Analysis of Histogram and Grayscale on Chest X-Ray in Lung Cancer Using Image-J

Fani Susanto; Hernastiti Sedya Utami

doi:10.35882/ijeeemi.v5i3.280

Fani Susanto Universitas Muhammadiyah Purwokerto
Hernastiti Sedya Utami, hernastitisedyautami

DOI: https://doi.org/10.35882/ijeeemi.v5i3.280

Keywords: Lung cancer, chest x-ray, computed radiography, grayscale, histogram

Abstract

Posteranterior (PA) chest radiographic examination is a support in screening for the diagnosis of lung cancer. Computed radiography (CR) modality can produce chest images quickly, optimally and can be processed as needed. However, so far radiologists interpret images only by visual assessment, so the results are very subjective. Therefore digital medical image processing can be done by looking at the histogram and gray scale values to increase the accuracy of enforcing patient diagnoses. This study aims to analyze the comparison of histograms and gray degree values on CR chest images between normal patients and lung cancer patients. The study was conducted using 30 chest images consisting of normal and lung cancer patient groups with 15 images each. All images are calculated grayscale and display histogram graphics with the Image-J application and statistically analyzed using the Independent T-Test. The results show that there is a difference in grayscale values between normal chest images and lung cancer (p<0.001). The grayscale and histogram values on lung cancer chest images ( 103.2908 + 6.119 ) are higher and tend to the right compared to the grayscale and histogram values on normal chest images ( 64.5848 + 3.28) . Histogram and grayscale values add objective image interpretation in diagnosing lung cancer.

Downloads

Download data is not yet available.

References

[1] T. Rancati, GL Ceresoli, G. Gagliardi, S. Schipani, and GM Cattaneo, “Factors predicting radiation pneumonitis in lung cancer patients: A retrospective study,” Radiother. Oncol. , vol. 67, no. 3, pp. 275–283, 2003, doi: 10.1016/S0167-8140(03)00119-1.
[2] N. Aliyah, E. Pranggono, and B. Andriyoko, "Lung Cancer: A Brief Study," Indonesian. J. Chest Emerg. med. , vol. 4, no. 1, pp. 28–32, 2016.
[3] RPM Sundhari, “Enhanced histogram equalization based nodule enhancement and neural network based detection for chest x-ray radiographs,” J. Ambient Intell. Humaniz. Comput. , vol. 12, no. 3, pp. 3831–3839, 2021, doi: 10.1007/s12652-020-01701-z.
[4] JP Lampignano and Leslie E. Kendric, Radiographic Positioning and Related Anatomy Ninth Edition , vol. 9, no. 1. 2018.
[5] Endahyani, "Histograms and Gray Degree Values of Thoracic Computed Radiography (Cr) Images for Lung Tuberculosis (TB) Patients," Journal of Science and Mathematics , vol. 18, no. 4.pp. 118–122, 2010.
[6] K. Senthil Kumar, K. Venkatalakshmi, and K. Karthikeyan, “Lung Cancer Detection Using Image Segmentation by means of Various Evolutionary Algorithms,” Comput. Math. Methods Med. , vol. 2019, 2019, doi: 10.1155/2019/4909846.
[7] N. Ahmad and A. Hadinegoro, "The Histogram Equalization Method for Digital Image Improvement," Semantics , vol. 2, no. 1, pp. 23–2012, 2012, [Online]. Available: http://publikasi.dinus.ac.id/index.php/semantik/article/view/185
[8] L. Lança and A. Silva, “Digital imaging systems for plain radiography,” Digit. Imaging System. Plain Radiogr. , vol. 9781461450672, no. February 2015, pp. 1–161, 2013, doi: 10.1007/978-1-4614-5067-2.
[9] KY Astina, "Comparison of Grayscale and Histogram Thorax Computed Radiography (CR) Image in Tuberculosis (TBC) Patients with Normal Thorax Image," J. Med. sci. Clin. Res. , vol. 6, no. 12, 2018, doi: 10.18535/jmscr/v6i12.156.
[10] ZH Chen et al. , “Investigation on the incidence and risk factors of lung cancer among Chinese hospital employees,” Thorac. Cancer , vol. 13, no. 15, p. 2210–2222, 2022, doi: 10.1111/1759-7714.14549.
[11] A. Panunzio and P. Sartori, “Lung Cancer and Radiological Imaging,” Curr. Radiopharm. , vol. 13, no. 3, pp. 238–242, 2020, doi: 10.2174/1874471013666200523161849.
[12] BYM Kwan and HK Kwan, “Improved Lung Nodule Visualization on Chest Radiographs using Digital Filtering and Contrast Enhancement,” Int. J. Biomed. Bio. Eng. , vol. 5, no. 12, p. 700–703, 2011, [Online]. Available: http://www.who.int/en/
[13] SH Bradley et al. , “Estimating lung cancer risk from chest X-ray and symptoms: A prospective cohort study,” Br. J. Gen. Pract. , vol. 71, no. 705, pp. E280–E286, 2021, doi: 10.3399/BJGP20X713993.
[14] AG Nicholson et al. , “The 2021 WHO Classification of Lung Tumors: Impact of Advances Since 2015,” J. Thorac. Oncol. , vol. 17, no. 3, pp. 362–387, 2022, doi: 10.1016/j.jtho.2021.11.003.
[15] RW Foley et al. , “Chest X-ray in suspected lung cancer is harmful,” Eur. Radiol. , vol. 31, no. 8, pp. 6269–6274, 2021, doi: 10.1007/s00330-021-07708-0.
[16] T. Team, “Lung Cancer Incidence and Mortality with Extended Follow-up in the National Lung Screening Trial,” J. Thorac. Oncol. , vol. 1 4, no. 10, p. 1732–1742, 2019, doi: 10.1016/j.jtho.2019.05.044.
[17] C. Freitas et al. , “The Role of Liquid Biopsy in Early Diagnosis of Lung Cancer,” Front. Oncol. , vol. 11, no. April, 2021, doi: 10.3389/fonc.2021.634316.
[18] MA , SHS , Uray Ristian, "Comparison of the Histogram Equalization Combined Method with Contrast Stretching to Improve Radiological Image Quality," Coding J. Komput. and Apps. , vol. 8, no. 2, 2020, doi: 10.26418/coding.v8i2.41501.