A Cross Sectional Study of Tumors Using Bio-Medical Imaging Modalities

Author(s): Mashal Tariq, Ayesha A. Siddiqi*, Ghous Baksh Narejo, Shehla Andleeb

Journal Name: Current Medical Imaging
Formerly: Current Medical Imaging Reviews

Volume 15 , Issue 1 , 2019

Become EABM
Become Reviewer
Call for Editor

Graphical Abstract:


Background: Digital Signal Processing (D.S.P) is an evolutionary field. It has a vast variety of applications in all fields. Bio medical engineering has various applications of digital signal processing. Digital Image Processing is one of the branches of signal processing. Medical image modalities proved to be helpful for disease diagnosis. Higher expertise is required in image analysis by medical professional, either doctors or radiologists.

Methods: Extensive research is being done and has produced remarkable results. The study is divided into three main parts. The first deals with introduction of mostly used imaging modalities such as, magnetic resonance imaging, x-rays, ultrasound, positron emission tomography and computed tomography. The next section includes explanation of the basic steps of digital image processing are also explained in the paper. Magnetic Resonance imaging modalities is selected for this research paper. Different methods are tested on MRI images.

Discussion: Brain images are selected with and without tumor. Solid cum Cystic tumor is opted for the r esearch. Results are discussed and shown. The software used for digital image processing is MATLAB. It has in built functions which are used throughout the study. The study represents the importance of DIP for tumor segmentation and detection.

Conclusion: This study provides an initial guideline for researchers from both fields, that is, medicine and engineering. The analyses are shown and discussed in detail through images. This paper shows the significance of image processing platform for tumor detection automation.

Keywords: Brain, Image processing, digital imaging techniques, MRI, cancer, tumor.

Gonzalez RC, Woods RE. Digital image processing Aavailable from: http://web.ipac.caltech.edu/staff/fmasci/home/astro_refs/Digital_Image_Processing_2ndEd.pdf
Russ JC, Neal FB. The image processing handbook. CRC press 2015.
Rafael Gonzalez C, Woods ER, Eddins LS. Digital Image processing using MATLAB. Gatesmark Publishing 2009.
Desurmont X, Bastide A, Chaudy C, Parisot C, Delaigle JF, Macq B. Image analysis architectures and techniques for intelligent surveillance systems. In: IEE proceedings-vision, image and signal processing 2005 IET. 224-31.
Du CJ, Sun DW. Recent developments in the applications of image processing techniques for food quality evaluation. Trends Food Science Technol 2004; 15(5): 230-49.
Walter T, Klein JC, Massin P, Erginay A. A contribution of image processing to the diagnosis of diabetic retinopathy-detection of exudates in color fundus images of the human retina. IEEE Trans Med Imaging 2002; 21(10): 1236-43.
Ahmad HA, Yu HJ, Miller CG. Medical imaging modalities. In: Medical imaging in clinical trials2014. 3-26.
Shehar B, et al. Awareness about cancer in Pakistan [Online] (2013). NAYS Survey. Available from: 2013.http://www.nays.com.pk/ nays-survey/
Graber M, Gordon R, Franklin N. Reducing diagnostic errors in medicine: what’s the goal? Acad Med 2002; 77(10): 981-92.
Hornberg JJ, Bruggeman FJ, Westerhoff HV, Lankelma J. Cancer: A systems biology disease. Biosystems 2006; 83(2): 81-90.
Carrillo A, Duerk JL, Lewin JS, Wilson DL. Semiautomatic 3-D image registration as applied to interventional MRI liver cancer treatment. IEEE Trans Med Imaging 2000; 19(3): 175-85.
Tariq M, Khawajah A, Hussain M. Image processing with the specific focus on early tumor detection. Int J Mach Learn Comput 2013; 3(5): 404.
Acharya R, Wasserman R, Stevens J, Hinojosa C. Biomedical imaging modalities: A tutorial. Comput Med Imaging Graph 1995; 19(1): 3-25.
Depeursinge A, Foncubierta-Rodriguez A, Van De Ville D, Müller H. Three-dimensional solid texture analysis in biomedical imaging: Review and opportunities. Medical Image Anal 2014; 18(1): 176-96.
Lizzy, The creative work of Lizzy Rainey, 2015. (Accessed 10 June.2015). Available from: http://auntlizzy.com/lizzy/asrt-cover-art/chest-ct
Geripal: A Geriatrics and Palliative Care Blog, 2013. (Accessed 10 June. 2015) Available from: http://www.geripal.org/2013/ 11/should-my- patient-get-amyloid-pet-scan.html
Accelerators for Society 2015 (Accessed 10 June. 2015). Available from:. http://www.accelerators-for-society.org/health/ index.php?id=7
Brahme A, Nyman P, Skatt B. 4D laser camera for accurate patient positioning, collision avoidance, image fusion and adaptive approaches during diagnostic and therapeutic procedures. Med Phys 2008; 35(5): 1670-81.
Remakelhealth’s: A Blog for Health Consumers, 2009. (Accessed 10June.2015). Available from: http://blog.remakehealth. com/blog_Healthcare_Consumers0/bid/10205/What-does-an-abdominal-liver-gallbladder-ultrasound-show
Prager RW, Ijaz UZ, Gee AH, Treece GM. Three-dimensional ultrasound imaging. Proc Inst Mech Eng H 2010; 224(2): 193-223.
Wilson K, Homan K, Emelianov S. Biomedical photoacoustics beyond thermal expansion using triggered nanodroplet vaporization for contrast-enhanced imagingNat Comm; 3: 618
Jack CR, Bernstein MA, Fox NC, et al. The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods. J Magn Reson Imaging 2008; 27(4): 685-91.
Meyer-Baese A, Schmid VJ. Pattern recognition and signal analysis in medical imaging. Elsevier 2014.
Fedorov A, Beichel R, Kalpathy-Cramer J, et al. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging 2012; 30(9): 1323-41.
Sim KS, Tso CP, Tan YY. Recursive sub-image histogram equalization applied to gray scale images. Patt Recog Lett 2007; 28(10): 1209-21.
Syed A. The Discrete Cosine Transform (DCT): Theory and application. [Tutorial] ECE 802 – 602: Information Theory and Coding Seminar 1. (Accessed 10 June. 2015). Available from: http://www.lokminglui.com/DCT_TR802.pdf
Wang Y, Zhou H. Total variation wavelet-based medical image denoising. Int J Biomed Imaging 2006; 2006: 89095.
Hou Z. A review on MR image intensity inhomogeneity correction. Int J Biomed Imaging 2006; 2006: 49515.
Ma Z, Tavares JM, Jorge RN, Mascarenhas T. A review of algorithms for medical image segmentation and their applications to the female pelvic cavity. Comput Methods Biomech Biomed Engin 2010; 13(2): 235-46.
Zhang DQ, Chen SC. A novel kernelized fuzzy c-means algorithm with application in medical image segmentation. Artif Intell Med 2004; 32(1): 37-50.
Otsu N. A threshold selection method from grey scale histogram. IEEE Trans Systems Man Cybernetics 1979; 9(1): 62-6.
Siddiqi AA, Narejo GB, Zardari S, Tariq M, Andleeb S. Application of image processing algorithms for brain tumor analysis in 2D and 3D leading to tumor’s positioning in skull: Overview. Mehran Univer Res J Engineer Technol 2017; 36(1): 201-8.

Rights & PermissionsPrintExport Cite as

Article Details

Year: 2019
Published on: 07 December, 2018
Page: [66 - 73]
Pages: 8
DOI: 10.2174/1573405613666170614081434
Price: $65

Article Metrics

PDF: 26