Studies on abnormality analysis in typical medical modalities based on biomedical simulation tools

Abstract

Abnormality in medical images is an ill healthy or unnecessary growth or deterioration of a minute part in any organ. The most common form of defect is Cancer, Bleed, Edema, Infarct, Tumor, and Aneurysm in any part of the body. Among these abnormalities of High-risk factor Cancer (Malignant Tumor), Large Tumor (Benign) and Aneurysm are treated as exceptional cases. Region Of Interest (ROI) confirms Abnormality Of Interest (AOI) in various locations of cancer, tumor, and aneurysm images. Cancer and tumor have interdependence and imperial relationship. Using Segmentation ROI of any AOI is identified by subtracting the background from the foreground The case studies of cancer or tumor interest are known as Oncological Studies. The associated research related to Aneurysm is Homodynamic or Haemodynamic analysis. This paper provides qualitative information of all the three abnormalities. It first describes different kinds of abnormalities in brief and later, a particular focus on three abnormalities (Cancer, Tumor, and Aneurysm) and their Symptoms, Types, Treatment, Associated medical image modalities. Later, it concentrates on the Opportunities and challenges in these studies via different databases/data sets for diagnosis, tools for their modeling and analysis which provides professional growth in Medical academia, Research and Development (R&D) and Medical-surgical interventional community.

Keywords

• Abnormality
• Aneurysm
• Biomedical Simulation tools
• Cancer
• Modality
• Segmentation
• Treatment
• Tumor
• Virtual Models

References

1. REFERENCES
2. [1] Pham DL, Xu C, Prince JL. Current methods in medical image segmentation. Annu Rev Biomed Eng 2000;2:315–337. [CrossRef]
3. [2] Ramya G, Shanthi AS. Enhancement of image segmentation based on modularity optimization. Int J Eng Trends Technol 2016;35:564–569. [CrossRef]
4. [3] Dougherty G. Digital image processing for medical applications. New York: Cambridge University Press; 2009. [CrossRef]
5. [4] Fass L. Imaging and cancer: a review. Mol Oncol. 2008;2:115–152. [CrossRef]
6. [5] Oppelt A, Grandke T. Magnetic resonance imaging. Supercond Sci Technol 1993;6:381. [CrossRef]
7. [6] Gibbs SJ, Hall LD. What roles are there for magnetic resonance imaging in process tomography? Meas Sci Technol 1996;7:827. [CrossRef]
8. [7] McDonald PJ, Newling B. Stray field magnetic resonance imaging. Rep Prog Phys 2004;61:1493–1498. [CrossRef]

9. [8] Kunz WG, Eschbach RS, Stahl R, Kazmierczak PM, Bartenstein P, Rominger A, et al. Identification and characterization of myocardial metastases in neuroendocrine tumor patients using 68Ga-DOTATATE PET-CT. Cancer Imaging 2008;18:34. [CrossRef]
10. [9] Gui M, Feng Y, Yi B, Dhople AA, Yu C. Fourdimensional intensity-modulated radiation therapy planning for dynamic tracking using a direct aperture deformation (DAD) method. Med Phys 2010;37:1966–1975. [CrossRef]
11. [10] Suh Y, Murray W, Keall PJ. IMRT treatment planning on 4D geometries for the era of dynamic MLC tracking. Technol Cancer Res Treat 2014;13:505–515. [CrossRef]
12. [11] Shukla AK, Kumar U. Positron emission tomography: an overview. J Med Phys 2006;31:13–21. [CrossRef]
13. [12] Pace L, Nicolai E, Aiello M, Catalano OA, Salvatore M. Whole-body PET/MRI in oncology: current status and clinical applications. Clin Transl Imaging 2013;1:31–44. [CrossRef]
14. [13] Currie GM, Iqbal B, Wheat JM, Davidson R, Kiat H. Single photon emission computed tomography (SPECT)/computed tomography (CT): an introduction. Radiographer 2011;58:60–66. [CrossRef]
15. [14] Madsen MT. Recent advances in SPECT imaging. J Nucl Med 2007;48:661–673. [CrossRef]
16. [15] Townsend DW. Combined PET/CT: the historical perspective. Semin Ultrasound CT MRI 2008;29:232–235. [CrossRef]
17. [16] Chen YY, Chen WS, Ni HS. Image segmentation in thermal images. In: Proceedings of the IEEE International Conference on Industrial Technology (ICIT); 2016 Mar 14-17; Taipei, Taiwan. p. 1507-1512. [CrossRef]
18. [17] Duarte A, Carrão L, Espanha M, Viana T, Freitas D, Bártolo P, et al. Segmentation algorithms for thermal images. Procedia Technol. 2014;16:1560–1569. [CrossRef]
19. [18] Ansari S, Salankar S. An overview on thermal image processing. In: Proceedings of the Second International Conference on Research in Intelligent and Computing in Engineering; 2017 Mar 24-26; Gopeshwar, Uttarakhand, India. p. 117–120. [CrossRef]
20. [19] Moghbel M, Mashohor S, Mahmud R, Bin Saripan MI, Hamid SA, Sani Mohamad S, et al. Breast boundary segmentation in thermography images based on random walkers. Turk J Electr Eng Comput Sci 2017;25:1733–1750. [CrossRef]
21. [20] Hartung MP, Grist TM, François CJ. Magnetic resonance angiography: current status and future directions. J Cardiovasc Magn Reson 2011;13:19. [CrossRef]
22. [21] Nishimura DG, Macovski A, Pauly JM. Magnetic resonance angiography. IEEE Trans Med Imaging 1986;5:140–151. [CrossRef]
23. [22] Buhk JH, Kallenberg K, Mohr A, Dechent P, Knauth M. Evaluation of angiographic computed tomography in the follow-up after endovascular treatment cerebral aneurysms: a comparative study with DSA and TOF-MRA. Eur Radiol 2001;19:430–436. [CrossRef]
24. [23] Smith JJ, Sorensen AG, Thrall JH. Biomarkers in imaging: realizing radiology’s future. Radiology 2003;227:633–638. [CrossRef]
25. [24] Bates AJ, Couillard SA, Arons DF, Yung WKA, Vogelbaum M, Wen PY, et al. Brain tumor patient and caregiver survey on clinical trials: identifying attitudes and barriers to patient participation. Neuro Oncol 2017;19(Suppl 6):vi109. [CrossRef]
26. [25] World Health Organization. Fact sheet: cancer. Available at: https://www.who.int/news-room/factsheets/detail/cancer Accessed Feb 21, 2025.
27. [26] National Cancer Institute. A to Z list. Available at: https://www.cancer.gov/types Accessed Feb 21, 2025.
28. [27] Centers for Disease Control and Prevention. Available at: http://wonder.cdc.gov/ucd-icd10.html Accessed Feb 21, 2025.
29. [28] Menard MT, Belkin M. Peripheral aneurysms. In: Hallett JW Jr, Mills JL Sr, editors. Comprehensive vascular and endovascular surgery. 2nd ed. Philadelphia: Mosby Elsevier; 2009. p. 579–591 [CrossRef]
30. [29] Merck Manual. Peripheral arterial aneurysms. Available at: http://www.merckmanuals.com/professional/cardiovascular-disorders/peripheral-arterial-disorders/peripheral-arterial-aneurysms Accessed Feb 21, 2025.
31. [30] Greenberg MS. SAH and aneurysms. In: Handbook of neurosurgery. 8th ed. New York: Thieme; 2016. p. 1156–1176.
32. [31] Loscalzo J, editor. Harrison’s principles of internal medicine. 19th ed. New York: Tata McGraw-Hill Education Medical; 2017.
33. [32] Rutherford RB. Vascular surgery. 6th ed. Philadelphia: WB Saunders Elsevier; 2005.
34. [33] Brito CJ, Duque A, Merlo I. Cirurgia vascular: cirurgia endovascular - angiologia. 2 vols. Rio de Janeiro: Revinter; 2008.
35. [34] Maffei FU, Lastória S, Yoshida WB, Rollo HA. Doenças vasculares periféricas. Vol. 1. Rio de Janeiro: Guanabara Koogan; 2008.
36. [35] Lobato AC, Araújo AP, Pereira AH. Cirurgia endovascular. São Paulo: Instituto de Cirurgia Vascular e Endovascular de São Paulo; Rio de Janeiro: Revinter;2006.
37. [36] Cronenwett JL, Rutherford RB. Decision making in vascular surgery. Philadelphia: WB Saunders Elsevier; 2001.
38. [37] Haimovici H, Ascher E, Hollier LH. Cirurgia vascular. 5th ed. Rio de Janeiro: Revinter; 2006.
39. [38] Zelenock GB, Huber TS, Messina LM. Mastery of vascular and endovascular surgery. Philadelphia: Lippincott Williams & Wilkins; 2005.
40. [39] Celi S, Berti S. Aneurysm. In: Murai Y, editor. Rijeka: InTech; 2012.
41. [40] Ahuja AT, Antonio GE. editors. Case studies in medical imaging: radiology for students and trainees. Cambridge: Cambridge University Press; 2006. [CrossRef]
42. [41] Jamali AA, Deuel C, Perreira A, Salgado CJ, Hunter JC, Strong EB. Linear and angular measurements of computer-generated models: are they accurate, valid, and reliable? Comput Aided Surg 2007;12:278–285. [CrossRef]
43. [42] Anvil site. Available at: http://www.anvilmcs.com/pages/sm.htm Accessed Feb 21, 2025.
44. [43] OpenCV. Available at: https://opencv.org/ Accessed Feb 21, 2025.
45. [44] OpenCV tutorial site 1. Available at: https://opencvpython-tutroals.readthedocs.io/en/latest/py_tutorials/py_setup/py_intro/py_intro.html Accessed Feb 21, 2025.
46. [45] OpenCV tutorial site 2. Available at: https://python.swaroopch.com/ Accessed Feb 21, 2025. [ 46] ITK tutorial site. Available at: www.itk.org/ITK/help/tutorials.html Accessed Feb 21, 2025.
47. [47] ITK ImageJ updates site. Available at: https://imagej.net/List_of_update_sites Accessed Feb 21, 2025.
48. [48] Radiology. Available at: www.radiologyinfo.org Accessed Feb 21, 2025.
49. [49] Emmet B, Claire C, Campbell AG. Augmented reality EVAR training in mixed reality educational space. In: Proceedings of the IEEE Global Engineering Education Conference (EDUCON); 2017 Apr 25-28; Athens, Greece. p. 1571–1579. [CrossRef]
50. [50] Desender L, Rancic Z, Aggarwal R, Duchateau J, Glenck M, Lachat M, et al; EVEREST (European Virtual Reality Endovascular Research Team). Patient-specific rehearsal prior to EVAR: a pilot study. Eur J Vasc Endovasc Surg 2013;45:639–647. [CrossRef]
51. [51] Srinivas T, Srinivasa Rao Ch. Fast aneurysm and blood vessel delineation method. In: Proceedings of the International Conference on Recent Trends in Engineering, Science & Technology (ICRTEST 2016); 2016 Oct 25-27; Hyderabad, India. p. 115–120.
52. [52] Thirumala S, Chanamallu SR. A fast and efficient region-based aneurysm segmentation model for medical image segmentation. Journal of Biology and Today’s World 2017;6:174–185. [CrossRef]
53. [53] Thirumala S, Chanamallu SR. Aortic aneurysm virtual modeling and predictive cum precautionary analysis using ANSYS. International Journal of Advanced Science and Technology 2020;29:6661–6675.
54. [54] Thirumala S, Chanamallu SR. A novel framework on abdominal aortic aneurysm analysis based on biomedical simulation tools. Int J Recent Technol Eng 2019;8:2300–2309. [CrossRef]
55. [55] World Health Organization. Fact sheet: colorectal cancer. Available at: https://www.who.int/newsroom/fact-sheets/detail/colorectal-cancer Accessed Feb 21, 2025.
56. [56] Zalis ME, Barish MA, Choi JR, Dachman AH, Fenlon HM, Ferrucci JT, et al. CT colonography reporting and data system: a consensus proposal. Radiology 2005;236:3–9. [CrossRef]