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All Journal International Journal of Advances in Intelligent Informatics
Lestari Lestari
School of Applied Science, Telkom University
Computer Science & IT

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Enhanced tuberculosis diagnosis: microscopic automatic stitching of sputum samples utilizing the SURF feature detector Nadhya Gita Anggana; Indrarini Dyah Irawati; Suci Aulia; Lestari Lestari
International Journal of Advances in Intelligent Informatics Vol 12, No 2 (2026): May 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v12i2.2325

Abstract

In conventional tuberculosis diagnosis, 100-300 fields of view (FOVs) must be observed, which can lead to observer fatigue. For both tasks, an automatic stitching framework was developed that extends conventional feature-based transformations by incorporating affine-geometry-based feature matching and RANSAC-based homography refinement, thereby accounting for the unique low-texture morphology and irregular patterns of Mycobacterium tuberculosis in ZN-stained sputum smears. The system was tested on a set of 10 overlapping image pairs with a fixed overlap of 30%. Among the evaluated image pairs, the proposed optimized method achieved a 100% success rate. Objective zero-pixel metric-based quantitative analysis also validated higher transparency quality compared to other methods. The proposed SURF implementation reached a minimum number of 345.263 zero-pixels, outperforming standard SURF (964.247) and SIFT (1.069.687). This improved robustness to rotation and illumination variations made the optimized SURF-affine framework a preferred choice for automatic TB diagnosis systems.
Co-Authors Indrarini Dyah Irawati Nadhya Gita Anggana Suci Aulia