [Epub ahead of print].
  74. Zahir ST, Mirtalebi M (2012). Survival of patients with lung cancer, Yazd, Iran. Asian Pac J Cancer Prev, 13, 4387-91. https://doi.org/10.7314/APJCP.2012.13.9.4387
  75. Zhang H, Fritts JE, Goldman SA (2008). Image segmentation evaluation: A survey of unsupervised methods. Computer Vision Image Understand, 110, 260-80. https://doi.org/10.1016/j.cviu.2007.08.003

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