dor_id: 4110159

506.#.#.a: Público

590.#.#.d: Los artículos enviados a la revista "Journal of Applied Research and Technology", se juzgan por medio de un proceso de revisión por pares

510.0.#.a: Scopus, Directory of Open Access Journals (DOAJ); Sistema Regional de Información en Línea para Revistas Científicas de América Latina, el Caribe, España y Portugal (Latindex); Indice de Revistas Latinoamericanas en Ciencias (Periódica); La Red de Revistas Científicas de América Latina y el Caribe, España y Portugal (Redalyc); Consejo Nacional de Ciencia y Tecnología (CONACyT); Google Scholar Citation

561.#.#.u: https://www.icat.unam.mx/

650.#.4.x: Ingenierías

336.#.#.b: article

336.#.#.3: Artículo de Investigación

336.#.#.a: Artículo

351.#.#.6: https://jart.icat.unam.mx/index.php/jart

351.#.#.b: Journal of Applied Research and Technology

351.#.#.a: Artículos

harvesting_group: RevistasUNAM

270.1.#.p: Revistas UNAM. Dirección General de Publicaciones y Fomento Editorial, UNAM en revistas@unam.mx

590.#.#.c: Open Journal Systems (OJS)

270.#.#.d: MX

270.1.#.d: México

590.#.#.b: Concentrador

883.#.#.u: https://revistas.unam.mx/catalogo/

883.#.#.a: Revistas UNAM

590.#.#.a: Coordinación de Difusión Cultural

883.#.#.1: https://www.publicaciones.unam.mx/

883.#.#.q: Dirección General de Publicaciones y Fomento Editorial

850.#.#.a: Universidad Nacional Autónoma de México

856.4.0.u: https://jart.icat.unam.mx/index.php/jart/article/view/706/676

100.1.#.a: Maheswari, K. Uma; Sathiyamoorthy, S.

524.#.#.a: Maheswari, K. Uma, et al. (2018). Fixed grid wavelet network segmentation on diffuse optical tomography image to detect sarcoma. Journal of Applied Research and Technology; Vol. 16 Núm. 2. Recuperado de https://repositorio.unam.mx/contenidos/4110159

245.1.0.a: Fixed grid wavelet network segmentation on diffuse optical tomography image to detect sarcoma

502.#.#.c: Universidad Nacional Autónoma de México

561.1.#.a: Instituto de Ciencias Aplicadas y Tecnología, UNAM

264.#.0.c: 2018

264.#.1.c: 2019-06-20

653.#.#.a: Diffuse Optical Tomography; Fixed Grid Wavelet Network; Orthogonal Least Square Algorithm; Vignette Correction

506.1.#.a: La titularidad de los derechos patrimoniales de esta obra pertenece a las instituciones editoras. Su uso se rige por una licencia Creative Commons BY-NC-SA 4.0 Internacional, https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode.es, para un uso diferente consultar al responsable jurídico del repositorio por medio del correo electrónico gabriel.ascanio@icat.unam.mx

884.#.#.k: https://jart.icat.unam.mx/index.php/jart/article/view/706

001.#.#.#: 074.oai:ojs2.localhost:article/706

041.#.7.h: eng

520.3.#.a: Objective To detect and explore the boundary of the sarcoma in Diffuse Optical Tomography (DOT) images, we need to extract the scattering and absorption property of the tissue at the cellular level. The DOT images suffer with lower optical resolution; therefore to improve the resolution in non-invasive imaging technique we apply Fixed Grid Wavelet Network (FGWN) image segmentation. Methods We have subjected the reconstructed optical image to Vignette Correction to enhance the corners so that it traces the smooth boundary of tumor region. Fixed Grid Wavelet Network segmentation applied to reduce the training with the significant ortho-normal property. R, G and B values of optical image were considered as network inputs which lead to the formation of Wavelet network. Effective wavelet selection was based on Orthogonal Least Squares Algorithm and the network weights were calculated to optimize the network structure. The Mexican hat wavelet chosen facilitates the diffusion operator for image restoration, hence well-suited for Diffuse Optical Tomography (DOT) images.Results Analysis made on data base of 30 DOT images and the 6 criteria results was evaluated. The boundary of the tumor region was traced on grayscale and the following Image Metrics were measured namely Mean Square Error, Root Mean Square Error, Peak Signal to Noise Ratio, Pearson Correlation Coefficient and Mean absolute error. The Receiver Operating Characteristics (ROC) was estimated at 99.527%, 88.73% and 93.8% with respect to sensitivity, specificity and overall accuracy. Conclusions FGWN was compared with genetic algorithm and graph cut segmentation based on image metrics which exhibited 5.2% improvement and it was evaluated such that FGWN based image segmentation was superior to other methodologies.

773.1.#.t: Journal of Applied Research and Technology; Vol. 16 Núm. 2

773.1.#.o: https://jart.icat.unam.mx/index.php/jart

022.#.#.a: ISSN electrónico: 2448-6736; ISSN: 1665-6423

310.#.#.a: Bimestral

264.#.1.b: Instituto de Ciencias Aplicadas y Tecnología, UNAM

doi: https://doi.org/10.22201/icat.16656423.2018.16.2.706

harvesting_date: 2023-11-08 13:10:00.0

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