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.