Published December 19, 2023 | Version v1
Book chapter Open

Recognition of images of blood cells using texture and neural networks to diagnose leukemia

Creators

  • 1. National Autonomous University of Mexico (UNAM)
  • 2. Institute of Information Technology

Description

Analysis of white blood cells from blood can help to detect Acute Lymphoblastic Leukemia, a potentially fatal blood cancer if left untreated. The morphological analysis of blood cells images is typically performed manually by an expert; however, this method has numerous drawbacks, including slow analysis, low precision, and the results depend on the operator’s skill. We have developed and present here an automated method for the identification and classification of white blood cells using microscopic images of peripheral blood smears. Once the image has been obtained, we propose describing it using brightness, contrast, and micro-contour orientation histograms. Each of these descriptions provides a coding of the image, which in turn provides n parameters. The extracted characteristics are presented to an encoder’s input. The encoder generates a high-dimensional binary output vector, which is presented to the input of the neural classifier. This paper presents the performance of one classifier, the Random Threshold Classifier. The classifier’s output is the recognized class, which is either a healthy cell or an Acute Lymphoblastic Leukemia-affected cell. As shown below, the proposed neural Random Threshold Classifier achieved a recognition rate of 98.3 % when the data has partitioned on 80 % training set and 20 % testing set for. Our system of image recognition is evaluated using the public dataset of peripheral blood samples from Acute Lymphoblastic Leukemia Image Database. It is important to mention that our system could be implemented as a computational tool for detection of other diseases, where blood cells undergo alterations, such as Covid-19.

Files

48-Book Manuscript-170-1-10-20231220.pdf

Files (2.8 MB)

Name Size Download all
md5:6bd3ea6d80c2c2e1858c39a6e5f2112a
2.8 MB Preview Download

Additional details

References

  • González, C. (2011). Recognition of Textures in Medical Images. UNAM. Available at: https://xdoc.mx/documents/tesis-unam-5ddae474b690d
  • Mammadova, M., Bayramov, N., Jabrayilova, Z. (2021). Development of the principles of fuzzy rule-based system for hepatocelular carcinoma staging. EUREKA: Physics and Engineering, 3, 3–13. doi: https://doi.org/10.21303/2461-4262.2021.001829
  • Mammadova, M., Jabrayilova, Z. (2022). Synthesis of decision making in a distributed intelligent personnel health management system on offshore oil platform. EUREKA: Physics and Engineering, 4, 179–192. doi: https://doi.org/10.21303/2461-4262.2022.002520
  • Huerta Aragonés, J., Cela de Julián, E. E. (2019). Practical Hematology: Interpretation of the Complete Blood Count and Coagulation Tests. Pediatric Update Congress 2019. Madrid: Lúa Ed. 3.0, 507–528.
  • Rahi, M. S., Jindal, V., Reyes, S.-P., Gunasekaran, K., Gupta, R., Jaiyesimi, I. (2021). Hematologic disorders associated with COVID-19: a review. Annals of Hematology, 100 (2), 309–320. doi: https://doi.org/10.1007/s00277-020-04366-y
  • Zini, G., Bellesi, S., Ramundo, F., d'Onofrio, G. (2020). Morphological anomalies of circulating blood cells in COVID‐19. American Journal of Hematology, 95 (7), 870–872. doi: https://doi.org/10.1002/ajh.25824
  • Salib, C., Khattar, P., Cheng, J., Teruya-Feldstein, J. (2020). Atypical Peripheral Blood Cell Morphology in COVID-19 (Sars-CoV-2) Patients from Mount Sinai Health System in New York City. Blood, 136, 26–27. doi: https://doi.org/10.1182/blood-2020-142581
  • Introcaso, G., Biondi, M. L. (2022). Morphological anomalies of blood cells in COVID-19 patients. Open Journal of Clinical and Medical Images, 2 (2), 1080. doi: https://doi.org/10.52768/2833-2725/1080
  • Guía de Referencia Rápida (2018). Diagnosis and Treatment Lymphoblastic Leukemia Acute In The Adult. Instituto Mexicano del Seguro Social. Dirección de Prestaciones Médicas. Unidad de Atención Médica. Coordinación de Unidades Médicas de Alta Especialidad. División de Excelencia Clínica.
  • Putzu, L., Caocci, G., Di Ruberto, C. (2014). Leucocyte classification for leukaemia detection using image processing techniques. Artificial Intelligence in Medicine, 62 (3), 179–191. doi: https://doi.org/10.1016/j.artmed.2014.09.002
  • Piuri, V., Scotti, F. (2004). Morphological classification of blood leucocytes by microscope images. 2004 IEEE International Conference OnComputational Intelligence for Measurement Systems and Applications, 2004. CIMSA. doi: https://doi.org/10.1109/cimsa.2004.1397242
  • Ruberto, C. D., Putzu, L. (2014). Accurate Blood Cells Segmentation through Intuitionistic Fuzzy Set Threshold. 2014 Tenth International Conference on Signal-Image Technology and Internet-Based Systems. doi: https://doi.org/10.1109/sitis.2014.43
  • Ongun, G., Halici, U., Leblebicioglu, K., Atalay, V., Beksac, M., Beksac, S. (2001). An automated differential blood count system. 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society. doi: https://doi.org/10.1109/iembs.2001.1017309
  • Mohamed, M., Far, B., Guaily, A. (2012). An efficient technique for white blood cells nuclei automatic segmentation. 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC). doi: https://doi.org/10.1109/icsmc.2012.6377703
  • Mohammed, E. A., Mohamed, M. M. A., Far, B. H., Naugler, C. (2014). Peripheral blood smear image analysis: A comprehensive review. Journal of Pathology Informatics, 5 (1), 9. doi: https://doi.org/10.4103/2153-3539.129442
  • Madhloom, H. T., Kareem, S. A., Ariffin, H., Zaidan, A. A., Alanazi, H. O., Zaidan, B. B. (2010). An Automated White Blood Cell Nucleus Localization and Segmentation using Image Arithmetic and Automatic Threshold. Journal of Applied Sciences, 10 (11), 959–966. doi: https://doi.org/10.3923/jas.2010.959.966
  • Halim, N. H. A., Mashor, M. Y., Hassan, R. (2011). Automatic Blasts Counting for Acute Leukemia Based on Blood Samples. International Journal of Research and Reviews in Computer Science, 2 (4), 971–976. Available at: https://www.proquest.com/scholarly-journals/automatic-blasts-counting-acute-leukemia-based-on/docview/903775031/se-2
  • Tang, J. (2010). A color image segmentation algorithm based on region growing. 2010 2nd International Conference on Computer Engineering and Technology. doi: https://doi.org/10.1109/iccet.2010.5486012
  • Navon, E., Miller, O., Averbuch, A. (2005). Color image segmentation based on adaptive local thresholds. Image and Vision Computing, 23 (1), 69–85. doi: https://doi.org/10.1016/j.imavis.2004.05.011
  • Mahameed, A. I., Ahmed, M. K., Abdullah, N. B. (2022). Iris recognition method based on segmentation. EUREKA: Physics and Engineering, 2, 166–176. doi: https://doi.org/10.21303/2461-4262.2022.002341
  • Kumar, R. S., Verma, A., Singh, J. (2007). Color Image Segmentation and Multi-Level Thresholding by Maximization of Conditional Entropy. International Journal of Computer and Information Engineering, 1 (6), 1633–1641. doi: https://doi.org/10.5281/zenodo.1059435
  • Scotti, F. (2005). Automatic morphological analysis for acute leukemia identification in peripheral blood microscope images. CIMSA. 2005 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, 2005. doi: https://doi.org/10.1109/cimsa.2005.1522835
  • Mahmood, N. H., Lim, P. C., Mazalan, S. M., Razak, M. A. A. (2013). Blood cells extraction using color based segmentation technique. Int. J. Life Sci. Biotechnol. Pharma Res, 2 (2), 233–240. Available at: https://www.researchgate.net/publication/289676562_Blood_cells_extraction_using_color_based_segmentation_technique
  • Di Ruberto, C., Loddo, A., Puglisi, G. (2020). Blob Detection and Deep Learning for Leukemic Blood Image Analysis. Applied Sciences, 10 (3), 1176. doi: https://doi.org/10.3390/app10031176
  • Skuratov, V., Kuzmin, K., Nelin, I., Sedankin, M. (2020). Application of a convolutional neural network and a Kohonen network for accelerated detection and recognition of objects in images. EUREKA: Physics and Engineering, 4, 11–18. doi: https://doi.org/10.21303/2461-4262.2020.001360
  • Skuratov, V., Kuzmin, K., Nelin, I., Sedankin, M. (2020). Application of kohonen self-organizing map to search for region of interest in the detection of objects. EUREKA: Physics and Engineering, 1, 62–69. doi: https://doi.org/10.21303/2461-4262.2020.001133
  • Labati, R. D., Piuri, V., Scotti, F. (2011). All-IDB: The acute lymphoblastic leukemia image database for image processing. 2011 18th IEEE International Conference on Image Processing. doi: https://doi.org/10.1109/icip.2011.6115881
  • Acharya, V., Kumar, P. (2019). Detection of acute lymphoblastic leukemia using image segmentation and data mining algorithms. Medical & Biological Engineering & Computing, 57 (8), 1783–1811. doi: https://doi.org/10.1007/s11517-019-01984-1
  • Mirmohammadi, P., Ameri, M., Shalbaf, A. (2021). Recognition of acute lymphoblastic leukemia and lymphocytes cell subtypes in microscopic images using random forest classifier. Physical and Engineering Sciences in Medicine, 44 (2), 433–441. doi: https://doi.org/10.1007/s13246-021-00993-5
  • He, K., Zhang, X., Ren, S., Sun, J. (2016). Identity Mappings in Deep Residual Networks. Computer Vision – ECCV 2016, 630–645. doi: https://doi.org/10.1007/978-3-319-46493-0_38
  • Rodrigues, L. F., Backes, A. R., Travençolo, B. A. N., de Oliveira, G. M. B. (2022). Optimizing a Deep Residual Neural Network with Genetic Algorithm for Acute Lymphoblastic Leukemia Classification. Journal of Digital Imaging, 35 (3), 623–637. doi: https://doi.org/10.1007/s10278-022-00600-3
  • Chand, S., Vishwakarma, V. P. (2022). A novel Deep Learning Framework (DLF) for classification of Acute Lymphoblastic Leukemia. Multimedia Tools and Applications, 81 (26), 37243–37262. doi: https://doi.org/10.1007/s11042-022-13543-2
  • Rejula, M. A., Amutha, S., Shilpa, G. M. (2023). Classification of acute lymphoblastic leukemia using improved ANFIS. Multimedia Tools and Applications, 82 (23), 35475–35491. doi: https://doi.org/10.1007/s11042-023-15113-6
  • Renuka, T. V., Surekha, B. (2021). Acute-Lymphoblastic Leukemia Detection Through Deep Transfer Learning Approach of Neural Network. Lecture Notes in Networks and Systems, 163–170. doi: https://doi.org/10.1007/978-981-33-4073-2_17
  • Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M. et al. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60–88. doi: https://doi.org/10.1016/j.media.2017.07.005
  • Alam, A., Anwar, S. (2021). Detecting Acute Lymphoblastic Leukemia Through Microscopic Blood Images Using CNN. Trends in Wireless Communication and Information Security, 207–214. doi: https://doi.org/10.1007/978-981-33-6393-9_22
  • Wady, S. H. (2022). Classification of Acute Lymphoblastic Leukemia through the Fusion of Local Descriptors. UHD Journal of Science and Technology, 6 (1), 21–33. doi: https://doi.org/10.21928/uhdjst.v6n1y2022.pp21-33
  • Alagu, S., Bagan, K. B. (2019). Acute Lymphoblastic Leukemia Diagnosis in Microscopic Blood Smear Images Using Texture Features and SVM Classifier. Alliance International Conference on Artificial Intelligence and Machine Learning (AICAAM), 175–186. Available at: https://www.researchgate.net/publication/343444613_Acute_Lymphoblastic_leukemia_diagnosis_in_microscopic_blood_smear_images_using_texture_features_and_SVM_classifier
  • González, C., Baydyk, T., Kussul, E. (2010). Recognition of Textures in Medical Images. 1st International Congress on Instrumentation and Applied Sciences ICIAS.
  • Schwartz, R. (1984). Pat. No. US4433912A. Method and a circuit for determining a contour in an image. published: 28.02.1984. Available at: https://patents.google.com/patent/US4433912A/en?oq=4433912+US
  • Duda, R., Hart, P., Stork, D. (2000). Pattern classification. John Wiley & Sons, Inc., 680.
  • Goltsev, A., Gritsenko, V., Kussul, E., Baidyk, T. (2015). Finding the Texture Features Characterizing the Most Homogeneous Texture Segment in the Image. Lecture Notes in Computer Science, 287–300. doi: https://doi.org/10.1007/978-3-319-19258-1_25
  • Roldan-Serrato, L., Baydyk, T., Kussul, E., Escalante-Estrada, A., Rodriguez, M. T. G. (2015). Recognition of pests on crops with a random subspace classifier. 2015 4th International Work Conference on Bioinspired Intelligence (IWOBI). doi: https://doi.org/10.1109/iwobi.2015.7160138
  • Roldán-Serrato, K. L., Escalante-Estrada, J. A. S., Rodríguez-González, M. T. (2018). Automatic pest detection on bean and potato crops by applying neural classifiers. Engineering in Agriculture, Environment and Food, 11 (4), 245–255. doi: https://doi.org/10.1016/j.eaef.2018.08.003
  • Bennett, J. M., Catovsky, D., Daniel, M., Flandrin, G., Galton, D. A. G., Gralnick, H. R., Sultan, C. (1976). Proposals for the Classification of the Acute Leukaemias French‐American‐British (FAB) Co‐operative Group. British Journal of Haematology, 33 (4), 451–458. doi: https://doi.org/10.1111/j.1365-2141.1976.tb03563.x
  • Baidyk, T., Kussul, E., Makeyev, O. (2005). Texture Recognition with Random Subspace Neural Classifier. WSEAS Transactions on Circuits and Systems, 4 (4), 319–324.
  • Baidyk, T., Kussul, E., Makeyev, O. (2008). General purpose image recognition systems based on neural classifiers. Progress in Neurocomputing Research. Chap. 3. NOVA Publishers, 83–114.
  • Kussul, E., Makeyev, O., Baidyk, T., Martín-Gonzalez, A., Toledo-Ramirez, G. (2011). Some Applications of Computer Vision Systems in Micromechanics. Computer Vision. Nova Science Publishers.
  • Gayathri, S., Jyothi, R. L. (2018). An Automated Leucocyte Classification for Leukemia Detection. International Research Journal of Engineering and Technology (IRJET), 5 (5), 4254–4264. Available at: https://www.irjet.net/archives/V5/i5/IRJET-V5I5908.pdf
  • Curtidor, A., Kussul, E., Baydyk, T., Mammadova, M. (2023). Analysis and automated classification of images of blood cells to diagnose acute lymphoblastic leukemia. EUREKA: Physics and Engineering, 5, 177–190. doi: https://doi.org/10.21303/2461-4262.2023.003070