Un nuevo algoritmo multiumbral para la segmentación de imágenes de resonancia magnética

Bhavna Kaushik Pancholi; Pramodkumar Sevantilal Modi; Nehal Gitesh Chitaliya

doi:10.56294/saludcyt2023408

Autores/as

Bhavna Kaushik Pancholi The Maharaja Sayajirao University of Baroda, Vadodara, Department of Electrical Engineering, Gujarat https://orcid.org/0000-0002-8908-0590
Pramodkumar Sevantilal Modi The Maharaja Sayajirao University of Baroda, Vadodara, Department of Electrical Engineering, Gujarat https://orcid.org/0000-0002-2651-8743
Nehal Gitesh Chitaliya The Maharaja Sayajirao University of Baroda, Vadodara, Department of Electrical Engineering, Gujarat https://orcid.org/0000-0003-3699-2542

DOI:

https://doi.org/10.56294/saludcyt2023408

Palabras clave:

Segmentation, brain magnetic resonance imaging, metaheuristic algorithms, innovation technology

Resumen

La segmentación es una etapa crucial en las técnicas de evaluación de imágenes. La segmentación precisa de imágenes de resonancia magnética cerebral se ha estudiado ampliamente porque el uso de este tipo de métodos permite detectar y reconocer una amplia gama de trastornos. El umbralaje es un método sencillo y eficaz para segmentar imágenes. Pero dependiendo de cuántos umbrales se empleen para la segmentación, las técnicas basadas en el umbral tienden a ser más costosas de calcular. En consecuencia, los algoritmos metaheurísticos son una herramienta crucial para el umbralado multinivel que ayudan a determinar los mejores valores. Utilizando un algoritmo de búsqueda cucú (NCS), hemos sugerido un método más eficiente para segmentar imágenes de resonancia magnética. Se utilizaron tres funciones objetivo diferentes (el método de Otsu, la entropía de Kapur y la función de entropía de Tsallis) comparando el resultado de la estrategia proyectada con el algoritmo de búsqueda del cuco (CS).

Métricas

Cargando métricas ...

Citas

Yang X, Deb S. Cuckoo Search via Levy Flights. In: Proceedings of the World Congress on Nature & Biologically Inspired Computing; 2009. p. 210-214.

Mohamad AB, Zain AM, Erne N, Bazin N. Cuckoo Search Algorithm for Optimization Problems, A Literature Review and its Applications. Appl. Artif. Intell. 2014;28(5):419-448.

Kutzelnigg R, Reinhard C. A further analysis of Cuckoo Hashing with a Stash and Random Graphs of Excess r. Discrete Mathematics And Theoretical Computer Science. 2010;12:81-101.

Walton S, Hassan O, Morgan K, Brown MR. Modified cuckoo search: A new gradient-free optimization algorithm. Interdiscip. J. Nonlinear Sci. Nonequilibrium Complex Phenom. 2011;44(9):710-718.

Vaishya R, Gupta BM, Kappi M, Vaish A. International Orthopaedics journal: A bibliometric analysis during 1977-2022. Iberoamerican Journal of Science Measurement and Communication. 2023;3(1).

Gherboudj A, Layeb A, Chikhi S. Solving 0-1 knapsack problems by a discrete binary version of cuckoo search algorithm. International Journal of Bio-Inspired Computation (IJBIC). 2012;4(4).

Durgun I, Yildiz AR. Structural Design Optimization of Vehicle Components Using Cuckoo Search Algorithm. Carl Hanser Verlag Munich, Germany. 2012;54:185-188.

Valian E, Tavakoli S, Mohanna S, Haghi A. Improved cuckoo search for reliability optimization problems. Comput. Ind. Eng. 2013;64(1):459-468.

Yildiz AR. Cuckoo search algorithm for the selection of optimal machining parameters in milling operations. 2013;55-61.

Ouaarab A, Ahiod B, Yang X. Discrete cuckoo search algorithm for the traveling salesman problem. 2014;1659-1669.

Kumar A, Kumar V, Kumar A, G. Kumar G. Cuckoo search algorithm and wind-driven optimization based study of satellite image segmentation for multilevel thresholding using Kapur's entropy. Expert Syst. Appl. 2014;41(7):3538-3560.

Wang J, Jiang H, Wu Y, Dong Y. Forecasting solar radiation using an optimized hybrid model by Cuckoo Search algorithm. Energy. 2015;81:627-644.

Mohapatra P, Chakravarty S, Dash PK. An improved cuckoo search-based extreme learning machine for medical data classification. Swarm Evol. Comput. 2015;24:25-49.

Thanh T, Viet A, Anh T. A novel method based on adaptive cuckoo search for optimal network reconfiguration and distributed generation allocation in distribution network. Int. J. Electr. Power Energy Syst. 2016;78:801-815.

Sanajaoba S, Fernandez E. Maiden application of Cuckoo Search algorithm for optimal sizing of a remote hybrid renewable energy system. Renew. Energy. 2016;96:1-10.

Simhan L, Basupi G. None Deep Learning Based Analysis of Student Aptitude for Programming at College Freshman Level. Data & Metadata. 2023;2:38.

Pandey AC, Rajpoot DS, Saraswat M. Twitter sentiment analysis using hybrid cuckoo search method. 2017;53:764-779.

Zhu X, Wang N. Splicing process-inspired cuckoo search algorithm based ENNs for modeling FCCU reactor-regenerator system. Chem. Eng. J. 2018.

Kumari S, Pushkar S. Cuckoo search-based hybrid models for improving the accuracy of software effort estimation. Microsyst. Technol. 2018;24(12):4767-4774.

Zhang M, Wang H, Cui Z, Chen J. Hybrid multi-objective cuckoo search with dynamical local search. Memetic Comput. 2018;10(2):199-208.

Tran-ngoc H, Khatir S, De Roeck G, Bui-tien T, Wahab MA. An efficient artificial neural network for damage detection in bridges and beam-like structures by improving training parameters using cuckoo search algorithm. Eng. Struct. 2019;199:109637.

Zhang C, Zeng G, Wang H, Tu X. Hierarchical resource scheduling method using improved cuckoo search algorithm for internet of things. 2019;1606-1614.

Mahato DP. On scheduling transaction in grid computing using cuckoo search-ant colony optimization considering load. Cluster Comput. 2019;0123456789.

Cui Z, Zhang M, Wang H, Cai X, Zhang W, Wang H. A hybrid many-objective cuckoo search algorithm. Soft Comput. 2019;23(21):10681-10697.

Rao T, Mani N, Matta S, Koratana S, Kumar R. A fuzzied Pareto multiobjective cuckoo search algorithm for power losses minimization incorporating SVC. Soft Comput. 2019;23(21):10811-10820.

Chen L, Chen L, Chen L. Dimension-by-dimension enhanced cuckoo search algorithm for global optimization. Soft Comput. 2019;23(21):11297-11312.

Prem Jacob T, Pradeep K. A Multi-objective Optimal Task Scheduling in Cloud Environment Using Cuckoo Particle Swarm Optimization. Wirel. Pers. Commun. 2019;109(1):315-331.

Bala A, Ismail I, Ibrahim R, Sait SM, Onoruoiza H. Prediction Using Cuckoo Search Optimized Echo State Network. Arab. J. Sci. Eng. 2019;44(11):9769-9778.

Cai X, Niu Y, Geng S, Li J, Chen J, Zhang J. An under-sampled software defect prediction method based on hybrid multi-objective cuckoo search. 2019;May:1-14.

Yang X. Cuckoo Search and Firefly Algorithm: Overview and Analysis. Springer International Publishing Switzerland. 2014;1-26.

Rahaman, Jarjish, Sing M. An Efficient Multilevel Thresholding Based Satellite Image Segmentation Approach Using a New Adaptive Cuckoo Search Algorithm. Expert Syst. Appl. 2021;174:114633.

Deng Q, Shi Z, Ou C. Self-Adaptive Image Thresholding within Nonextensive Entropy and the Variance of the Gray-Level Distribution. Entropy. 2022;24:319.