{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,2]],"date-time":"2025-11-02T04:58:26Z","timestamp":1762059506914,"version":"build-2065373602"},"reference-count":44,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,7,28]],"date-time":"2022-07-28T00:00:00Z","timestamp":1658966400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Taif University","award":["TURSP-2020\/231"],"award-info":[{"award-number":["TURSP-2020\/231"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JPM"],"abstract":"<jats:p>White blood cells (WBCs) are the important constituent of a blood cell. These blood cells are responsible for defending the body against infections. Abnormalities identified in WBC smears lead to the diagnosis of disease types such as leukocytosis, hepatitis, and immune system disorders. Digital image analysis for infection detection at an early stage can help fast and precise diagnosis, as compared to manual inspection. Sometimes, acquired blood cell smear images from an L2-type microscope are of very low quality. The manual handling, haziness, and dark areas of the image become problematic for an efficient and accurate diagnosis. Therefore, WBC image enhancement needs attention for an effective diagnosis of the disease. This paper proposed a novel virtual hexagonal trellis (VHT)-based image filtering method for WBC image enhancement and contrast adjustment. In this method, a filter named the virtual hexagonal filter (VHF), of size 3 \u00d7 3, and based on a hexagonal structure, is formulated by using the concept of the interpolation of real and square grid pixels. This filter is convolved with WBC ALL-IBD images for enhancement and contrast adjustment. The proposed filter improves the results both visually and statically. A comparison with existing image enhancement approaches proves the validity of the proposed work.<\/jats:p>","DOI":"10.3390\/jpm12081232","type":"journal-article","created":{"date-parts":[[2022,7,28]],"date-time":"2022-07-28T03:21:16Z","timestamp":1658978476000},"page":"1232","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Single Channel Image Enhancement (SCIE) of White Blood Cells Based on Virtual Hexagonal Filter (VHF) Designed over Square Trellis"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2313-8454","authenticated-orcid":false,"given":"Shahid","family":"Rasheed","sequence":"first","affiliation":[{"name":"Department of Computer Science, COMSATS University Islamabad, Wah Campus, Islamabad 47040, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9124-9298","authenticated-orcid":false,"given":"Mudassar","family":"Raza","sequence":"additional","affiliation":[{"name":"Department of Computer Science, COMSATS University Islamabad, Wah Campus, Islamabad 47040, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Muhammad","family":"Sharif","sequence":"additional","affiliation":[{"name":"Department of Computer Science, COMSATS University Islamabad, Wah Campus, Islamabad 47040, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1939-4842","authenticated-orcid":false,"given":"Seifedine","family":"Kadry","sequence":"additional","affiliation":[{"name":"Department of Applied Data Science, Noroff University College, 4612 Kristiansand, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdullah","family":"Alharbi","sequence":"additional","affiliation":[{"name":"Department of Information Technology, College of Computers and Information Technology, Taif University, Taif 21944, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"19428","DOI":"10.1038\/s41598-021-98599-0","article-title":"New segmentation and feature extraction algorithm for classification of white blood cells in peripheral smear images","volume":"11","author":"Tavakoli","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_2","unstructured":"Tigner, A., Ibrahim, S.A., and Murray, I. (2021). Histology, white blood cell. StatPearls, StatPearls Publishing."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.jceh.2021.04.013","article-title":"Kupffer Cell Hyaline Globules in Children With Autoimmune Hepatitis","volume":"12","author":"Khedr","year":"2021","journal-title":"J. Clin. Exp. Hepatol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"e33","DOI":"10.1590\/s1678-9946202163033","article-title":"Death-related factors in HIV\/AIDS patients undergoing hemodialysis in an intensive care unit","volume":"63","author":"Cavalcante","year":"2021","journal-title":"Rev. Inst. Med. Trop. S\u00e3o Paulo"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Walkovich, K., and Connelly, J.A. (2022). Disorders of white blood cells. Lanzkowsky\u2019s Manual of Pediatric Hematology and Oncology, Elsevier.","DOI":"10.1016\/B978-0-12-821671-2.00002-7"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"182","DOI":"10.15575\/join.v6i2.739","article-title":"Enhancement of White Blood Cells Images using Shock Filtering Equation for Classification Problem","volume":"6","author":"Vito","year":"2021","journal-title":"J. Online Inform."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"165877","DOI":"10.1016\/j.ijleo.2020.165877","article-title":"Image enhancement with the preservation of brightness and structures by employing contrast limited dynamic quadri-histogram equalization","volume":"226","author":"Huang","year":"2020","journal-title":"Optik"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1364\/JOSAA.401280","article-title":"Structural similarity assessment of an optical coherence tomographic image enhanced using the wavelet transform technique","volume":"38","author":"Dehshiri","year":"2020","journal-title":"J. Opt. Soc. Am. A"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1007\/s00371-020-02016-y","article-title":"A comprehensive survey on video frame interpolation techniques","volume":"38","author":"Parihar","year":"2022","journal-title":"Vis. Comput."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Pace, T., Manville, D., Lee, H., Cloud, G., and Puritz, J. (2008, January 18\u201319). A multiresolution approach to image enhancement via histogram shaping and adaptive wiener filtering. Proceedings of the SPIE Visual Information Processing XVII, Orlando, FL, USA.","DOI":"10.1117\/12.778038"},{"key":"ref_11","unstructured":"Chen, C., Dou, Q., Chen, H., Qin, J., and Heng, P.A. (February, January 27). Synergistic image and feature adaptation: Towards cross-modality domain adaptation for medical image segmentation. Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, HI, USA."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"113094","DOI":"10.1109\/ACCESS.2019.2934160","article-title":"Detection of Triple JPEG Compressed Color Images","volume":"7","author":"Wang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.micron.2018.01.010","article-title":"Fast and robust segmentation of white blood cell images by self-supervised learning","volume":"107","author":"Zheng","year":"2018","journal-title":"Micron"},{"key":"ref_14","first-page":"17","article-title":"Automatic red blood cell counting using watershed segmentation","volume":"14","author":"Chourasiya","year":"2014","journal-title":"Hemoglobin"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Harun, N.H., Bakar, J.A., Abd Wahab, Z., Osman, M.K., and Harun, H. (2020, January 18\u201319). Color image enhancement of acute leukemia cells in blood microscopic image for leukemia detection sample. Proceedings of the 2020 IEEE 10th Symposium on Computer Applications & Industrial Electronics (ISCAIE), Penang, Malaysia.","DOI":"10.1109\/ISCAIE47305.2020.9108810"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"10657","DOI":"10.1109\/ACCESS.2020.3048172","article-title":"A Review on Traditional Machine Learning and Deep Learning Models for WBCs Classification in Blood Smear Images","volume":"9","author":"Khan","year":"2020","journal-title":"IEEE Access"},{"key":"ref_17","unstructured":"Salihah, A.A., Mashor, M.Y., Harun, N.H., Abdullah, A.A., and Rosline, H. (December, January 30). Improving colour image segmentation on acute myelogenous leukaemia images using contrast enhancement techniques. Proceedings of the 2010 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES), Kuala Lumpur, Malaysia."},{"key":"ref_18","first-page":"20","article-title":"White blood cells identification and counting from microscopic blood image","volume":"7","author":"Putzu","year":"2013","journal-title":"Int. J. Med. Health Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"741","DOI":"10.1109\/TIP.2006.888338","article-title":"Transform Coefficient Histogram-Based Image Enhancement Algorithms Using Contrast Entropy","volume":"16","author":"Agaian","year":"2007","journal-title":"IEEE Trans. Image Process."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1007\/s11760-012-0293-5","article-title":"Vision-based hexagonal image processing using Hex-Gabor","volume":"8","author":"Veni","year":"2012","journal-title":"Signal Image Video Process."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Laine, A.F., Schuler, S., Huda, W., Honeyman-Buck, J.C., and Steinbach, B.G. (1993). Hexagonal wavelet processing of digital mammography. Medical Imaging 1993: Image Processing, SPIE.","DOI":"10.1117\/12.154543"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Huang, C.-H., and Lin, C.-T. (2006, January 4\u20137). Image enhancement algorithm for hexagonal cellular neural networks. Proceedings of the APCCAS 2006 IEEE Asia Pacific Conference on Circuits and Systems, Singapore.","DOI":"10.1109\/APCCAS.2006.342451"},{"key":"ref_23","unstructured":"He, X., and Jia, W. (2005, January 4\u20136). Hexagonal structure for intelligent vision. Proceedings of the 2005 International Conference on Information and Communication Technologies, Las Vegas, NV, USA."},{"key":"ref_24","first-page":"853","article-title":"Image enhancement of medical images using Gabor Filter Bank on hexagonal sampled grids","volume":"4","author":"Narayanankutty","year":"2010","journal-title":"Int. J. Electron. Commun. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"043022","DOI":"10.1117\/1.JEI.23.4.043022","article-title":"Hexagonal filters for ultrasound images","volume":"23","author":"Fox","year":"2014","journal-title":"J. Electron. Imaging"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.displa.2017.09.005","article-title":"Implementation of a simulated display for hexagonal image processing","volume":"50","author":"Li","year":"2017","journal-title":"Displays"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2021\/5584464","article-title":"Efficient Single Image Dehazing Model Using Metaheuristics-Based Brightness Channel Prior","volume":"2021","author":"Kehar","year":"2021","journal-title":"Math. Probl. Eng."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2150053","DOI":"10.1142\/S0219467821500534","article-title":"Single-Image Dehazing via Dark Channel Prior and Adaptive Threshold","volume":"21","author":"Pan","year":"2021","journal-title":"Int. J. Image Graph."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2040","DOI":"10.1002\/mrm.28573","article-title":"Unsupervised learning for magnetization transfer contrast MR fingerprinting: Application to CEST and nuclear Overhauser enhancement imaging","volume":"85","author":"Kang","year":"2020","journal-title":"Magn. Reson. Med."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Chen, X., Liu, Y., Zhang, Z., Qiao, Y., and Dong, C. (2021, January 19\u201325). HDRUnet: Single image hdr reconstruction with denoising and dequantization. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPRW53098.2021.00045"},{"key":"ref_31","unstructured":"Agaian, S.S., Panetta, K., and Grigoryan, A.M. (2000, January 19\u201322). A new measure of image enhancement. Proceedings of the IASTED International Conference on Signal Processing & Communication, Malaga, Spain."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Dileep, M., and Murthy, A.S. (2011, January 23\u201324). A comparison between different colour image contrast enhancement algorithms. Proceedings of the 2011 International Conference on Emerging Trends in Electrical and Computer Technology, Nagercoil, India.","DOI":"10.1109\/ICETECT.2011.5760209"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Wong, S.L., Yu, Y.P., Ho, N.A.J., and Paramesran, R. (2014, January 1\u20134). Comparative analysis of underwater image enhancement methods in different color spaces. Proceedings of the 2014 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), Sarawak, Malaysia.","DOI":"10.1109\/ISPACS.2014.7024420"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"4695","DOI":"10.1109\/TIP.2012.2214050","article-title":"No-Reference Image Quality Assessment in the Spatial Domain","volume":"21","author":"Mittal","year":"2012","journal-title":"IEEE Trans. Image Process."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1109\/LSP.2012.2227726","article-title":"Making a \u201ccompletely blind\u201d image quality analyzer","volume":"20","author":"Mittal","year":"2012","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"3339","DOI":"10.1109\/TIP.2012.2191563","article-title":"Blind Image Quality Assessment: A Natural Scene Statistics Approach in the DCT Domain","volume":"21","author":"Saad","year":"2012","journal-title":"IEEE Trans. Image Process."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1002\/spe.2834","article-title":"An efficient radix trie -based semantic visual indexing model for large-scale image retrieval in cloud environment","volume":"51","author":"Krishnaraj","year":"2020","journal-title":"Softw. Pract. Exp."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"104665","DOI":"10.1016\/j.compbiomed.2021.104665","article-title":"Artificial intelligence-driven assessment of radiological images for COVID-19","volume":"136","author":"Bouchareb","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"ref_39","unstructured":"Lamberti, W.F. (2022). Classification of White Blood Cell Leukemia with Low Number of Interpretable and Explainable Features. arXiv."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"12227","DOI":"10.1007\/s11042-019-08417-z","article-title":"Acute lymphoblastic leukemia image segmentation driven by stochastic fractal search","volume":"79","author":"Dhal","year":"2020","journal-title":"Multimedia Tools Appl."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Mahdi, T.F., Daway, H.G., and Jouda, J. (2022). Automatic White Blood Cell Detection Depending on Color Features Based on Red and (A) in the LAB Space. Pervasive Computing and Social Networking, Springer.","DOI":"10.1007\/978-981-16-5640-8_44"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Dong, N., Feng, Q., Zhai, M., Chang, J., and Mai, X. (2022). A novel feature fusion based deep learning framework for white blood cell classification. J. Ambient. Intell. Humaniz. Comput., 1\u201313.","DOI":"10.1007\/s12652-021-03642-7"},{"key":"ref_43","first-page":"1576","article-title":"COVID-19 outcomes in patients waitlisted for kidney transplantation and kidney transplant recipients","volume":"21","author":"Salinas","year":"2020","journal-title":"Am. J. Transplant."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1970","DOI":"10.3934\/mbe.2022093","article-title":"Improved Otsu and Kapur approach for white blood cells segmentation based on LebTLBO optimization for the detection of Leukemia","volume":"19","author":"Deshpande","year":"2021","journal-title":"Math. Biosci. Eng."}],"container-title":["Journal of Personalized Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4426\/12\/8\/1232\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:57:53Z","timestamp":1760140673000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4426\/12\/8\/1232"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,28]]},"references-count":44,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["jpm12081232"],"URL":"https:\/\/doi.org\/10.3390\/jpm12081232","relation":{},"ISSN":["2075-4426"],"issn-type":[{"type":"electronic","value":"2075-4426"}],"subject":[],"published":{"date-parts":[[2022,7,28]]}}}