Distinguishing of different tissue types using K-Means clustering of color segmentation
Millions of lives might be saved if stained tissues could be detected quickly. Image classification algorithms may be used to detect the shape of cancerous cells, which is crucial in determining the severity of the disease. With the rapid advancement of digital technology, digital images now play a...
Guardado en:
Autores principales: | Zinah R. Hussein, Ans Ibrahim Mahameed, Jawaher Abdulwahab Fadhil |
---|---|
Formato: | article |
Lenguaje: | EN RU UK |
Publicado: |
PC Technology Center
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/32fecde6d2304fd2a4b6cceb6d786ca0 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Semi-Automated Ground Truth Segmentation and Phenotyping of Plant Structures Using k-Means Clustering of Eigen-Colors (kmSeg)
por: Michael Henke, et al.
Publicado: (2021) -
Cover & Preface JOSI 20(1) 2021
por: Hilma Raimona Zadry
Publicado: (2021) -
Determinants of E-commerce Adoption on Business Performance: A Study of MSMEs in Malang City, Indonesia
por: Abu Muna Almaududi Ausat, et al.
Publicado: (2021) -
Color-Based Segmentation vs. Stereology: A Simple Comparison Between Two Semi-Automated Methods of Image Analysis for the Quantification of Collagen
por: Salinas,Paulo, et al.
Publicado: (2018) -
Performance Analysis in the Segmentation of urban asphalted roads in RGB satellite images using K-Means++ and SegNet
por: João Batista Pacheco Junior, et al.
Publicado: (2021)