Development of Mastoid Air Cell System Extraction Method on Temporal CT-scan Image

  • Syafri Arlis Universitas Putra Indonesia YPTK Padang
  • Sarjon Defit Universitas Putra Indonesia YPTK Padang
  • Sumijan Universitas Putra Indonesia YPTK Padang
Keywords: extraction, mastoid air cell system, morphology, regionprops

Abstract

Mastoiditis is disease that to infection of the mastoid bone cavity that affects the size of the air cell system of the temporal bone. Visually, the information temporal CT image mastoid bone has can assist medical experts in viewing the mastoid air cell system (MACS), but the fact that medical personnel are experiencing difficulties in determining the size MACS is due to the many different characteristics and objects overlap, so that in the measurement of the area, precise and accurate results have not been obtained. This study aims to separate the object of the MACS with the development of extraction. The proposed method uses Morphology and Regionprops operations. The dataset used in the testing process is 347 of 5 patients indicated for Mastoiditis. The results obtained can calculate the area of MACS for each test image. Based on image testing, the area of the smallest MACS in this study was 0.589 cm2 and the largest was 6.183 cm2. This, the smaller the size of the MACS indicates the severity of infection, so this study can help medical personnel make decisions and take appropriate treatment actions.

 

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References

Yuhandri, S. Madenda, E. P. Wibowo, and karmila@staff gunadarma ac id Karmilasari, “Object feature extraction of songket image using chain code algorithm,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 7, no. 1, pp. 235–241, 2017, doi: 10.18517/ijaseit.7.1.1479.

M. Yasir, M. S. Hossain, S. Nazir, S. Khan, and R. Thapa, “Object Identification Using Manipulated Edge Detection Techniques,” Science (80-. )., vol. 3, no. 1, pp. 1–6, 2022, doi: 10.11648/j.scidev.20220301.11.

H. Rodrigues et al., “Mastoid, middle ear and inner ear analysis in CT-scan – a possible contribution for the identification of remains,” Med. Sci. Law, vol. 60, no. 2, pp. 102–111, 2020, doi: 10.1177/0025802419893424.

L. Liu, J. Cheng, Q. Quan, F. X. Wu, Y. P. Wang, and J. Wang, “A survey on U-shaped networks in medical image segmentations,” Neurocomputing, vol. 409, pp. 244–258, 2020, doi: 10.1016/j.neucom.2020.05.070.

N. Bartov et al., “Management of Acute Mastoiditis with Immediate Needle Aspiration for Subperiosteal Abscess,” Otol. Neurotol., vol. 40, no. 6, pp. E612–E618, 2019, doi: 10.1097/MAO.0000000000002231.

M. W. Mather, P. D. Yates, J. Powell, and I. Zammit-Maempel, “Radiology of acute Mastoiditis and its complications: A pictorial review and interpretation checklist,” J. Laryngol. Otol., vol. 133, no. 10, pp. 856–861, 2019, doi: 10.1017/S0022215119001609.

D. H. Toneva, S. Y. Nikolova, D. K. Zlatareva, V. G. Hadjidekov, and N. E. Lazarov, “Sex estimation by Mastoid Triangle using 3D models,” Homo, vol. 70, no. 1, pp. 63–73, 2019, doi: 10.1127/homo/2019/1010.

M. Viscaino, J. C. Maass, P. H. Delano, M. Torrente, C. Stott, and F. Auat Cheein, “Computer-aided diagnosis of external and middle ear conditions: A machine learning approach,” PLoS One, vol. 15, no. 3, pp. 1–18, 2020, doi: 10.1371/journal.pone.0229226.

Q. Nie, Y. bing Zou, and J. C. W. Lin, “Feature Extraction for Medical CT Images of Sports Tear Injury,” Mob. Networks Appl., vol. 26, no. 1, pp. 404–414, 2021, doi: 10.1007/s11036-020-01675-4.

H. A. Balfas, No Title, Bedah Otol. Bedah Otologi Dan Bedah Neutrologi Dasar, 2017.

H. S. Gendeh, A. binti Abdullah, B. S. Goh, and N. D. Hashim, “Intracranial Complications of Chronic Otitis Media: Why Does It Still Occur?,” Ear, Nose Throat J., vol. 98, no. 7, pp. 416–419, 2019, doi: 10.1177/0145561319840166.

N. R. Sayal, S. Boyd, G. Zach White, and M. Farrugia, “Incidental Mastoid effusion diagnosed on imaging: Are we doing right by our patients?,” Laryngoscope, vol. 129, no. 4, pp. 852–857, 2019, doi: 10.1002/lary.27452.

S. W. Byun, S. S. Lee, J. Y. Park, and J. H. Yoo, “Normal Mastoid air cell system geometry: Has surface area been overestimated?,” Clin. Exp. Otorhinolaryngol., vol. 9, no. 1, pp. 27–32, 2016, doi: 10.21053/ceo.2016.9.1.27.

O. Cros, M. Gaihede, A. Eklund, and H. Knutsson, “Surface and curve skeleton from a structure tensor analysis applied on Mastoid air cells in human Temporal bones,” Proc. - Int. Symp. Biomed. Imaging, vol. i, pp. 270–274, 2017, doi: 10.1109/ISBI.2017.7950517.

S. Nikan et al., “Pwd-3dnet: A deep learning-based fully-automated segmentation of multiple structures on Temporal bone ct scans,” IEEE Trans. Image Process., vol. 30, pp. 739–753, 2021, doi: 10.1109/TIP.2020.3038363.

Z. Liu, C. Maere, and Y. Song, “Novel approach for automatic region of interest and seed point detection in CT images based on Temporal and spatial data,” Comput. Mater. Contin., vol. 59, no. 2, pp. 669–686, 2019, doi: 10.32604/cmc.2019.04590.

K. A. Powell, T. Kashikar, B. Hittle, D. Stredney, T. Kerwin, and G. J. Wiet, “Atlas-based segmentation of Temporal bone surface structures,” Int. J. Comput. Assist. Radiol. Surg., vol. 14, no. 8, pp. 1267–1273, 2019, doi: 10.1007/s11548-019-01978-2.

Y. Lv, J. Ke, Y. Xu, Y. Shen, J. Wang, and J. Wang, “Automatic segmentation of Temporal bone structures from clinical conventional CT using a CNN approach,” Int. J. Med. Robot. Comput. Assist. Surg., vol. 17, no. 2, pp. 1–9, 2021, doi: 10.1002/rcs.2229.

R. Hussain, A. Lalande, K. B. Girum, C. Guigou, and A. Bozorg Grayeli, “Automatic segmentation of inner ear on CT-scan using auto-context convolutional neural network,” Sci. Rep., vol. 11, no. 1, pp. 1–10, 2021, doi: 10.1038/s41598-021-83955-x.

K. U. Ahamed et al., “A deep learning approach using effective preprocessing techniques to detect COVID-19 from chest CT-scan and X-ray images,” Comput. Biol. Med., vol. 139, no. October, p. 105014, 2021, doi: 10.1016/j.compbiomed.2021.105014.

L. J. Jensen, D. Kim, T. Elgeti, I. G. Steffen, B. Hamm, and S. N. Nagel, “Stability of radiomic features across different region of interest sizes-A CT and MR phantom study,” Tomography, vol. 7, no. 2, pp. 238–252, 2021, doi: 10.3390/tomography7020022.

J. Na’am et al., “Detection of infiltrate on infant chest X-ray,” Telkomnika (Telecommunication Comput. Electron. Control., vol. 15, no. 4, pp. 1943–1951, 2017, doi: 10.12928/TELKOMNIKA.v15i4.3163.

Katherine, R. Rulaningtyas, and K. Ain, “CT-scan image segmentation based on hounsfield unit values using Otsu thresholding method,” J. Phys. Conf. Ser., vol. 1816, no. 1, 2021, doi: 10.1088/1742-6596/1816/1/012080.

A. Soni and A. Rai, “CT-scan Based Brain Tumor Recognition and Extraction using Prewitt and Morphological Dilation,” 2021 Int. Conf. Comput. Commun. Informatics, ICCCI 2021, vol. 1, no. 1, 2021, doi: 10.1109/ICCCI50826.2021.9402677.

R. I. Borman, Y. Fernando, Y. Egi, and P. Yudoutomo, “Identification of Vehicle Types Using Learning Vector Quantization,” vol. 5, no. 158, pp. 339–345, 2022, doi: 10.29207/resti.v6i2.3954

eman samir, E. S. Sabry, F. E. A. El-Samie, S. Elagooz, G. El Banby, and R. A. Ramadan, “Evaluating Image Extraction Methods Over Different Types of Images,” SSRN Electron. J., pp. 1–14, 2022, doi: 10.2139/ssrn.4024192.

A. Loddo, C. Di Ruberto, and M. Kocher, “Recent advances of malaria parasites detection systems based on mathematical morphology,” Sensors (Switzerland), vol. 18, no. 2, pp. 1–21, 2018, doi: 10.3390/s18020513.

Sumijan, S. Madenda, J. Harlan, and E. P. Wibowo, “Hybrids Otsu method, feature region and mathematical morphology for calculating volume hemorrhage brain on CT-scan image and 3D reconstruction,” Telkomnika (Telecommunication Comput. Electron. Control., vol. 15, no. 1, pp. 283–291, 2017, doi: 10.12928/TELKOMNIKA.v15i1.3146.

Na'am, J., Pranata, F. S., Hidayat, R., Adif, A. M., & Ellyzarti, E. (2021). Automated Identification Model of Ground-Glass Opacity in CT-Scan Image by COVID-19. International Journal on Advanced Science, Engineering and Information Technology, 11(2), 595-602, doi:10.18517/ijaseit.11.2.14143.

Published
2022-07-02
How to Cite
Arlis, S., Sarjon Defit, & Sumijan. (2022). Development of Mastoid Air Cell System Extraction Method on Temporal CT-scan Image . Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 6(3), 457 - 465. https://doi.org/10.29207/resti.v6i3.4090
Section
Artikel Teknologi Informasi