Open Access Open Access  Restricted Access Subscription or Fee Access

Volume Estimation of Ultrasound Thyroid Gland Image

D. Shraddha, Neetu Chikyal

Abstract


Thyroid volume is very much important in the follow-up procedure after treatment of enlarged thyroid glands. Among the various types of available imaging techniques, an ultrasound imaging is one of the commonly used techniques, as it is a non-invasive technique, which does not require any ionizing radiation. The present study proposes a technique useful to calculate thyroid volume. After separating the thyroid gland region by the segmentation process, a particle swarm optimization technique is used for estimation of the volume of thyroid gland.

Keywords


Thyroid segmentation, particle swarm optimization, a block difference of inverse probabilities thyroid volume

Full Text:

PDF

References


Pleśniak J, Urbański S. Comparative thyroid gland volume by two methods: Ultrasonography and planar scintigraphy. Polish J Radiol. 2012; 77(2): 19–21. doi: 10.12659/pjr.882966.

Mahmood NH, Rusli AH. Segmentation and Area Measurement for Thyroid Ultrasound Image. Int J Sci Eng Res. 2011; 2(12): 1–8.

Kollorz ENK, Hahn DA, Linke R, Goecke TW, Hornegger J, Kuwert T. Quantification of thyroid volume using 3-D ultrasound imaging. IEEE Trans Med Imaging. 2008; 27(4): 457–466. doi: 10.1109/TMI.2007.907328.

Wunderling T, Golla B, Poudel P, Arens C, Friebe M, Hansen C. Comparison of thyroid segmentation techniques for 3D ultrasound. Med Imaging 2017: Image Process. 2017; 10133: 1013317. doi: 10.1117/12.2254234.

Garg H, Jindal A. Segmentation of thyroid gland in ultrasound image using neural network. 2013 4th Int Conf Comput Commun Netw Technol (ICCCNT). 2013; 1–5. doi: 10.1109/ICCCNT.2013.6726797.

Chang CY, Lei YF, Tseng CH, Shih SR. Thyroid segmentation and volume estimation in ultrasound images. IEEE Trans Biomed Eng. 2010; 57(6): 1348–1357. doi: 10.1109/TBME.2010.2041003.

Patil S. Preprocessing to be Considered for MR and CT Images Containing Tumors. IOSR J Electr Electron Eng. 2012; 1(4): 54–57. doi: 10.9790/1676-0145457.

Thivakaran TK, Chandrasekaran R. Nonlinear Filter Based Image Denoising Using AMF Approach. 2010; 224–227. [Online]. Available: http://arxiv.org/abs/1003.1803.

Nguyen VD, Nguyen DT, Nguyen TD, Truong QD, Le MD. Combination of block difference inverse probability features and support vector machine to reduce false positives in computer-aided detection for massive lesions in mammographic images. Proc 2013 6th Int Conf Biomed Eng Informatics, BMEI 2013. 2013; 28–32. doi: 10.1109/BMEI.2013.6746901.

Koundal D. Computer-Aided Diagnosis of Thyroid Nodule: A Review. Int J Comput Sci Eng Surv. 2012; 3(4): 67–83. doi: 10.5121/ijcses.2012.3406.

I. Conference, O. Innovations, and A. I. Science. Thyroid Segmentation and Volume Estimation Using CT Images. IJIRSET. 2014; 3(Special Issue 5): 334–339.

Shabana W, Peeters E, De Maeseneer M. Measuring thyroid gland volume: Should we change the correction factor? Am J Roentgenol. 2006; 186(1): 234–236. doi: 10.2214/AJR.04.0816.

Chikyal N, Swamy KV. Performance assessment of various thyroid image segmentation techniques with consistency verification. J Adv Res Dyn Control Syst. 2019; 11(2) Special Issue: 1299–1309.




DOI: https://doi.org/10.37591/(rrjobi).v8i1.1131

Refbacks

  • There are currently no refbacks.


Copyright (c) 2020 Research & Reviews: A Journal of Bioinformatics