Araştırma Makalesi
BibTex RIS Kaynak Göster

KAN HÜCRELERİNİN OPTİMUM ODAKLI GÖRÜNTÜLENMESİ İÇİN DERİN ÖĞRENME TABANLI YAKLAŞIMIN GELİŞTİRİLMESİ

Yıl 2024, Cilt: 27 Sayı: 4, 1465 - 1476, 03.12.2024

Fatma Tuana Doğu , Zeinab Danaei , Hülya Doğan , Ramazan Özgür Doğan , Feride Sena Sezen

https://doi.org/10.17780/ksujes.1506248

Öz

Mikroskobik sistemlerde var olan odaklama derinliği sebebiyle kan hücreleri bulunan numuneler tamamıyla odaklı görüntülenememektedir. Bu durum yapay zeka ve görüntü işleme algoritmalarının performans kaybına sebep olabilmektedir. Bunu çözmek için odaklama derinliğinin artırılması yaklaşımları kullanılmakta ve numunenin optimum odaklı görüntüsü elde edilmektedir. Literatürde birçok odaklama derinliğinin artırılması yaklaşımı bulunmasına rağmen bu alanda hala yüksek çalışma süresi, kullanılan numuneye ve mikroskop çeşidine göre farklı performans gösterme gibi çeşitli eksiklikler mevcuttur. Bu çalışmada, literatürdeki bu eksiklikleri gidermek amacıyla mikroskobik sistemlerde kan hücrelerinin optimum odaklı görüntülenmesi için hem yeni veri seti oluşturulmakta hem de derin öğrenme tabanlı yeni bir odaklama derinliği artırılması yaklaşımı önerilmektedir. Çalışmanın performansını değerlendirmek için Algı Tabanlı Görüntü Kalitesi, Referanssız Görüntü Uzamsal Kalite, Bulanıklık ve Doğallık Görüntü Kalitesi olmak üzere dört farklı kriter kullanılmaktadır. Geliştirilen çalışmada 13 farklı odaklama derinliğinin artırılması yaklaşımı test edilmektedir. Bu çalışmada performans değerlendirme kriterleri sonuçları ile kan hücrelerinin optimum odaklı görüntülenmesi için önerilen derin öğrenme tabanlı odaklama derinliğinin artırılması yaklaşımının diğer yaklaşımlara göre daha performanslı olduğu ispatlanmaktadır.

Anahtar Kelimeler

Derin öğrenme odaklama derinliğinin artırılması ışıklı mikroskop odaklama ölçüm operatörleri

Kaynakça

  • Ahmad, M. B., & Choi, T. S. (2007). Application of three dimensional shape from image focus in LCD/TFT displays manufacturing. IEEE Transactions on Consumer Electronics, 53(1), 1-4. https://doi.org/10.1109/TCE.2007.339492
  • Akpinar, U., Sahin, E., Meem, M., Menon, R., & Gotchev, A. (2021). Learning wavefront coding for extended depth of field imaging. IEEE Transactions on Image Processing, 30, 3307-3320. https://doi.org/10.1109/TIP.2021.3060166
  • Alam, M. M., & Islam, M. T. (2019). Machine learning approach of automatic identification and counting of blood cells. Healthcare Technology Letters, 6(4), 103-108. https://doi.org/10.1049/htl.2018.5098
  • Ambikumar, A. S., Bailey, D. G., & Gupta, G. S. (2016, November). Extending the depth of field in microscopy: A review. In 2016 International Conference on Image and Vision Computing New Zealand (IVCNZ) (pp. 1-6). IEEE.
  • An, Y., Kang, G., Kim, I. J., Chung, H. S., & Park, J. (2008, December). Shape from focus through Laplacian using 3D window. In 2008 Second International Conference on Future Generation Communication and Networking (Vol. 2, pp. 46-50). IEEE.
  • Cao, Z., Zhai, C., Li, J., Xian, F., & Pei, S. (2017). Combination of color coding and wavefront coding for extended depth of field. Optics Communications, 392, 252-257. https://doi.org/10.1016/j.optcom.2017.02.016
  • Cengil, E., Çınar, A., & Yıldırım, M. (2022). A hybrid approach for efficient multi‐classification of white blood cells based on transfer learning techniques and traditional machine learning methods. Concurrency and Computation: Practice and Experience, 34(6), e6756. https://doi.org/10.1002/cpe.6756
  • Crete, F., Dolmiere, T., Ladret, P., & Nicolas, M. (2007, February). The blur effect: perception and estimation with a new no-reference perceptual blur metric. In Human Vision and Electronic Imaging XII (Vol. 6492, pp. 196-206). SPIE.
  • Danışmaz, S., Emir, S. N., Doğan, H., & Doğan, R. Ö. (2023). Odaklama derinliğinin artırılmasında derin özelliklerin odaklama değerlerinin çıkarılmasındaki etkilerinin incelenmesi. Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi, 31(3), 917-930. https://doi.org/10.31796/ogummf.1299670
  • Dogan, H., Dogan, R. O., Ay, I., & Sezen, S. F. (2024). DL-EDOF: Novel Multi-Focus Image Data Set and Deep Learning-Based Approach for More Accurate and Specimen-Free Extended Depth of Focus. Journal of Imaging Informatics in Medicine, 1-23. https://doi.org/10.1007/s10278-024-01076-z
  • Dowski, E. R., & Cathey, W. T. (1995). Extended depth of field through wave-front coding. Applied Optics, 34(11), 1859-1866. https://doi.org/10.1364/AO.34.001859
  • Du, H., Dong, L., Liu, M., Zhao, Y., Wu, Y., Li, X., ... & Kong, L. (2019). Increasing aperture and depth of field simultaneously with wavefront coding technology. Applied Optics, 58(17), 4746-4752. https://doi.org/10.1364/AO.58.004746
  • Elmalem, S., Giryes, R., & Marom, E. (2018). Learned phase coded aperture for the benefit of depth of field extension. Optics Express, 26(12), 15316-15331. https://doi.org/10.1364/OE.26.015316
  • Forster, B., Van De Ville, D., Berent, J., Sage, D., & Unser, M. (2004). Complex wavelets for extended depth‐of‐field: A new method for the fusion of multichannel microscopy images. Microscopy Research and Technique, 65(1‐2), 33-42. https://doi.org/10.1002/jemt.20092
  • Geusebroek, J. M., Cornelissen, F., Smeulders, A. W., & Geerts, H. (2000). Robust autofocusing in microscopy. Cytometry: The Journal of the International Society for Analytical Cytology, 39(1), 1-9. https://doi.org/10.1002/(SICI)1097-0320(20000101)39:1<1::AID-CYTO2>3.0.CO;2-J
  • Gu, W., & Sun, K. (2024). AYOLOv5: Improved YOLOv5 based on attention mechanism for blood cell detection. Biomedical Signal Processing and Control, 88, 105034. https://doi.org/10.1016/j.bspc.2023.105034
  • Guo, Y., Shahin, A. I., & Garg, H. (2024). An indeterminacy fusion of encoder-decoder network based on neutrosophic set for white blood cells segmentation. Expert Systems with Applications, 246, 123156. https://doi.org/10.1016/j.eswa.2024.123156
  • H Mohamed, E., H El-Behaidy, W., Khoriba, G., & Li, J. (2020). Improved white blood cells classification based on pre-trained deep learning models. Journal of Communications Software and Systems, 16(1), 37-45. https://doi.org/10.24138/jcomss.v16i1.818
  • Helmli, F. S., & Scherer, S. (2001, June). Adaptive shape from focus with an error estimation in light microscopy. In ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat. (pp. 188-193). IEEE.
  • Hermessi, H., Mourali, O., & Zagrouba, E. (2021). Multimodal medical image fusion review: Theoretical background and recent advances. Signal Processing, 183, 108036. https://doi.org/10.1016/j.sigpro.2021.108036 Khan, Z., hamad Shirazi, S., Shahzad, M., Munir, A., Rasheed, A., Xie, Y., & Gul, S. (2024). A Framework for Segmentation and Classification of Blood Cells Using Generative Adversarial Networks. IEEE Access, 12, 51995-52015. https://doi.org/10.1109/ACCESS.2024.3378575
  • Lee, S. Y., Kumar, Y., Cho, J. M., Lee, S. W., & Kim, S. W. (2008). Enhanced autofocus algorithm using robust focus measure and fuzzy reasoning. IEEE Transactions on Circuits and Systems for Video Technology, 18(9), 1237-1246. https://doi.org/10.1109/TCSVT.2008.924105
  • Lee, S. Y., Yoo, J. T., Kumar, Y., & Kim, S. W. (2009). Reduced energy-ratio measure for robust autofocusing in digital camera. IEEE Signal Processing Letters, 16(2), 133-136. https://doi.org/10.1109/LSP.2008.2008938
  • Li, Y., Wang, J., Zhang, X., Hu, K., Ye, L., Gao, M., ... & Xu, M. (2022). Extended depth-of-field infrared imaging with deeply learned wavefront coding. Optics Express, 30(22), 40018-40031. https://doi.org/10.1364/OE.471443
  • Lin, T. Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., ... & Zitnick, C. L. (2014). Microsoft coco: Common objects in context. In Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13 (pp. 740-755). Springer International Publishing.
  • Lorenzo, J., Castrillon, M., Méndez, J., & Deniz, O. (2008, December). Exploring the use of local binary patterns as focus measure. In 2008 International Conference on Computational Intelligence for Modelling Control & Automation (pp. 855-860). IEEE.
  • Lu, Y., Qin, X., Fan, H., Lai, T., & Li, Z. (2021). WBC-Net: A white blood cell segmentation network based on UNet++ and ResNet. Applied Soft Computing, 101, 107006. https://doi.org/10.1016/j.asoc.2020.107006
  • Lu, N., Tay, H. M., Petchakup, C., He, L., Gong, L., Maw, K. K., ... & Hou, H. W. (2023). Label-free microfluidic cell sorting and detection for rapid blood analysis. Lab on a Chip, 23(5), 1226-1257. https://doi.org/10.1039/D2LC00904H
  • Mahmood, F., Mahmood, J., Zeb, A., & Iqbal, J. (2018, April). 3D shape recovery from image focus using Gabor features. In Tenth international conference on machine vision (ICMV 2017) (Vol. 10696, pp. 368-375). SPIE.
  • Malik, A. S., & Choi, T. S. (2008). A novel algorithm for estimation of depth map using image focus for 3D shape recovery in the presence of noise. Pattern Recognition, 41(7), 2200-2225. https://doi.org/10.1016/j.patcog.2007.12.014
  • Minhas, R., Mohammed, A. A., & Wu, Q. J. (2011). Shape from focus using fast discrete curvelet transform. Pattern Recognition, 44(4), 839-853. https://doi.org/10.1016/j.patcog.2010.10.015
  • Mittal, A., Moorthy, A. K., & Bovik, A. C. (2012). No-reference image quality assessment in the spatial domain. IEEE Transactions on Image Processing, 21(12), 4695-4708. https://doi.org/10.1109/TIP.2012.2214050
  • Mittal, A., Soundararajan, R., & Bovik, A. C. (2012). Making a “completely blind” image quality analyzer. IEEE Signal Processing Letters, 20(3), 209-212. https://doi.org/10.1109/LSP.2012.2227726
  • Mo, X., Zhang, T., Wang, B., Huang, X., Kuang, C., & Liu, X. (2019). Alleviating image artifacts in wavefront coding extended depth of field imaging system. Optics Communications, 436, 232-238. https://doi.org/10.1016/j.optcom.2018.12.006
  • Pan, C., Chen, J., Zhang, R., & Zhuang, S. (2008). Extension ratio of depth of field by wavefront coding method. Optics Express, 16(17), 13364-13371. https://doi.org/10.1364/OE.16.013364
  • Patil, A. M., Patil, M. D., & Birajdar, G. K. (2021). White blood cells image classification using deep learning with canonical correlation analysis. Irbm, 42(5), 378-389. https://doi.org/10.1016/j.irbm.2020.08.005
  • Pech-Pacheco, J. L., Cristóbal, G., Chamorro-Martinez, J., & Fernández-Valdivia, J. (2000, September). Diatom autofocusing in brightfield microscopy: a comparative study. In Proceedings 15th International Conference on Pattern Recognition. ICPR-2000 (Vol. 3, pp. 314-317). IEEE.
  • Pertuz, S., Puig, D., & Garcia, M. A. (2013). Analysis of focus measure operators for shape-from-focus. Pattern Recognition, 46(5), 1415-1432. https://doi.org/10.1016/j.patcog.2012.11.011
  • Shen, C. H., & Chen, H. H. (2006, January). Robust focus measure for low-contrast images. In 2006 Digest of technical papers international conference on consumer electronics (pp. 69-70). IEEE.
  • Tessens, L., Ledda, A., Pizurica, A., & Philips, W. (2007, April). Extending the depth of field in microscopy through curvelet-based frequency-adaptive image fusion. In 2007 IEEE International Conference on Acoustics, Speech and Signal Processing-ICASSP'07 (Vol. 1, pp. I-861). IEEE.
  • Venkatanath, N., Praneeth, D., Bh, M. C., Channappayya, S. S., & Medasani, S. S. (2015, February). Blind image quality evaluation using perception based features. In 2015 twenty first national conference on communications (NCC) (pp. 1-6). IEEE.
  • Wee, C. Y., & Paramesran, R. (2007). Measure of image sharpness using eigenvalues. Information Sciences, 177(12), 2533-2552. https://doi.org/10.1016/j.ins.2006.12.023
  • Wei, X., Han, J., Xie, S., Yang, B., Wan, X., & Zhang, W. (2019). Experimental analysis of a wavefront coding system with a phase plate in different surfaces. Applied Optics, 58(33), 9195-9200. https://doi.org/10.1364/AO.58.009195
  • Xie, H., Rong, W., & Sun, L. (2007). Construction and evaluation of a wavelet‐based focus measure for microscopy imaging. Microscopy Research and Technique, 70(11), 987-995. https://doi.org/10.1002/jemt.20506
  • Yap, P. T., & Raveendran, P. (2004). Image focus measure based on Chebyshev moments. IEE Proceedings-Vision, Image and Signal Processing, 151(2), 128-136.
  • Zhao, T., Mauger, T., & Li, G. (2013). Optimization of wavefront-coded infinity-corrected microscope systems with extended depth of field. Biomedical Optics Express, 4(8), 1464-1471. https://doi.org/10.1364/BOE.4.001464

DEVELOPMENT OF A DEEP LEARNING-BASED APPROACH FOR OPTIMAL FOCUSED IMAGING OF BLOOD CELLS

Yıl 2024, Cilt: 27 Sayı: 4, 1465 - 1476, 03.12.2024

Fatma Tuana Doğu , Zeinab Danaei , Hülya Doğan , Ramazan Özgür Doğan , Feride Sena Sezen

https://doi.org/10.17780/ksujes.1506248

Öz

Due to the depth of focus in microscopic systems, samples containing blood cells cannot be imaged completely in focus. This may cause performance loss of artificial intelligence and image processing algorithms. To solve this, extended depth of field approaches are used and an optimally focused image of the sample is obtained. Although many extended depth of field approaches were developed in the literature, there are still various shortcomings in this field, such as high running time and different performance depending on the sample used and the type of microscope. To eliminate these shortcomings in the literature, a new dataset is created for optimally focused imaging of blood cells in microscopic systems and a new deep learning-based approach to depth of field is proposed. Four different criteria are used to evaluate the performance of the study: Perception-based Image Quality Evaluator, Blind Image Spatial Quality Evaluator, Blurring and Naturalness Image Quality. In the developed study, 13 different extended depth of field approaches are tested. In this study, the results of the performance evaluation criteria prove that the deep learning-based extended depth of field approach proposed for optimally focused imaging of blood cells is more performant than other approaches.

Anahtar Kelimeler

Deep learning extended depth of field light microscope focusing measurement operators

Kaynakça

  • Ahmad, M. B., & Choi, T. S. (2007). Application of three dimensional shape from image focus in LCD/TFT displays manufacturing. IEEE Transactions on Consumer Electronics, 53(1), 1-4. https://doi.org/10.1109/TCE.2007.339492
  • Akpinar, U., Sahin, E., Meem, M., Menon, R., & Gotchev, A. (2021). Learning wavefront coding for extended depth of field imaging. IEEE Transactions on Image Processing, 30, 3307-3320. https://doi.org/10.1109/TIP.2021.3060166
  • Alam, M. M., & Islam, M. T. (2019). Machine learning approach of automatic identification and counting of blood cells. Healthcare Technology Letters, 6(4), 103-108. https://doi.org/10.1049/htl.2018.5098
  • Ambikumar, A. S., Bailey, D. G., & Gupta, G. S. (2016, November). Extending the depth of field in microscopy: A review. In 2016 International Conference on Image and Vision Computing New Zealand (IVCNZ) (pp. 1-6). IEEE.
  • An, Y., Kang, G., Kim, I. J., Chung, H. S., & Park, J. (2008, December). Shape from focus through Laplacian using 3D window. In 2008 Second International Conference on Future Generation Communication and Networking (Vol. 2, pp. 46-50). IEEE.
  • Cao, Z., Zhai, C., Li, J., Xian, F., & Pei, S. (2017). Combination of color coding and wavefront coding for extended depth of field. Optics Communications, 392, 252-257. https://doi.org/10.1016/j.optcom.2017.02.016
  • Cengil, E., Çınar, A., & Yıldırım, M. (2022). A hybrid approach for efficient multi‐classification of white blood cells based on transfer learning techniques and traditional machine learning methods. Concurrency and Computation: Practice and Experience, 34(6), e6756. https://doi.org/10.1002/cpe.6756
  • Crete, F., Dolmiere, T., Ladret, P., & Nicolas, M. (2007, February). The blur effect: perception and estimation with a new no-reference perceptual blur metric. In Human Vision and Electronic Imaging XII (Vol. 6492, pp. 196-206). SPIE.
  • Danışmaz, S., Emir, S. N., Doğan, H., & Doğan, R. Ö. (2023). Odaklama derinliğinin artırılmasında derin özelliklerin odaklama değerlerinin çıkarılmasındaki etkilerinin incelenmesi. Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi, 31(3), 917-930. https://doi.org/10.31796/ogummf.1299670
  • Dogan, H., Dogan, R. O., Ay, I., & Sezen, S. F. (2024). DL-EDOF: Novel Multi-Focus Image Data Set and Deep Learning-Based Approach for More Accurate and Specimen-Free Extended Depth of Focus. Journal of Imaging Informatics in Medicine, 1-23. https://doi.org/10.1007/s10278-024-01076-z
  • Dowski, E. R., & Cathey, W. T. (1995). Extended depth of field through wave-front coding. Applied Optics, 34(11), 1859-1866. https://doi.org/10.1364/AO.34.001859
  • Du, H., Dong, L., Liu, M., Zhao, Y., Wu, Y., Li, X., ... & Kong, L. (2019). Increasing aperture and depth of field simultaneously with wavefront coding technology. Applied Optics, 58(17), 4746-4752. https://doi.org/10.1364/AO.58.004746
  • Elmalem, S., Giryes, R., & Marom, E. (2018). Learned phase coded aperture for the benefit of depth of field extension. Optics Express, 26(12), 15316-15331. https://doi.org/10.1364/OE.26.015316
  • Forster, B., Van De Ville, D., Berent, J., Sage, D., & Unser, M. (2004). Complex wavelets for extended depth‐of‐field: A new method for the fusion of multichannel microscopy images. Microscopy Research and Technique, 65(1‐2), 33-42. https://doi.org/10.1002/jemt.20092
  • Geusebroek, J. M., Cornelissen, F., Smeulders, A. W., & Geerts, H. (2000). Robust autofocusing in microscopy. Cytometry: The Journal of the International Society for Analytical Cytology, 39(1), 1-9. https://doi.org/10.1002/(SICI)1097-0320(20000101)39:1<1::AID-CYTO2>3.0.CO;2-J
  • Gu, W., & Sun, K. (2024). AYOLOv5: Improved YOLOv5 based on attention mechanism for blood cell detection. Biomedical Signal Processing and Control, 88, 105034. https://doi.org/10.1016/j.bspc.2023.105034
  • Guo, Y., Shahin, A. I., & Garg, H. (2024). An indeterminacy fusion of encoder-decoder network based on neutrosophic set for white blood cells segmentation. Expert Systems with Applications, 246, 123156. https://doi.org/10.1016/j.eswa.2024.123156
  • H Mohamed, E., H El-Behaidy, W., Khoriba, G., & Li, J. (2020). Improved white blood cells classification based on pre-trained deep learning models. Journal of Communications Software and Systems, 16(1), 37-45. https://doi.org/10.24138/jcomss.v16i1.818
  • Helmli, F. S., & Scherer, S. (2001, June). Adaptive shape from focus with an error estimation in light microscopy. In ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat. (pp. 188-193). IEEE.
  • Hermessi, H., Mourali, O., & Zagrouba, E. (2021). Multimodal medical image fusion review: Theoretical background and recent advances. Signal Processing, 183, 108036. https://doi.org/10.1016/j.sigpro.2021.108036 Khan, Z., hamad Shirazi, S., Shahzad, M., Munir, A., Rasheed, A., Xie, Y., & Gul, S. (2024). A Framework for Segmentation and Classification of Blood Cells Using Generative Adversarial Networks. IEEE Access, 12, 51995-52015. https://doi.org/10.1109/ACCESS.2024.3378575
  • Lee, S. Y., Kumar, Y., Cho, J. M., Lee, S. W., & Kim, S. W. (2008). Enhanced autofocus algorithm using robust focus measure and fuzzy reasoning. IEEE Transactions on Circuits and Systems for Video Technology, 18(9), 1237-1246. https://doi.org/10.1109/TCSVT.2008.924105
  • Lee, S. Y., Yoo, J. T., Kumar, Y., & Kim, S. W. (2009). Reduced energy-ratio measure for robust autofocusing in digital camera. IEEE Signal Processing Letters, 16(2), 133-136. https://doi.org/10.1109/LSP.2008.2008938
  • Li, Y., Wang, J., Zhang, X., Hu, K., Ye, L., Gao, M., ... & Xu, M. (2022). Extended depth-of-field infrared imaging with deeply learned wavefront coding. Optics Express, 30(22), 40018-40031. https://doi.org/10.1364/OE.471443
  • Lin, T. Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., ... & Zitnick, C. L. (2014). Microsoft coco: Common objects in context. In Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13 (pp. 740-755). Springer International Publishing.
  • Lorenzo, J., Castrillon, M., Méndez, J., & Deniz, O. (2008, December). Exploring the use of local binary patterns as focus measure. In 2008 International Conference on Computational Intelligence for Modelling Control & Automation (pp. 855-860). IEEE.
  • Lu, Y., Qin, X., Fan, H., Lai, T., & Li, Z. (2021). WBC-Net: A white blood cell segmentation network based on UNet++ and ResNet. Applied Soft Computing, 101, 107006. https://doi.org/10.1016/j.asoc.2020.107006
  • Lu, N., Tay, H. M., Petchakup, C., He, L., Gong, L., Maw, K. K., ... & Hou, H. W. (2023). Label-free microfluidic cell sorting and detection for rapid blood analysis. Lab on a Chip, 23(5), 1226-1257. https://doi.org/10.1039/D2LC00904H
  • Mahmood, F., Mahmood, J., Zeb, A., & Iqbal, J. (2018, April). 3D shape recovery from image focus using Gabor features. In Tenth international conference on machine vision (ICMV 2017) (Vol. 10696, pp. 368-375). SPIE.
  • Malik, A. S., & Choi, T. S. (2008). A novel algorithm for estimation of depth map using image focus for 3D shape recovery in the presence of noise. Pattern Recognition, 41(7), 2200-2225. https://doi.org/10.1016/j.patcog.2007.12.014
  • Minhas, R., Mohammed, A. A., & Wu, Q. J. (2011). Shape from focus using fast discrete curvelet transform. Pattern Recognition, 44(4), 839-853. https://doi.org/10.1016/j.patcog.2010.10.015
  • Mittal, A., Moorthy, A. K., & Bovik, A. C. (2012). No-reference image quality assessment in the spatial domain. IEEE Transactions on Image Processing, 21(12), 4695-4708. https://doi.org/10.1109/TIP.2012.2214050
  • Mittal, A., Soundararajan, R., & Bovik, A. C. (2012). Making a “completely blind” image quality analyzer. IEEE Signal Processing Letters, 20(3), 209-212. https://doi.org/10.1109/LSP.2012.2227726
  • Mo, X., Zhang, T., Wang, B., Huang, X., Kuang, C., & Liu, X. (2019). Alleviating image artifacts in wavefront coding extended depth of field imaging system. Optics Communications, 436, 232-238. https://doi.org/10.1016/j.optcom.2018.12.006
  • Pan, C., Chen, J., Zhang, R., & Zhuang, S. (2008). Extension ratio of depth of field by wavefront coding method. Optics Express, 16(17), 13364-13371. https://doi.org/10.1364/OE.16.013364
  • Patil, A. M., Patil, M. D., & Birajdar, G. K. (2021). White blood cells image classification using deep learning with canonical correlation analysis. Irbm, 42(5), 378-389. https://doi.org/10.1016/j.irbm.2020.08.005
  • Pech-Pacheco, J. L., Cristóbal, G., Chamorro-Martinez, J., & Fernández-Valdivia, J. (2000, September). Diatom autofocusing in brightfield microscopy: a comparative study. In Proceedings 15th International Conference on Pattern Recognition. ICPR-2000 (Vol. 3, pp. 314-317). IEEE.
  • Pertuz, S., Puig, D., & Garcia, M. A. (2013). Analysis of focus measure operators for shape-from-focus. Pattern Recognition, 46(5), 1415-1432. https://doi.org/10.1016/j.patcog.2012.11.011
  • Shen, C. H., & Chen, H. H. (2006, January). Robust focus measure for low-contrast images. In 2006 Digest of technical papers international conference on consumer electronics (pp. 69-70). IEEE.
  • Tessens, L., Ledda, A., Pizurica, A., & Philips, W. (2007, April). Extending the depth of field in microscopy through curvelet-based frequency-adaptive image fusion. In 2007 IEEE International Conference on Acoustics, Speech and Signal Processing-ICASSP'07 (Vol. 1, pp. I-861). IEEE.
  • Venkatanath, N., Praneeth, D., Bh, M. C., Channappayya, S. S., & Medasani, S. S. (2015, February). Blind image quality evaluation using perception based features. In 2015 twenty first national conference on communications (NCC) (pp. 1-6). IEEE.
  • Wee, C. Y., & Paramesran, R. (2007). Measure of image sharpness using eigenvalues. Information Sciences, 177(12), 2533-2552. https://doi.org/10.1016/j.ins.2006.12.023
  • Wei, X., Han, J., Xie, S., Yang, B., Wan, X., & Zhang, W. (2019). Experimental analysis of a wavefront coding system with a phase plate in different surfaces. Applied Optics, 58(33), 9195-9200. https://doi.org/10.1364/AO.58.009195
  • Xie, H., Rong, W., & Sun, L. (2007). Construction and evaluation of a wavelet‐based focus measure for microscopy imaging. Microscopy Research and Technique, 70(11), 987-995. https://doi.org/10.1002/jemt.20506
  • Yap, P. T., & Raveendran, P. (2004). Image focus measure based on Chebyshev moments. IEE Proceedings-Vision, Image and Signal Processing, 151(2), 128-136.
  • Zhao, T., Mauger, T., & Li, G. (2013). Optimization of wavefront-coded infinity-corrected microscope systems with extended depth of field. Biomedical Optics Express, 4(8), 1464-1471. https://doi.org/10.1364/BOE.4.001464
Toplam 45 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Görüntü İşleme, Derin Öğrenme, Yapay Zeka (Diğer)
Bölüm Bilgisayar Mühendisliği
Yazarlar

Fatma Tuana Doğu 0009-0008-0916-9394

Zeinab Danaei 0000-0002-5881-1960

Hülya Doğan 0000-0003-3695-8539

Ramazan Özgür Doğan 0000-0001-6415-5755

Feride Sena Sezen 0000-0002-7379-2518

Yayımlanma Tarihi 3 Aralık 2024
Gönderilme Tarihi 27 Haziran 2024
Kabul Tarihi 20 Kasım 2024
Yayımlandığı Sayı Yıl 2024Cilt: 27 Sayı: 4

Kaynak Göster

APA Doğu, F. T., Danaei, Z., Doğan, H., Doğan, R. Ö., vd. (2024). KAN HÜCRELERİNİN OPTİMUM ODAKLI GÖRÜNTÜLENMESİ İÇİN DERİN ÖĞRENME TABANLI YAKLAŞIMIN GELİŞTİRİLMESİ. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 27(4), 1465-1476. https://doi.org/10.17780/ksujes.1506248
 Kapak Resmi İndir