Call for Paper

CAE solicits original research papers for the July 2021 Edition. Last date of manuscript submission is June 30, 2021.

Read More

Measuring Non-geometric Soft Organ in Human Body, Liver Volume using Algorithms Bank Template

Amir Mohamed Elamir, Riza Sulaiman, Khalda F. Ali. Published in Algorithms.

Communications on Applied Electronics
Year of Publication: 2016
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Amir Mohamed Elamir, Riza Sulaiman, Khalda F. Ali

Amir Mohamed Elamir, Riza Sulaiman and Khalda F Ali. Measuring Non-geometric Soft Organ in Human Body, Liver Volume using Algorithms Bank Template. Communications on Applied Electronics 6(2):22-25, November 2016. BibTeX

	author = {Amir Mohamed Elamir and Riza Sulaiman and Khalda F. Ali},
	title = {Measuring Non-geometric Soft Organ in Human Body, Liver Volume using Algorithms Bank Template},
	journal = {Communications on Applied Electronics},
	issue_date = {November 2016},
	volume = {6},
	number = {2},
	month = {Nov},
	year = {2016},
	issn = {2394-4714},
	pages = {22-25},
	numpages = {4},
	url = {},
	doi = {10.5120/cae2016652433},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Many of the patients in liver functions need to donors that organ. Measurement of the liver needs optimal cute, accurate and faster. The measuring non-geometric soft organs in the human body using algorithms bank method to obtain the measuring liver volume with high accuracy, reducing the error rate and more fasting. In the realization of the objectives of this paper, developing a platform for measuring the liver volume with high accuracy and reduce the error rate, designing and develop the algorithm or more to calculate the liver volume with high accuracy and reduce the error rate, designing and developing a computer-aided measurement of liver volume in hepatic CT and MRI using computer graphics and visualization. The research method of this paper divided into four phases, phase one is image source, which is used to convert image source from CT and MRI to DICOM file. Phase two software application and development, which is used to remove image noise, enhance the liver boundary and liver image segmentation. Phase three template prototype application and measurement, which is used to create a template, compare the source image DICOM file with the template and restore an image as a template. Phase four compare the liver volume measurement with Gold standard reading, which is used to determine accuracy and error rate. The framework represents in three layers, technique layer, image layer and application layer. The research focusing on the application layer, which is divided into four stages represent in removing the image noise (internal and external) by Anisotropic diffusion filter and Median filter. Enhancing the liver boundary by A scale-specific gradient magnitude filter and Gaussian filter. Segmentation by hybrid based framework method using three algorithms, fast marching, Geodesic active contour, and level set. To make sure the fading of mistake and access to the value is zero, use one of the Artificial Intelligence techniques, which is the Artificial Neural Network. The template is a method to measure liver volume, which uses the template to measure the volume of the liver automatically instead of using the manual since the manual used the Gold standard method.


  1. Am Suk Oh et al. 2014. “Design and Implementation of Standard DICOM Interface Module International Journal of Bio-Science and Bio-Technology” Vol.6, No.2 (2014), pp.141-146.
  2. Kenji Suzuki, 2014, “Computerized Liver Volumetry on MRI Using 3DGeodesicActiveContourSegmentation” 2015.
  3. Henderson JM, Heymsfield SB, Horowitz J, Kutner MH.1981 “Measurement of liver and spleen volume by computed tomography. Assessment of reproducibility and changes found following a selective distal splenorenal shunt. Radiology 1981; 141: 525-527 [PMID: 6974875]
  4. V.P.Ananthi, P.Balasubramaniam, C.P.Lim. 2014.“Segmentation of gray scale image based on intuitionistic fuzzy sets constructed from several membership functions”. PatternRecognition47(2014)3870–3880.
  5. Etehad Tavakol M, Ng EYK. 2013. “Breast thermography as a potential non-contact method in the early detection of cancer: a review”. J Mech Med Biol 13: 1330001; doi:10.1142/S0219519413300019.
  6. 2015.
  7. Gurpreet kaur , Rajdavinder Singh. 2014 Image Enhancement and It's Techniques- A Review, 2014. International Journal of Computer Trends and Technology (IJCTT) .
  8. Mussarat Yasmin, Muhammad Sharif, Saleha Masood, Mudassar Raza and Sajjad Mohsin. 2012. Brain Image Enhancement - A Survey. World Applied Sciences Journal 17 (9): 1192-1204, 2012 ISSN 1818-4952 © IDOSI Publications, 2012.
  9. R. Sharmila and R. Uma.2011. “A New Approach to Image Contrast Enhancement using Weighted Threshold Histogram Equalization with Improved Switching Median Filter”, International Journal of Advanced Engineering Sciences and Technologies (IJAEST), 7(2), pp. 38-43, 2011.
  10. Rafael C. Gonzalez and Richard E. Woods, “Digital Image Processing”, 3rd edition, Prentice Hall, 2008.
  11. Gurpreet kaur , Rajdavinder Singh. 2014. Image Enhancement and Its Techniques. International Journal of Computer Trends and Technology (IJCTT) – volume 12 number 3 – Jun 2014.
  12. Book. Encyclopedia of Diagnostic imaging. Albert L.Baet. Springer reference. 2015.


Computer graphics, remove noise, enhancement boundary, Artificial Neural Network, the gold standard, segmentation.