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Recent metaheuristic algorithms for medical object localization using MSER detector in computer-aided diagnosis system

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Abstract

Object localization is a critical task in image analysis, often facilitated by artificial intelligence techniques. While the Maximally Stable Extremal Regions (MSER) detection algorithm is a popular choice for local detection, its exhaustive approaches can be algorithmically complex and prone to suboptimal results with improper parameter selection. Various metaheuristic algorithms have been proposed for medical object localization to address this. In this context, four contributions are presented. Firstly, recent metaheuristics, including the Slime Mould Algorithm (SMA), Marine Predators Algorithm (MPA), Heap-based Optimizer (HBO), and Gradient-based Optimizer (GBO), are adapted to tackle the MSER localization problem. These algorithms are rigorously evaluated across diverse medical image datasets using multiple metrics, with their performance statistically validated through the Friedman mean rank test. The second contribution introduces a novel objective function to improve the localization process. The third contribution involves a comparative analysis of the recent algorithms against seven standard metaheuristics specifically designed for MSER localization. Lastly, we present an improved computer-aided diagnosis system that integrates an SVM-based model with Local Binary Patterns (LBP) descriptors extracted from each MSER alongside DenseNet-121 features. Experimental results demonstrate that the HBO and SMA optimizers outperform conventional algorithms, showing a higher diversity, improved exploitation and exploration capabilities, a well-balanced exploration-exploitation trade-off, elevated fitness values, faster convergence speeds, and enhanced computational efficiency. Additionally, we integrate these findings into an improved computer-aided diagnosis system, yielding classification results surpassing state-of-the-art models. These findings highlight the potential advantages of this approach, especially in object detection applications.

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Data Availability

The present study utilized publicly available datasets, namely ISIC 2016, ISIC 2017, LISC, and Raabin-WBC. These datasets are accessible through their respective sources, and researchers interested in obtaining these datasets can refer to the following: \(\bullet \) ISIC 2016 and ISIC 2017: The ISIC datasets intended for research purposes can be retrieved from the International Skin Imaging Collaboration (ISIC) website at https://challenge.isic-archive.com/, with detailed access and usage guidelines provided on their platform. \(\bullet \) LISC: The LISC dataset is accessible through the official source at http://users.cecs.anu.edu.au/~hrezatofighi/Data/Leukocyte%20Data.htm. Comprehensive information regarding data access and usage terms can be found at the specified source. \(\bullet \) Raabin-WBC: For the Raabin-WBC dataset, access can be obtained from https://raabindata.com/free-data/. For specific data access and usage policies, we recommend referring to the source.

Notes

  1. https://docs.opencv.org/3.4/d3/d28/classcv_1_1MSER.html

References

  1. Abouqora Y, Herouane O, Lahcen M et al (2019) New interest points detector for 3d objects recognition. International Journal of Intelligent Engineering and Systems 12:169–176. https://doi.org/10.22266/ijies2019.0831.16

  2. Ahmadianfar I, Bozorg-Haddad O, Chu X (2020) Gradient-based optimizer: A new metaheuristic optimization algorithm. Inf Sci 540:131–159. https://doi.org/10.1016/j.ins.2020.06.037

    Article  MathSciNet  Google Scholar 

  3. Akyol K, Şen B (2021) Keypoint detectors and texture analysis based comprehensive comparison in different color spaces for automatic detection of the optic disc in retinal fundus images. SN Applied Sciences 3(9):774. https://doi.org/10.1007/s42452-021-04754-7

    Article  Google Scholar 

  4. Al-masni MA, Kim DH, Kim TS (2020) Multiple skin lesions diagnostics via integrated deep convolutional networks for segmentation and classification. Comput Methods Programs Biomed 190:105351. https://doi.org/10.1016/j.cmpb.2020.105351

    Article  Google Scholar 

  5. Ali MU, Kallu KD, Masood H et al (2022) A robust computer-aided automated brain tumor diagnosis approach using pso-relieff optimized gaussian and non-linear feature space. Life 12(12). https://doi.org/10.3390/life12122036

  6. Alorf A (2023) A survey of recently developed metaheuristics and their comparative analysis. Eng Appl Artif Intell 117:105622. https://doi.org/10.1016/j.engappai.2022.105622

    Article  Google Scholar 

  7. Anandhalli M, Tanuja A, Baligar VP et al (2022) Vehicle detection and tracking based on interest points of visual appearance. In: Bhaumik S, Chattopadhyay S, Chattopadhyay T et al (eds) Proceedings of international conference on industrial instrumentation and control. Springer Nature Singapore, Singapore, pp 351–362. https://doi.org/10.1007/978-981-16-7011-4_35

  8. Arun KS, Govindan VK, Kumar SDM (2020) Enhanced bag of visual words representations for content based image retrieval: a comparative study. Artif Intell Rev 53(3):1615–1653. https://doi.org/10.1007/s10462-019-09715-6

    Article  Google Scholar 

  9. Bay H, Ess A, Tuytelaars T et al (2008) Speeded-up robust features (surf). Computer Vision and Image Understanding 110(3):346–359. https://doi.org/10.1016/j.cviu.2007.09.014, similarity Matching in Computer Vision and Multimedia

  10. Belattar K, Adjadj M, Bakir M et al (2022) A comparative study of cnn architectures for melanoma skin cancer classification

  11. Belattar K, Ait Mehdi M, Ridane M et al (2023) An optimized mser using bat algorithm for skin lesion detection. In: Salem M, Merelo JJ, Siarry P, et al (eds) Artificial Intelligence: Theories and Applications. Springer Nature Switzerland, Cham, pp 79–93. https://doi.org/10.1007/978-3-031-28540-0_7

  12. Burger W, Burge MJ (2022) Maximally Stable Extremal Regions (MSER), Springer International Publishing, Cham, pp 765–795. https://doi.org/10.1007/978-3-031-05744-1_26

  13. Calonder M, Lepetit V, Strecha C et al (2010) Brief: Binary robust independent elementary features. In: Daniilidis K, Maragos P, Paragios N (eds) Computer Vision – ECCV 2010. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 778–792. https://doi.org/10.1007/978-3-642-15561-1_56

  14. Chinglemba T, Biswas S, Malakar D et al (2023) Introductory review of swarm intelligence techniques. In: Biswas A, Kalayci CB, Mirjalili S (eds) Advances in swarm intelligence: variations and adaptations for optimization problems. Springer International Publishing, Cham, pp 15–35, https://doi.org/10.1007/978-3-031-09835-2_2

  15. Codella NCF, Gutman D, Celebi ME et al (2018) Skin lesion analysis toward melanoma detection: A challenge at the 2017 international symposium on biomedical imaging (isbi), hosted by the international skin imaging collaboration (isic). ar**v:1710.05006

  16. Davis JE, Bednar AE, Goodin CT (2019) Optimizing maximally stable extremal regions (mser) parameters using the particle swarm optimization algorithm. Tech. rep., Engineer Research and Development Center, https://apps.dtic.mil/sti/pdfs/AD1080875.pdf

  17. Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: MHS’95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, pp 39–43. https://doi.org/10.1109/MHS.1995.494215

  18. Faramarzi A, Heidarinejad M, Mirjalili S et al (2020) Marine predators algorithm: A nature-inspired metaheuristic. Expert Syst Appl 152:113377. https://doi.org/10.1016/j.eswa.2020.113377

    Article  Google Scholar 

  19. Fu HL, Mueller JL, Whitley MJ et al (2016) Structured illumination microscopy and a quantitative image analysis for the detection of positive margins in a pre-clinical genetically engineered mouse model of sarcoma. PLoS ONE 11(1):1–19. https://doi.org/10.1371/journal.pone.0147006

    Article  Google Scholar 

  20. Gutman D, Codella NCF, Celebi E et al (2016) Skin lesion analysis toward melanoma detection: A challenge at the international symposium on biomedical imaging (isbi) 2016, hosted by the international skin imaging collaboration (isic). ar**v:1605.01397

  21. Harris C, Stephens M (1988) A combined corner and edge detector. In: Proceedings of the Alvey Vision Conference. Alvety Vision Club, pp 23.1–23.6. https://doi.org/10.5244/C.2.23

  22. He K, Zhang X, Ren S et al (2015) Deep residual learning for image recognition. ar**v:1512.03385

  23. Ho TKK, Gwak J (2022) Feature-level ensemble approach for covid-19 detection using chest x-ray images. PLoS ONE 17(7):1–19. https://doi.org/10.1371/journal.pone.0268430

    Article  Google Scholar 

  24. Holland JH (1992) Genetic algorithms. Sci Am 267(1):66–73. http://www.jstor.org/stable/24939139

  25. Huang G, Liu Z, van der Maaten L et al (2018) Densely connected convolutional networks. ar**v:1608.06993

  26. Hussain K, Salleh MNM, Cheng S et al (2019) On the exploration and exploitation in popular swarm-based metaheuristic algorithms. Neural Comput Appl 31(11):7665–7683. https://doi.org/10.1007/s00521-018-3592-0

    Article  Google Scholar 

  27. Joshi K, Patel MI (2020) Recent advances in local feature detector and descriptor: a literature survey. Int J Multimed Inform Retrieval 9(4):231–247. https://doi.org/10.1007/s13735-020-00200-3

  28. Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical Report, Erciyes University, Tech. rep

    Google Scholar 

  29. Khan SA, Gulzar Y, Turaev S et al (2021) A modified hsift descriptor for medical image classification of anatomy objects. Symmetry 13(11). https://doi.org/10.3390/sym13111987

  30. Kim HE, Cosa-Linan A, Santhanam N et al (2022) Transfer learning for medical image classification: a literature review. BMC Med Imaging 22(1):69. https://doi.org/10.1186/s12880-022-00793-7

    Article  Google Scholar 

  31. Kouzehkanan ZM, Saghari S, Tavakoli S et al (2022) A large dataset of white blood cells containing cell locations and types, along with segmented nuclei and cytoplasm. Sci Rep 12(1):1123. https://doi.org/10.1038/s41598-021-04426-x

    Article  Google Scholar 

  32. Lecron F, Benjelloun M, Mahmoudi S (2012) Descriptive image feature for object detection in medical images. In: Campilho A, Kamel M (eds) Image Analysis and Recognition. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 331–338. https://doi.org/10.1007/978-3-642-31298-4_39

  33. Li S, Chen H, Wang M et al (2020) Slime mould algorithm: A new method for stochastic optimization. Futur Gener Comput Syst 111:300–323. https://doi.org/10.1016/j.future.2020.03.055

    Article  Google Scholar 

  34. Li W, Dong P, **ao B et al (2016) Object recognition based on the region of interest and optimal bag of words model. Neurocomputing 172:271–280. https://doi.org/10.1016/j.neucom.2015.01.083

    Article  Google Scholar 

  35. Li Y, Shen L (2018) Skin lesion analysis towards melanoma detection using deep learning network. Sensors 18(2). https://doi.org/10.3390/s18020556

  36. Lowe D (1999) Object recognition from local scale-invariant features. In: Proceedings of the Seventh IEEE International Conference on Computer Vision, pp 1150–1157 vol.2. https://doi.org/10.1109/ICCV.1999.790410

  37. Łukasik S, Żak S (2009) Firefly algorithm for continuous constrained optimization tasks. In: Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 97–106. https://doi.org/10.1007/978-3-642-04441-0_8

  38. Ma J, Jiang X, Fan A et al (2021a) Image matching from handcrafted to deep features: A survey. Int J Comput Vis 129(1):23–79. https://doi.org/10.1007/s11263-020-01359-2

  39. Ma J, Jiang X, Fan A et al (2021b) Image matching from handcrafted to deep features: A survey. Int J Comput Vis 129(1):23–79. https://doi.org/10.1007/s11263-020-01359-2

  40. Matas J, Chum O, Urban M et al (2004) Robust wide-baseline stereo from maximally stable extremal regions. Image Vis Comput 22(10):761–767. https://doi.org/10.1016/j.imavis.2004.02.006, british Machine Vision Computing 2002

  41. Mikolajczyk K, Schmid C (2002) An affine invariant interest point detector. In: Heyden A, Sparr G, Nielsen M, et al (eds) Computer Vision — ECCV 2002. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 128–142.https://doi.org/10.1007/3-540-47969-4_9

  42. Moradi M, Abolmaesoumi P, Mousavi P (2006) Deformable registration using scale space keypoints. In: Reinhardt JM, Pluim JPW (eds) Medical Imaging 2006: Image Processing, International Society for Optics and Photonics, vol 6144. SPIE, p 61442G. https://doi.org/10.1117/12.652132

  43. Muñoz-Salinas R, Medina-Carnicer R (2020) Ucoslam: Simultaneous localization and map** by fusion of keypoints and squared planar markers. Pattern Recogn 101:107193. https://doi.org/10.1016/j.patcog.2019.107193

    Article  Google Scholar 

  44. Ngoc VTN, Agwu AC, Son LH et al (2020) The combination of adaptive convolutional neural network and bag of visual words in automatic diagnosis of third molar complications on dental x-ray images. Diagnostics (Basel) 10(4). https://doi.org/10.3390/diagnostics10040209

  45. Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987. https://doi.org/10.1109/TPAMI.2002.1017623

    Article  Google Scholar 

  46. Rezatofighi SH, Soltanian-Zadeh H (2011) Automatic recognition of five types of white blood cells in peripheral blood. Comput Med Imaging Graph 35(4):333–343. https://doi.org/10.1016/j.compmedimag.2011.01.003

    Article  Google Scholar 

  47. Rosten E, Drummond T (2006) Machine learning for high-speed corner detection. In: Leonardis A, Bischof H, Pinz A (eds) Computer vision – ECCV 2006. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 430–443. https://doi.org/10.1007/11744023_34

  48. Rublee E, Rabaud V, Konolige K. et al (2011) Orb: An efficient alternative to sift or surf. In: 2011 International Conference on Computer Vision, pp 2564–2571. https://doi.org/10.1109/ICCV.2011.6126544

  49. Sabrina AB, Feryel S, Khadidja B (2020) High-level image representation-based on gestalt theory for image classification. In: 2020 1st International conference on communications, control systems and signal processing (CCSSP), pp 186–192. https://doi.org/10.1109/CCSSP49278.2020.9151715

  50. Sargent D, Chen CI, Tsai CM et al (2009) Feature detector and descriptor for medical images. In: Medical imaging 2009: image processing, pp 991–998. https://doi.org/10.1117/12.811210

  51. Sitaula C, Aryal S (2021) New bag of deep visual words based features to classify chest x-ray images for COVID-19 diagnosis. Health Inform Sci Syst 9(1):24. https://doi.org/10.1007/s13755-021-00152-w

  52. Smith SM, Brady JM (1997) Susan–a new approach to low level image processing. Int J Comput Vision 23(1):45–78. https://doi.org/10.1023/A:1007963824710

    Article  Google Scholar 

  53. Storn R, Price K (1997) Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11(4):341–359. https://doi.org/10.1023/A:1008202821328

    Article  MathSciNet  Google Scholar 

  54. Tavakoli S, Ghaffari A, Kouzehkanan ZM et al (2021) New segmentation and feature extraction algorithm for classification of white blood cells in peripheral smear images. Sci Rep 11(1):19428. https://doi.org/10.1038/s41598-021-98599-0

    Article  Google Scholar 

  55. Tuyet VTH, Binh NT, Quoc NK et al (2021) Content based medical image retrieval based on salient regions combined with deep learning. Mob Netw Appl 26(3):1300–1310. https://doi.org/10.1007/s11036-021-01762-0

    Article  Google Scholar 

  56. Tuytelaars T, Mikolajczyk K (2008) Local invariant feature detectors: A survey. Found Trends® Comput Graph Vis 3(3):177–280. https://doi.org/10.1561/0600000017

  57. Utomo A, Juniawan EF, Lioe V et al (2021) Local features based deep learning for mammographic image classification: In comparison to cnn models. Procedia Comput Sci 179:169–176. https://doi.org/10.1016/j.procs.2020.12.022, 5th International Conference on Computer Science and Computational Intelligence 2020

  58. Van Thieu N, Mirjalili S (2023) Mealpy: An open-source library for latest meta-heuristic algorithms in python. J Syst Architect. https://doi.org/10.1016/j.sysarc.2023.102871

    Article  Google Scholar 

  59. Vijh S, Kumar S, Saraswat M (2022) New bag-of-feature for histopathology image classification using reinforced cat swarm algorithm and weighted gaussian mixture modelling. Complex Intell Syst 8(6):5027–5046. https://doi.org/10.1007/s40747-022-00726-5

    Article  Google Scholar 

  60. Xu H, **e S, Chen F (2020) Fast mser. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 3377–3386. https://doi.org/10.1109/CVPR42600.2020.00344

  61. Yang M, Yuan Y, Li X et al (2011) Medical image segmentation using descriptive image features. In: British machine vision conference, pp 1–11. https://projet.liris.cnrs.fr/imagine/pub/proceedings/BMVC-2011/Paper_244/2011_BMVC_abstract_MeijuanYang_final.pdf

  62. Yang XS (2010) A new metaheuristic bat-inspired algorithm. In: González JR, Pelta DA, Cruz C et al (eds) Nature inspired cooperative strategies for optimization (NICSO 2010). Springer Berlin Heidelberg, p 65–74. https://doi.org/10.1007/978-3-642-12538-6_6

  63. Yang XS, Deb S (2009) Cuckoo search via lévy flights. In: 2009 World congress on nature & biologically inspired computing (NaBIC), pp 210–214. https://doi.org/10.1109/NABIC.2009.5393690

  64. Yang Y, Zhang L, Du M et al (2021) A comparative analysis of eleven neural networks architectures for small datasets of lung images of covid-19 patients toward improved clinical decisions. Comput Biol Med 139:104887. https://doi.org/10.1016/j.compbiomed.2021.104887

    Article  Google Scholar 

  65. Zaman A, Yangyu F, Irfan M et al (2022) Lifelongglue: Keypoint matching for 3d reconstruction with continual neural networks. Expert Syst Appl 195:116613. https://doi.org/10.1016/j.eswa.2022.116613

    Article  Google Scholar 

  66. Zhang J, **e Y, Wu Q et al (2019) Medical image classification using synergic deep learning. Med Image Anal 54:10–19. https://doi.org/10.1016/j.media.2019.02.010

    Article  Google Scholar 

  67. Zheng X, Zhou M, Wang X (2008) Interest point based medical image retrieval. In: Gao X, Müller H, Loomes MJ, et al (eds) Medical imaging and informatics. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 118–124. https://doi.org/10.1007/978-3-540-79490-5_16

  68. Zhi Lj, Zhang Sm, Zhao Dz et al (2009) Medical image retrieval using sift feature. In: 2009 2nd International congress on image and signal processing, pp 1–4. https://doi.org/10.1109/CISP.2009.5304112

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  1. Faculty of Computer Science, Data Science and Artificial Intelligence Department, Laboratory for Research in Artificial Intelligence, University of Science and Technology Houari Boumediene, BP 32 El-Alia, Bab-Ezzouar, 16111, Algiers, Algeria

    Mohamed Ait Mehdi & Feryel Souami

  2. Department of Computer Science, University of Algiers Benyoucef Benkhedda, 16000, Algiers, Algeria

    Khadidja Belattar & Feryel Souami

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Ait Mehdi, M., Belattar, K. & Souami, F. Recent metaheuristic algorithms for medical object localization using MSER detector in computer-aided diagnosis system. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-19606-w

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