Characterization of uterine-cervix phantoms' elasticity using texture features extracted from US images
Por:
Orozco Flores, Monica, Perez-Gonzalez, Jorge, Torres Robles, Fabian, Garcia Segundo, Crescencio, Prieto Rodriguez, Scarlet, Camargo Marin, Lisbeth, Guzman Huerta, Mario, Medina-Banuelos, Veronica
Publicada:
1 ene 2018
Resumen:
An indirect method of tissue consistency measurement is proposed, based on intensity and texture features of conventional ultrasound (US) cervix images. Calibration and validation were carried out in five phantoms simulating different cervical firmness, as well as in short and long cervices. Several image features attributed to the histogram, the co-occurrence matrix and the run-length encoding matrix were extracted and analyzed to evaluate their ability to distinguish between degrees of phantoms' firmness. The most indicative of firmness indices were selected by correlating their values with the phantoms' elasticities determined through Young's moduli. Also, a random forest classifier was implemented, allowing to identify the features that contribute the most to class separation between phantoms. Using both tests, six features were selected: Mean, standard deviation, entropy, skewness and two RLE-matrix features. A 6-fold cross validation was used to evaluate the model, obtaining a 98.9±0.79% accuracy. Finally, a preliminary case study was conducted upon closed and opened cervical US images, classifying them between both groups using a random forest model, obtaining an 84.34% accuracy. The indicated tests show that intensity and texture features extracted from conventional US images provide indirect and less-invasive information than other methods regarding tissue consistency, and therefore may be used to measure changes in cervical firmness. © SPIE. Downloading of the abstract is permitted for personal use only.
Filiaciones:
Orozco Flores, Monica:
Neuroimaging Laboratory, Electrical Engineering Department, Universidad Autonoma Metropolitana Iztapalapa, Ciudad de Mexico, Mexico
Univ Autonoma Metropolitana Iztapalapa, Elect Engn Dept, Neuroimaging Lab, Mexico City, DF, Mexico
Perez-Gonzalez, Jorge:
Neuroimaging Laboratory, Electrical Engineering Department, Universidad Autonoma Metropolitana Iztapalapa, Ciudad de Mexico, Mexico
Biomechatronics Laboratory, Tecnologico de Monterrey Campus Guadalajara, Mexico
Univ Autonoma Metropolitana Iztapalapa, Elect Engn Dept, Neuroimaging Lab, Mexico City, DF, Mexico
Tecnol Monterrey Campus Guadalajara, Biomechatron Lab, Mexico City, DF, Mexico
Torres Robles, Fabian:
Universidad Nacional Autonoma de Mexico, Ciudad de Mexico DDepartment of Fetal Medicine, Instituto Nacional de Perinatologa, Ciudad de Mexico, Mexico
Univ Nacl Autonoma Mexico, Mexico City, DF, Mexico
Garcia Segundo, Crescencio:
Universidad Nacional Autonoma de Mexico, Ciudad de Mexico DDepartment of Fetal Medicine, Instituto Nacional de Perinatologa, Ciudad de Mexico, Mexico
Univ Nacl Autonoma Mexico, Mexico City, DF, Mexico
Prieto Rodriguez, Scarlet:
Neuroimaging Laboratory, Electrical Engineering Department, Universidad Autonoma Metropolitana Iztapalapa, Ciudad de Mexico, Mexico
Inst Nacl Perinatol, Dept Fetal Med, Mexico City, DF, Mexico
Camargo Marin, Lisbeth:
Neuroimaging Laboratory, Electrical Engineering Department, Universidad Autonoma Metropolitana Iztapalapa, Ciudad de Mexico, Mexico
Inst Nacl Perinatol, Dept Fetal Med, Mexico City, DF, Mexico
Guzman Huerta, Mario:
Neuroimaging Laboratory, Electrical Engineering Department, Universidad Autonoma Metropolitana Iztapalapa, Ciudad de Mexico, Mexico
Inst Nacl Perinatol, Dept Fetal Med, Mexico City, DF, Mexico
Medina-Banuelos, Veronica:
Neuroimaging Laboratory, Electrical Engineering Department, Universidad Autonoma Metropolitana Iztapalapa, Ciudad de Mexico, Mexico
Univ Autonoma Metropolitana Iztapalapa, Elect Engn Dept, Neuroimaging Lab, Mexico City, DF, Mexico
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