Segmentation of Tuberculosis Bacilli Using Watershed Transformation and Fuzzy C-Means

Abstract

The easily transmitted Tuberculosis (TB) disease is attributed to the fact that Mycobacterium Tuberculosis (MTB) bacteria/viruses can be transmitted through the air. One of the methods to screen the TB disease is by reading sputum slides. Sputum slides are colored sputum samples of TB patients placed on microscopic slides. However, TB disease microscopic analysis has some limitations since it requires high accuracy reading and well-trained health personnel to avoid errors in the process of interpretation. Furthermore, the number of TB patients in the Primary Health Care (PHC) and the process of manual calculation of bacteria in a field of view often complicate the decision-making in the screening process conducted by the medical staffs. In this paper, the researchers propose the use of Watershed Transformation and Fuzzy C-Means combination to help solve the problem. The researchers collect the photo shooting of three PHC in Indonesia with 55 images of sputum from different TB patients. The assessed results of the proposed method are compared with the opinions of three Microbiology doctors. The comparison shows Cohen’s Kappa Coefficient value of 0.838. It suggests that the proposed method can detect Acid Resistant Bacteria (ARB) although it needs some improvement to achieve higher accuracy.

Integration of Microscopic Image Capturing System for Automatic Detection of Mycobacterium Tuberculosis Bacteria

“…Like Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol. proposing the use of Watershed Transformation and Fuzzy C-Mean methods to detect TB bacteria, this study shows the value of Cohen Kappa Coefficient 0.838 from the comparison of the results of bacteria detection by three microbiological doctors with the results of system detection [5].…”

“…Referring to previous research conducted by the Informatics Engineering team, [5] that there is an FGD (focus discussion group) process with analysts from Temanggung Regency Health Office who stated the detection of TB bacteria throughout the 100 fields of view manually is very tiring to be ab le to diagnose a TB sufferer. The process requires the help of digital tools to observe the number of TB bacteria found in the patient's phlegm along 100 viewing fields-data testing using primary data from sputum samples from patients with TB disease with each severity.…”

“…System compute time Differences with Other StudiesThis research is a development of the TB bacteria detection system previously carried out by the Informatics Study Program team of Universitas Islam Indonesia in detecting TB bacteria from sputum images[5]…”

TBC Bacteria Detection in Microscopic Image With Watershed Countur Method

“…Based on the table above, the success of the colour space method in detecting TB bacteria was able to identify two groups of High-Density Background and Low-Density Background images. While the watershed method can segment bacteria even though their positions are randomly distributed, this method has a sensitivity of 90.3% and a precision of 70% [4][5] [6][7] [8].So the watershed method will be tested with the colour space method in preprocessing and the watershed method in bacterial segmentation because the watershed method can be detection random object and identification object.…”