Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal International Journal of Advances in Intelligent Informatics
Nadhya Gita Anggana
School of Applied Science, Telkom University
Computer Science & IT

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
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

Abstract

In conventional TB diagnosis, 100-300 field of view (FOV) microscopic fields are to be observed, which might lead to observer fatigue. For both of these tasks, an automatic stitching framework was studied, which extends to conventional feature-based transformations while incorporating feature matching using affine geometry and RANSAC-based homography refinement, which accounts 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 a higher quality of transparency as 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 rendered the optimized SURF-affine framework a preferred choice for automatic TB diagnosis systems.
Co-Authors Indrarini Dyah Irawati Lestari Lestari Suci Aulia