Generic placeholder image

Current Medical Imaging


ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

General Research Article

Semi-dynamic Control of FCM Initialization for Automatic Extraction of Inflamed Appendix from Ultrasonography

Author(s): Kwang Baek Kim*, Hyun Jun Park and Doo Heon Song

Volume 15, Issue 8, 2019

Page: [810 - 816] Pages: 7

DOI: 10.2174/1573405614666180719142536

open access plus


Background: Current naked-eye examination of the ultrasound images for inflamed appendix has limitations due to its intrinsic operator subjectivity problem.

Objective: In this paper, we propose a fully automatic intelligent method for extracting inflamed appendix from ultrasound images. Accurate and automatic extraction of inflamed appendix from ultrasonography is a major decision making resource of the diagnosis and management of suspected appendicitis.

Methods: The proposed method uses Fuzzy C-means learning algorithm in pixel clustering with semi-dynamic control of initializing the number of clusters based on the intensity contrast dispersion of the input image. Thirty percent of the prepared ultrasonography samples are classified into four different groups based on their intensity contrast distribution and then different number of clusters are assigned to the images in accordance with such groups in Fuzzy C-means learning process.

Results: In the experiment, the proposed system successfully extracts the target without human intervention in 82 of 85 cases (96.47% accuracy). The proposed method also shows that it can cover the false negative cases occurred previously that used self-organizing map as the learning engine.

Conclusion: Such high level reliable correct extraction of inflamed appendix encourages to use the automatic extraction software in the diagnosis procedure of suspected acute appendicitis.

Keywords: Appendicitis, inflamed appendix, fuzzy C-means, ultrasonography, automatic extraction software, diagnosis.

« Previous
Graphical Abstract
Lamps LW. Infectious causes of appendicitis. Infect Dis Clin North Am 2010; 24(4): 995-1018.
[] [PMID: 20937462]
Symptoms and Causes of Appendicitis. In: National Institute of Diabetes and Digestive and Kidney Diseases. 2014.Available from:
Lee JH, Choi PC, Shim MS, Song KJ, Jeong YK. Comparison of computer tomography and sonography in patients suspected of having appendicitis. J Korean Soc Emerg Med 2001; 12(3): 290-7.
Stewart B, Khanduri P, McCord C, et al. Global disease burden of conditions requiring emergency surgery. Br J Surg 2014; 101(1): e9-e22.
[] [PMID: 24272924]
Kasper D, Fauci A, Hauser S, Longo D, Jameson J, Loscalzo J. Harrison's principles of internal medicine, . 19 ed. 2015.
Park SI, Park HJ, Kim KB. Appendix analysis from ultrasonography with cubic spline interpolation and K-means clustering. Int J Bio-Sci Bio-Technol 2015; 7(1): 1-10.
Doherty GM. Current diagnosis & treatment: surgery. Lange Medical Books/McGraw-Hill 2010.
Israel GM, Malguria N, McCarthy S, Copel J, Weinreb J. MRI vs. ultrasound for suspected appendicitis during pregnancy. J Magn Reson Imaging 2008; 28(2): 428-33.
[] [PMID: 18666160]
Bhangu A, Søreide K, Di Saverio S, Assarsson JH, Drake FT. Acute appendicitis: modern understanding of pathogenesis, diagnosis, and management. Lancet 2015; 386(10000): 1278-87.
[] [PMID: 26460662]
Park NH, Oh HE, Park HJ, Park JY. Ultrasonography of normal and abnormal appendix in children. World J Radiol 2011; 3(4): 85-91.
[] [PMID: 21532869]
Hussain S, Rahman A, Abbasi T, Aziz T. Diagnostic accuracy of ultrasonography in acute appendicitis. J Ayub Med Coll Abbottabad 2014; 26(1): 12-7.
[PMID: 25358207]
Kim KB, Park HJ, Song DH, Han SS. Developing an intelligent automatic appendix extraction method from ultrasonography based on fuzzy ART and image processing. Comput Math Methods Med 2015.2015389057
[] [PMID: 26089963]
Park J, Song DH, Han SS, Joo Lee S, Baek Kim K. Automatic extraction of soft tissue tumor from ultrasonography using ART2 based intelligent image analysis. Curr Med Imaging 2017; 13(4): 447-53.
Gupta R, Elamvazuthi I, Dass SC, et al. Curvelet based automatic segmentation of supraspinatus tendon from ultrasound image: a focused assistive diagnostic method. Biomed Eng Online 2014; 13: 157.
Suryadibrata A, Song DH, Kim KB. Automatic ganglion cyst detection from ultrasound images using fuzzy C-means clustering method. International information institute (Tokyo). Information 2017; 20(4A): 2543-8.
Lee HJ, Song DH, Kim KB. Effective computer-assisted automatic cervical vertebrae extraction with rehabilitative ultrasound imaging by using K-means clustering. Iran J Electr Comput Eng 2016; 6(6): 2810.
Kim KB, Park HJ, Song DH, Han SS. Extraction of sternocleidomastoid and longus capitis/colli muscle using cervical vertebrae ultrasound images. Curr Med Imaging 2014; 10(2): 95-104.
Kutbay U, Hardalaç F, Akbulut M, Akaslan Ü, Serhatlıoğlu S. A computer-aided diagnosis system for measuring carotid artery Intima-Media Thickness (IMT) using quaternion vectors. J Med Syst 2016; 40(6): 149.
[] [PMID: 27137786]
Xian M, Zhang Y, Cheng HD, Xu F, Zhang B, Ding J. Automatic breast ultrasound image segmentation: A Survey. Patt Recogn 2017; pp. 1-40.
Kessler N, Cyteval C, Gallix B, et al. Appendicitis: evaluation of sensitivity, specificity, and predictive values of US, Doppler US, and laboratory findings. Radiology 2004; 230(2): 472-8.
[] [PMID: 14688403]
Petroianu A. Diagnosis of acute appendicitis. Int J Surg 2012; 10(3): 115-9.
[] [PMID: 22349155]
Grover CA, Sternbach G. Charles McBurney: McBurney’s point. J Emerg Med 2012; 42(5): 578-81.
[] [PMID: 21982626]
Wider M, Myint Y, Supriyanto E. Comparison of histogram thresholding methods for ultrasound appendix image extraction. NAUN Int J Comput 2011; 5(11): 542-9.
Lam J, Pahl C, Abduljabbar HN, Supriyanto E. Measurement and analysis of the diameter of appendix based on ultrasound images. Int J Biosci Biochem Bioinform 2014; 4(2): 130-6.
Park SI, Kim KB. Extraction of appendix from ultrasonographic images with fuzzy binarization technique. Int J Biosci Biotechnol 2013; 5(4): 139-48.
Kim KB, Song DH, Park HJ. Automatic extraction of appendix from ultrasonography with self-organizing map and shape-brightness pattern learning. BioMed Res Int 2016; 2016: 1-10.
[] [PMID: 27190991]
Kim KB, Lee HJ, Song DH, Woo YW. Extracting fascia and analysis of muscles from ultrasound images with FCM-based quantization technology. Neural Netw World 2010; 20(3): 405-16.
Izakian H, Abraham A. Fuzzy C-means and fuzzy swarm for fuzzy clustering problem. Expert Syst Appl 2011; 38(3): 1835-8.
Kim YM, Kim YI. Estimation of optimal number of K-means algorithm using standard deviation. In: Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference.
Gonzalez RC, Woods RE. Digital image processing. 2nd ed. New Jersey, USA: Prentice Hall 2002.
Kim KB, Park HJ, Song DH, Choi BK. Automatic ultrasonographic measurement of abdominal muscle thickness with fuzzy binarization and image processing techniques. J Med Imaging Health Inform 2016; 6(6): 1363-9.

© 2022 Bentham Science Publishers | Privacy Policy