ABSTRACT :

The diagnosis of tuberculosis (TB) is done by detecting and counting the number of mycobacterium tuberculosis in a sputum examination done manually using a microscope. It is considered ineffective because it requires a long time and different diagnostic results. To overcome this problem, this paper implements digital image processing. There are 5 processes used on the system. Preprocessing with the RGB to HSV method is used to clarify the color of the image. Segmentation to separate objects from background images using thresholding. Feature extraction to get the value of area, perimeter, and level of roundness of the object. Classification uses fuzzy logic to classify mycobacterium tuberculosis based on features. The next is the process of counting mycobacterium tuberculosis. And the last is the process of classify into IUATLD scale based on the number of mycobacterium tuberculosis. From the results of tests conducted on 15 data, the system show that the level of accuracy, precision, sensitivity and specificity of system in calculate mycobacterium tuberculosis is 89%, 90%, 91.66% and 78.88% respectively. And also level of sensitivity, specificity and accuracy of system in classifying the level of infection is 100%, 80 % and 93% respectively. This system was tested on a microscopic sputum image database of RSUD Dr. Soetomo from a different patient.
Keywords: counting bacilli, fuzzy logic, mycobacterium tuberculosis, segmentation, sputum microscopic image

INTRODUCTION :

According to World Health Organization (WHO) 2018 data, TB is one of the ten deadliest diseases in the world. This infectious disease infection is caused by mycobacterium tuberculosis, an acid-resistant aerobic, which is transmitted through the air. It is estimated that one third of the world's population has been infected with mycobacterium tuberculosis. Mycobacterium tuberculosis is a type of stem bacteria with a length of 1-4 micro meter with thickness 0.3-0.6 micro meter that causes tuberculosis. Most of the components mycobacterium tuberculosis are fat / lipids. So that bacteria are able to be resistant to acids and very resistant to chemicals and physical factors.

There are various ways to diagnose TB, one of which is sputum examination. Sputum examination is an examination by detecting and counting the number of mycobacterium tuberculosis in sputum preparations. To identify bacteria requires staining using predetermined dyes. Ziehl Neelsen (ZN) is one of the coloring techniques to determinate the presence of Acid Resistant Basil (BTA). Microscopically, with ZN staining, smear will appear red in blue around it.
But the problem is, sputum examination is still done manually with the naked eye. It was considered less effective because it took a very high amount of time, energy and concentration. In addition, the results of different diagnoses depend on the expertise and experience of medical personnel. Therefore, we need a system that can detect and calculate the number of mycobacterium tuberculosis in sputum microscopic images.