(School of Applied Science, Telkom University, Indonesia)(2) Indrarini Dyah Irawati
(School of Applied Science, Telkom University, Indonesia)(3) * Suci Aulia
(School of Applied Science, Telkom University, Indonesia)(4) Lestari Lestari
(School of Applied Science, Telkom University, Indonesia)*corresponding author
AbstractIn 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.
KeywordsTuberculosis; Autostitching ; SIFT ;SURF; Ziehl-Neelsen
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DOIhttps://doi.org/10.26555/ijain.v12i2.2325 |
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References
[1] J. Chakaya et al., “Global Tuberculosis Report 2020 – Reflections on the Global TB burden, treatment and prevention efforts,” Int. J. Infect. Dis., vol. 113, pp. S7–S12, Dec. 2021, doi: 10.1016/j.ijid.2021.02.107.
[2] N. Pamela Sari and A. S. Rachmawati, “Pendidikan Kesehatan Tuberkulosis ‘TOSS TB (Temukan Obati Sampai Sembuh),’” ABDIMAS J. Pengabdi. Masy., vol. 2, no. 1, pp. 103–107, Apr. 2019, doi: 10.35568/abdimas.v2i1.338.
[3] H.-T. Fu, H.-Z. Tu, H.-S. Lee, Y. E. Lin, and C.-W. Lin, “Evaluation of an AI-Based TB AFB Smear Screening System for Laboratory Diagnosis on Routine Practice,” Sensors, vol. 22, no. 21, p. 8497, Nov. 2022, doi: 10.3390/s22218497.
[4] S. Aulia, A. B. Suksmono, T. R. Mengko, and B. Alisjahbana, “A Novel Digitized Microscopic Images of ZN-Stained Sputum Smear and Its Classification Based on IUATLD Grades,” IEEE Access, vol. 12, pp. 51364–51380, 2024, doi: 10.1109/ACCESS.2024.3386208.
[5] M. R. Syahwana and R. M. Simanjorang, “Analisa Sistem Pakar Metode Bayes Dalam Mendiagnosa Penyakit Tubercolosis,” J. Sist. Informasi, Tek. Inform. dan Teknol. Pendidik., vol. 1, no. 2, pp. 57–66, Mar. 2022, doi: 10.55338/justikpen.v1i2.7.
[6] E. H. Tobin and D. Tristram, Tuberculosis Overview. StatPearls Publishing, 2024. Accessed: Jun. 20, 2026. [Online]. Available: https://www.ncbi.nlm.nih.gov/books/NBK441916/
[7] J. Long, H. E. Parker, K. Ehrlich, M. G. Tanner, K. Dhaliwal, and B. Mills, “Frugal filtering optical lenses for point-of-care diagnostics,” Biomed. Opt. Express, vol. 11, no. 4, p. 1864, Apr. 2020, doi: 10.1364/BOE.381014.
[8] A. Ojha, S. Banik, S. K. Melanthota, and N. Mazumder, “Light emitting diode (LED) based fluorescence microscopy for tuberculosis detection: a review,” Lasers Med. Sci., vol. 35, no. 6, pp. 1431–1437, Aug. 2020, doi: 10.1007/s10103-019-02947-6.
[9] T. N. Susilawati and R. Larasati, “A recent update of the diagnostic methods for tuberculosis and their applicability in Indonesia: a narrative review,” Med. J. Indones., vol. 28, no. 3, pp. 284–91, Oct. 2019, doi: 10.13181/mji.v28i3.2589.
[10] Y. Zaizen et al., “Deep-Learning-Aided Detection of Mycobacteria in Pathology Specimens Increases the Sensitivity in Early Diagnosis of Pulmonary Tuberculosis Compared with Bacteriology Tests,” Diagnostics, vol. 12, no. 3, p. 709, Mar. 2022, doi: 10.3390/diagnostics12030709.
[11] G. Davidson et al., “Overview of Tuberculosis: Causes, Symptoms and Risk Factors,” Asian J. Res. Infect. Dis., vol. 15, no. 9, pp. 8–19, Aug. 2024, doi: 10.9734/ajrid/2024/v15i9370.
[12] G. Bediang, B. Stoll, N. Elia, J.-L. Abena, and A. Geissbuhler, “SMS reminders to improve adherence and cure of tuberculosis patients in Cameroon (TB-SMS Cameroon): a randomised controlled trial,” BMC Public Health, vol. 18, no. 1, p. 583, Dec. 2018, doi: 10.1186/s12889-018-5502-x.
[13] M. Hatherill, R. E. Chaisson, and C. M. Denkinger, “Addressing critical needs in the fight to end tuberculosis with innovative tools and strategies,” PLOS Med., vol. 16, no. 4, p. e1002795, Apr. 2019, doi: 10.1371/journal.pmed.1002795.
[14] K. Nurzynska, D. Li, A. E. Walts, and A. Gertych, “Multilayer outperforms single-layer slide scanning in AI-based classification of whole slide images with low-burden acid-fast mycobacteria (AFB),” Comput. Methods Programs Biomed., vol. 234, no. June, p. 107518, Jun. 2023, doi: 10.1016/j.cmpb.2023.107518.
[15] H.-C. Huang, K.-L. Kuo, M.-H. Lo, H.-Y. Chou, and Y. E. Lin, “Novel TB smear microscopy automation system in detecting acid-fast bacilli for tuberculosis – A multi-center double blind study,” Tuberculosis, vol. 135, no. July, p. 102212, Jul. 2022, doi: 10.1016/j.tube.2022.102212.
[16] H. D. T. G. Samarasinghe, L. L. R. Sampath, H. R. M. M. V. B. Thilakarathna, R. A. R. C. Gopura, T. D. Lalitharatne, and Y. W. R. Amarasinghe, “Development of an automated microscopic imaging system for TB screening,” in 2016 Electrical Engineering Conference (EECon), IEEE, Dec. 2016, pp. 7–12. doi: 10.1109/EECon.2016.7830927.
[17] F. Yang, Z.-S. Deng, and Q.-H. Fan, “A method for fast automated microscope image stitching,” Micron, vol. 48, no. May, pp. 17–25, May 2013, doi: 10.1016/j.micron.2013.01.006.
[18] K. A. Johnson and G. M. Hagen, “Artifact-free whole-slide imaging with structured illumination microscopy and Bayesian image reconstruction,” Gigascience, vol. 9, no. 4, Apr. 2020, doi: 10.1093/gigascience/giaa035.
[19] G. Nikolaev, D. Korshunov, and A. Khvostikov, “Automatic stitching of panoramas for geological images of polished sections,” ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci., vol. X-2/W1-202, pp. 39–46, Dec. 2024, doi: 10.5194/isprs-annals-X-2-W1-2024-39-2024.
[20] N. K. EL Abbadi, S. A. Al Hassani, and A. H. Abdulkhaleq, “Panoramic Image Stitching Techniques Based on SURF and Singular Value Decomposition,” in Communications in Computer and Information Science, vol. 1511 CCIS, Springer Science and Business Media Deutschland GmbH, 2021, pp. 63–86. doi: 10.1007/978-3-030-93417-0_5.
[21] E. Karami, M. Shehata, and A. Smith, “Image Identification Using SIFT Algorithm: Performance Analysis against Different Image Deformations,” pp. 1–4, Mar. 2018, Accessed: Jun. 20, 2026. [Online]. Available: http://arxiv.org/abs/1710.02728.
[22] N. Kumar, R. Gupta, and S. Gupta, “Whole Slide Imaging (WSI) in Pathology: Current Perspectives and Future Directions,” J. Digit. Imaging, vol. 33, no. 4, pp. 1034–1040, Aug. 2020, doi: 10.1007/s10278-020-00351-z.
[23] R. Miggiano, M. Rizzi, and D. M. Ferraris, “Mycobacterium tuberculosis Pathogenesis, Infection Prevention and Treatment,” Pathogens, vol. 9, no. 5, p. 385, May 2020, doi: 10.3390/pathogens9050385.
[24] N. Chamidah, Y. S. Yonani, E. Ana, and B. Lestari, “Identification the number of Mycobacterium tuberculosis based on sputum image using local linear estimator,” Bull. Electr. Eng. Informatics, vol. 9, no. 5, pp. 2109–2116, Oct. 2020, doi: 10.11591/eei.v9i5.2021.
[25] N. G. Anggana, I. D. Irawati, S. Aulia, and Lestari, “Stitching Microscopic Images for Tuberculosis Sputum Samples,” J. Innov. Image Process., vol. 7, no. 4, pp. 1168–1185, Oct. 2025, doi: 10.36548/jiip.2025.4.005.
[26] J. Lockhart, S. Assefa, A. Alajdad, A. Alexander, T. Balch, and M. Veloso, “SURF: improving classifiers in production by learning from busy and noisy end users,” in Proceedings of the First ACM International Conference on AI in Finance, New York, NY, USA: ACM, Oct. 2020, pp. 1–8. doi: 10.1145/3383455.3422547.
[27] D. Mistry and A. Banerjee, “Comparison of Feature Detection and Matching Approaches: SIFT and SURF,” Glob. Res. Dev. Journals, vol. 2, no. 4, pp. 7–13, May 2025, doi: 10.70179/3qhg4p03.
[28] W. Y. Sulistyo, I. Riadi, and A. Yudhana, “Penerapan Teknik SURF pada Forensik Citra untuk Analisa Rekayasa Foto Digital,” JUITA J. Inform., vol. 8, no. 2, p. 179, Nov. 2020, doi: 10.30595/juita.v8i2.6602.
[29] P. Kaur, N. Kumar, and M. Singh, “SURFBCS: speeded up robust features based fuzzy vault scheme in biometric cryptosystem,” J. Supercomput., vol. 79, no. 11, pp. 12292–12316, Jul. 2023, doi: 10.1007/s11227-023-05142-1.
[30] N. K. EL Abbadi, S. A. Al Hassani, and A. H. Abdulkhaleq, “A Review Over Panoramic Image Stitching Techniques,” J. Phys. Conf. Ser., vol. 1999, no. 1, p. 012115, Sep. 2021, doi: 10.1088/1742-6596/1999/1/012115.
[31] X. Chen, J. Li, and Y. Sui, “A New Stitching Method for Dark-Field Surface Defects Inspection Based on Simplified Target-Tracking and Path Correction,” Sensors, vol. 20, no. 2, p. 448, Jan. 2020, doi: 10.3390/s20020448.
[32] Z. Hu, G. Xu, L. Chen, N. Fu, and S. Zhen, “Error matching elimination based on a local affine algorithm,” J. Phys. Conf. Ser., vol. 2216, no. 1, p. 012075, Mar. 2022, doi: 10.1088/1742-6596/2216/1/012075.
[33] Y. Tang, S. Tian, P. Shuai, and Y. Duan, “MGHE-Net: A Transformer-Based Multi-Grid Homography Estimation Network for Image Stitching,” IEEE Access, vol. 12, pp. 49216–49227, 2024, doi: 10.1109/ACCESS.2024.3384598.
[34] Y. Riwanto, M. T. Nuruzzaman, S. Uyun, and B. Sugiantoro, “Data Search Process Optimization using Brute Force and Genetic Algorithm Hybrid Method,” IJID (International J. Informatics Dev., vol. 11, no. 2, pp. 222–231, Jan. 2023, doi: 10.14421/ijid.2022.3743.
[35] I. Hadi Ali and S. Salman, “360-Degree Panoramic Image Stitching for Un-ordered Images Based on Harris Corner Detection,” Indian J. Sci. Technol., vol. 12, no. 4, pp. 1–9, Jan. 2019, doi: 10.17485/ijst/2019/v12i4/140988.
[36] H. Singh, Practical Machine Learning and Image Processing. Berkeley, CA: Apress, 2019. doi: 10.1007/978-1-4842-4149-3.
[37] M. Tuli, K. Kamali, and D. B. Lindell, “Generative Panoramic Image Stitching,” Jul. 2025, Accessed: Jun. 20, 2026. [Online]. Available: http://arxiv.org/abs/2507.07133.
[38] S. Rabby and S. M. Masudul Ahsan, “Copy-Move Forgery Detection Utilizing SIFT and FLANN with Optimized Keypoint Matching,” in 2024 27th International Conference on Computer and Information Technology (ICCIT), IEEE, Dec. 2024, pp. 2237–2242. doi: 10.1109/ICCIT64611.2024.11022130.
[39] S. Venezia, S. Coleman, D. Kerr, and J. Fegan, “Feature-based Approaches to Multi-View Image Stitching,” in 2025 IEEE Symposium on Computational Intelligence in Image, Signal Processing and Synthetic Media Companion (CISM Companion), IEEE, Mar. 2025, pp. 1–5. doi: 10.1109/CISMCompanion65074.2025.11033067.
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