PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Powiadomienia systemowe
  • Sesja wygasła!
  • Sesja wygasła!
Tytuł artykułu

An improved feature based image fusion technique for enhancement of liver lesions

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper describes two methods for enhancement of edge and texture of medical images. In the first method optimal kernel size of range filter suitable for enhancement of liver and lesions is deduced. The results have been compared with conventional edge detection algorithms. In the second method the feasibility of feature based pixel wise image fusion for enhancing abdominal images is investigated. Among the different algorithms developed in the medical image fusion pixel level fusion is capable of retaining the maximum relevant information with better implementation and computational efficiency. Conventional image fusion includes multi-modal fusion and multi-resolution fusion. The present work attempts to fuse together, texture enhanced and edge enhanced images of the input image in order to obtain significant enhancement in the output image. The algorithm is tested in low contrast medical images. The result shows an improvement in contrast and sharpness of output image which will provide a basis for a better visual interpretation leading to more accurate diagnosis. Qualitative and quantitative performance evaluation is done by calculating information entropy, MSE, PSNR, SSIM and Tenengrad values.
Twórcy
autor
  • Department of Electrical Engineering, College of Engineering, Trivandrum, India
autor
  • Department of Electrical Engineering, College of Engineering, Trivandrum, India
Bibliografia
  • [1] Beghdadi A, Negrate AL. Contrast enhancement technique based on local detection of edges. Comput Vis Graph Image Process 1989;46(2):162–74. http://dx.doi.org/10.1016/0734-189x(89)90166-7.
  • [2] Sonka M, Hlavac V, Boyle R. Image processing, analysis and machine vision. Springer US; 1993. http://dx.doi.org/10.1007/978-1-4899-3216-7.
  • [3] James AP, Dasarathy BV. Medical image fusion: a survey of the state of the art. Inf Fusion 2014;19:4–19. http://dx.doi.org/10.1016/j.inffus.2013.12.002.
  • [4] Clezardin P, Teti A. Bone metastasis: pathogenesis and therapeutic implications. Clin Exp Metastasis 2007;24(8):599–608. http://dx.doi.org/10.1007/s10585-007-9112-8.
  • [5] Ackerman NB, Hechmer PA. The blood supply of experimental liver metastases. Am J Surg 1980;140(5): 625–31. http://dx.doi.org/10.1016/0002-9610(80)90044-6.
  • [6] Gonzalez RC, Woods RE. Processing; 2002.
  • [7] Dhawan AP. Medical image analysis. John Wiley & Sons, Inc; 2011. http://dx.doi.org/10.1002/9780470918548.
  • [8] Li B, Xie W. Adaptive fractional differential approach and its application to medical image enhancement. Comput Electr Eng 2015;45:324–35. http://dx.doi.org/10.1016/j.compeleceng.2015.02.013.
  • [9] He N, Wang J-B, Zhang L-L, Lu K. An improved fractional-order differentiation model for image denoising. Signal Process 2015;112:180–8. http://dx.doi.org/10.1016/j.sigpro.2014.08.025.
  • [10] Isa IS, Sulaiman SN, Mustapha M, Karim NKA. Automatic contrast enhancement of brain MR images using average intensity replacement based on adaptive histogram equalization (AIR-AHE). Biocybern Biomed Eng 2017;37 (1):24–34. http://dx.doi.org/10.1016/j.bbe.2016.12.003.
  • [11] Li C, Yang Y, Xiao L, Li Y, Zhou Y, Zhao J. A novel image enhancement method using fuzzy sure entropy. Neurocomputing 2016;215:196–211. http://dx.doi.org/10.1016/j.neucom.2015.07.156.
  • [12] Chaira T. A rank ordered filter for medical image edge enhancement and detection using intuitionistic fuzzy set. Appl Soft Comput 2012;12(4):1259–66. http://dx.doi.org/10.1016/j.asoc.2011.12.011.
  • [13] Chaira T. An improved medical image enhancement scheme using type II fuzzy set. Appl Soft Comput 2014;25:293–308. http://dx.doi.org/10.1016/j.asoc.2014.09.004.
  • [14] Du J, Li W, Lu K, Xiao B. An overview of multi-modal medical image fusion. Neurocomputing 2016;215:3–20. http://dx.doi.org/10.1016/j.neucom.2015.07.160.
  • [15] Maurya L, Mahapatra PK, Kumar A. A social spider optimized image fusion approach for contrast enhancement and brightness preservation. Appl Soft Comput 2017;52:575–92. http://dx.doi.org/10.1016/j.asoc.2016.10.012.
  • [16] Mohammadi-Nejad A-R, Hossein-Zadeh G-A, Soltanian-Zadeh H. Multi-modal data fusion using group-structured sparse canonical correlation analysis: a simulation study. 2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA). IEEE; 2017. http://dx.doi.org/10.1109/pria.2017.7983031.
  • [17] Manchanda M, Sharma R. A novel method of multimodal medical image fusion using fuzzy transform. J Vis Commun Image Represent 2016;40:197–217. http://dx.doi.org/10.1016/j.jvcir.2016.06.021.
  • [18] Xu Z. Medical image fusion using multi-level local extrema. Inf Fusion 2014;19:38–48. http://dx.doi.org/10.1016/j.inffus.2013.01.001.
  • [19] Khan MF, Khan E, Abbasi Z. Image contrast enhancement using normalized histogram equalization. Optik 2015;126(24):4868–75. http://dx.doi.org/10.1016/j.ijleo.2015.09.161.
  • [20] Hassanpour H, Samadiani N, Salehi SM. Using morphological transforms to enhance the contrast of medical images. Egypt J Radiol Nucl Med 2015;46 (2):481–9. http://dx.doi.org/10.1016/j.ejrnm.2015.01.004.
  • [21] Papadopoulos A, Fotiadis D, Costaridou L. Improvement of microcalcification cluster detection in mammography utilizing image enhancement techniques. Comput Biol Med 2008;38(10):1045–55. http://dx.doi.org/10.1016/j.compbiomed.2008.07.006.
  • [22] Bai X, Zhou F, Xue B. Image enhancement using multi scale image features extracted by top-hat transform. Optics Laser Technol 2012;44(2):328–36. http://dx.doi.org/10.1016/j.optlastec.2011.07.009.
  • [23] Handbook of medical image processing and analysis. Elsevier; 2009. http://dx.doi.org/10.1016/b978-0-12-373904-9.x0001-4.
  • [24] Jähne B. Digital image processing. Berlin/Heidelberg: Springer; 2002. http://dx.doi.org/10.1007/978-3-662-04781-1.
  • [25] Bailey D, Hodgson R. Range filters: local-intensity subrange filters and their properties. Image Vis Comput 1985;3(3): 99–110. http://dx.doi.org/10.1016/0262-8856(85)90058-7.
  • [26] Sandulescu DL. Hybrid ultrasound imaging techniques (fusion imaging). World J Gastroenterol 2011;17(1):49. http://dx.doi.org/10.3748/wjg.v17.i1.49.
  • [27] Prada F, Vitale V, Bene MD, Boffano C, Sconfienza LM, Pinzi V, et al. Contrast-enhanced MR imaging versus contrastenhanced US: a comparison in glioblastoma surgery by using intraoperative fusion imaging. Radiology 2017;285(1):242–9. http://dx.doi.org/10.1148/radiol.2017161206.
  • [28] Mauri G. Expanding role of virtual navigation and fusion imaging in percutaneous biopsies and ablation. Abdom Imaging 2015;40(8):3238–9. http://dx.doi.org/10.1007/s00261-015-0495-8.
  • [29] Mauri G, Beni SD, Forzoni L, Kolev OSDV, Lagana MM, Solbiati L. Virtual navigator automatic registration technology in abdominal application. 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE; 2014. http://dx.doi.org/10.1109/embc.2014.6944889.
  • [30] Mauri G, Cova L, Beni SD, Ierace T, Tondolo T, Cerri A, et al. Real-time US-CT/MRI image fusion for guidance of thermal ablation of liver tumors undetectable with US: results in 295 cases. Cardiovasc Interv Radiol 2014;38(1):143–51. http://dx.doi.org/10.1007/s00270-014-0897-y.
  • [31] Erickson EA. Radiology data from the cancer genome atlas liver hepatocellular carcinoma [TCGA-LIHC] collection; 2016. http://dx.doi.org/10.7937/K9/TCIA.2016.IMMQW8UQ.
  • [32] Wang Z, Bovik A, Sheikh H, Simoncelli E. Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 2004;13(4):600–12. http://dx.doi.org/10.1109/tip.2003.819861.
  • [33] John J, Wilscy M. Enhancement of weather degraded color images and video sequences using wavelet fusion. Lecture Notes in Electrical Engineering. Netherlands: Springer; 2009. p. 99–109. http://dx.doi.org/10.1007/978-90-481-2311-7_9.
Uwagi
PL
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-4eef025c-1765-4f38-8520-0bb848f81abc
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.