Abstract
Color image segmentation is essential for medical image processing to figure out the cells, tissues, lesion areas, etc. The hippocampus is an extension of the temporal lobe of the brain. This area of the brain has been intensively studied for its clinical significance. It is the first and most severely affected structure in neuropsychiatric conditions. Meta-heuristic algorithm-based optimal segmentation is a widely accepted method in the medical domain. In this work, a hybrid method called the quantum-inspired firefly algorithm (QIFA) has been implemented in a multi-core environment to perform color segmentation of the hippocampus images in a parallel manner. The parallel QIFA runs on three different channels, Red, Green, and Blue of the input color image, and a subsequent merging is applied. The correlation has been considered as the objective function. Finally, a study has been carried out concerning various image segmentation evaluation parameters, and the proposed method has been compared to other metaheuristic algorithms. The analysis of the results shows that the method is effective for medical image segmentation. The speed-up of the technique has also been examined in detail for various image sizes and color levels.
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References
Lange, N., et al.: Two macroscopic and microscopic brain imaging studies of human hippocampus in early Alzheimer’s disease and schizophrenia research. Stat. Med. 23(2), 327–350 (2004). https://doi.org/10.1002/sim.1720
Mesejo, P., Ibáñez, Ó., Cordón, Ó., Cagnoni, S.: A survey on image segmentation using metaheuristic-based deformable models: state of the art and critical analysis. Appl. Soft Comput. 44, 1–29 (2016). https://doi.org/10.1016/j.asoc.2016.03.004
Heckers, S.: Neuroimaging studies of the hippocampus in schizophrenia. Hippocampus 11(5), 520–528 (2001). https://doi.org/10.1002/hipo.1068
Heckers, S., et al.: Impaired recruitment of the hippocampus during conscious recollection in schizophrenia. Nat. Neurosci. 1(4), 318–323 (1998). https://doi.org/10.1038/1137
Ragland, J.D.: Effect of schizophrenia on frontotemporal activity during word encoding and recognition: a PET cerebral blood flow study. Am. J. Psychiatry 158(7), 1114–1125 (2001). https://doi.org/10.1176/appi.ajp.158.7.1114
Chakraborty, S., et al.: Modified cuckoo search algorithm in microscopic image segmentation of hippocampus. Microsc. Res. Tech. 80(10), 1051–1072 (2017). https://doi.org/10.1002/jemt.22900
Dey, N., Ashour, A.S.: Meta-heuristic algorithms in medical image segmentation. In: Advancements in Applied Metaheuristic Computing, pp. 185-203. IGI Global (2018). https://doi.org/10.4018/978-1-5225-4151-6.ch008
Ghosh, P., Mali, K., Das, S.K.: Chaotic firefly algorithm-based fuzzy C-means algorithm for segmentation of brain tissues in magnetic resonance images. J. Vis. Commun. Image Representation 54, 63–79 (2018). https://doi.org/10.1016/j.jvcir.2018.04.007
Giuliani, D.: A grayscale segmentation approach using the firefly algorithm and the gaussian mixture model. Int. J. Swarm Intell. Res. 9(1), 39–57 (2018). https://doi.org/10.4018/ijsir.2018010103
Oliva, D., Abd Elaziz, M., Hinojosa, S.: Multilevel thresholding for image segmentation based on metaheuristic algorithms. In: Metaheuristic Algorithms for Image Segmentation: Theory and Applications. SCI, vol. 825, pp. 59–69. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-12931-6_6
Upadhyay, P., Chhabra, J.K.: Kapur’s entropy based optimal multilevel image segmentation using crow search algorithm. Appl. Soft Comput. 97, 105522 (2020). https://doi.org/10.1016/j.asoc.2019.105522
Hernandez del Rio, A.A., Cuevas, E., Zaldivar, D.: Multi-level image thresholding segmentation using 2D histogram non-local means and metaheuristics algorithms. In: Oliva, D., Hinojosa, S. (eds.) Applications of Hybrid Metaheuristic Algorithms for Image Processing. SCI, vol. 890, pp. 121–149. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-40977-7_6
Pare, S., Bhandari, A.K., Kumar, A., Singh, G.K.: A new technique for multilevel color image thresholding based on modified fuzzy entropy and Lévy flight firefly algorithm. Comput. Electr. Eng. 70, 476–495 (2018). https://doi.org/10.1016/j.compeleceng.2017.08.008
He, L., Huang, S.: An efficient krill herd algorithm for color image multilevel thresholding segmentation problem. Appl. Soft Comput. 89, 106063 (2020). https://doi.org/10.1016/j.asoc.2020.106063
Dhal, K.G., Das, A., Ray, S., Gálvez, J.: Randomly attracted rough firefly algorithm for histogram based fuzzy image clustering. Knowl. Based Syst. 216, 106814 (2021). https://doi.org/10.1016/j.knosys.2021.106814
Choudhury, A., Samanta, S., Pratihar, S., Bandyopadhyay, O.: Multilevel segmentation of Hippocampus images using global steered quantum inspired firefly algorithm. Appl. Intell. 52(7), 7339–7372 (2021). https://doi.org/10.1007/s10489-021-02688-6
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Choudhury, A., Samanta, S., Pratihar, S., Bandyopadhyay, O. (2023). Color Hippocampus Image Segmentation Using Quantum Inspired Firefly Algorithm and Merging of Channel-Wise Optimums. In: Rojas, I., Valenzuela, O., Rojas Ruiz, F., Herrera, L.J., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2023. Lecture Notes in Computer Science(), vol 13920. Springer, Cham. https://doi.org/10.1007/978-3-031-34960-7_19
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