Development and modification Sobel edge detection in tuberculosis X-ray images

Retno Devita, Iskandar Fitri, Yuhandri Yuhandri, Finny Fitry Yani


Tuberculosis (TB), a major global health threat caused by mycobacterium tuberculosis, claims lives across all age groups, underscoring the urgent need for accurate diagnostic methods. Traditional TB diagnosis using X-ray images faces challenges in detection accuracy, highlighting a critical problem in medical imaging. Addressing this, our study investigates the use of image processing techniques-specifically, a dataset of 112 TB X-ray images-employing pre-processing, segmentation, edge detection, and feature extraction methods. Central to our method is the adoption of a modified Sobel edge detection technique, named modification and extended magnitude gradient (MEMG), designed to enhance TB identification from X-ray images. The effectiveness of MEMG is rigorously evaluated against the gray-level co-occurrence matrix (GLCM) parameters, contrast, and correlation, where it demonstrably surpasses the standard Sobel detection, amplifying the contrast value by over 50% and achieving a correlation value nearing 1. Consequently, the MEMG method significantly improves the clarity and detail of TB-related anomalies in X-ray images, facilitating more precise TB detection. This study concludes that leveraging the MEMG technique in TB diagnosis presents a substantial advancement over conventional methods, promising a more reliable tool for combating this global health menace.


GLCM; MEMG; Sobel detection; Tuberculosis; X-ray images

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The Indonesian Journal of Electrical Engineering and Computer Science (IJEECS)
p-ISSN: 2502-4752, e-ISSN: 2502-4760
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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