Semiautomated segmentation of bone marrow biopsies images based on texture features and Generalized Regression Neural Networks

This work presents preliminary results of a method for semi-automatic detection of fat and hematopoietic cells as well as trabecular surfaces in bone marrow biopsies, in order to calculate the percentage of each type of tissue or cell area in relation to the whole area. Experimental results using s...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autores principales: Meschino, Gustavo, Moler, Emilce Graciela, Passoni, Lucía Isabel
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2004
Materias:
Ciencias Informáticas
Digital Image Processing
Segmentation
Texture
Classification
Generalized Regression
Neural Networks
Visual
COMPUTER GRAPHICS
Neural nets
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/22371
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-22371
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
Digital Image Processing
Segmentation
Texture
Classification
Generalized Regression
Neural Networks
Visual
COMPUTER GRAPHICS
Neural nets
spellingShingle Ciencias Informáticas
Digital Image Processing
Segmentation
Texture
Classification
Generalized Regression
Neural Networks
Visual
COMPUTER GRAPHICS
Neural nets
Meschino, Gustavo
Moler, Emilce Graciela
Passoni, Lucía Isabel
Semiautomated segmentation of bone marrow biopsies images based on texture features and Generalized Regression Neural Networks
topic_facet Ciencias Informáticas
Digital Image Processing
Segmentation
Texture
Classification
Generalized Regression
Neural Networks
Visual
COMPUTER GRAPHICS
Neural nets
description This work presents preliminary results of a method for semi-automatic detection of fat and hematopoietic cells as well as trabecular surfaces in bone marrow biopsies, in order to calculate the percentage of each type of tissue or cell area in relation to the whole area. Experimental results using selected clinical cases are presented. Twenty six biopsies were used, presenting varied distributions of cellularity and trabeculae topography. The approach is based on Digital Image Processing techniques and a Neural Network used for classification using textural features obtained from biopsies images. Results were improved with Mathematical Morphology filters. The algorithm produces highly satisfactory results. The method was shown to be faster and more reproducible than conventional ones, like region growing, edge detection, split and merging. The results from this computer-assisted technique are compared to others obtained by visual inspection by two expert pathologists, and differences of less than 9 % are observed.
format Objeto de conferencia
Objeto de conferencia
author Meschino, Gustavo
Moler, Emilce Graciela
Passoni, Lucía Isabel
author_facet Meschino, Gustavo
Moler, Emilce Graciela
Passoni, Lucía Isabel
author_sort Meschino, Gustavo
title Semiautomated segmentation of bone marrow biopsies images based on texture features and Generalized Regression Neural Networks
title_short Semiautomated segmentation of bone marrow biopsies images based on texture features and Generalized Regression Neural Networks
title_full Semiautomated segmentation of bone marrow biopsies images based on texture features and Generalized Regression Neural Networks
title_fullStr Semiautomated segmentation of bone marrow biopsies images based on texture features and Generalized Regression Neural Networks
title_full_unstemmed Semiautomated segmentation of bone marrow biopsies images based on texture features and Generalized Regression Neural Networks
title_sort semiautomated segmentation of bone marrow biopsies images based on texture features and generalized regression neural networks
publishDate 2004
url http://sedici.unlp.edu.ar/handle/10915/22371
work_keys_str_mv AT meschinogustavo semiautomatedsegmentationofbonemarrowbiopsiesimagesbasedontexturefeaturesandgeneralizedregressionneuralnetworks
AT moleremilcegraciela semiautomatedsegmentationofbonemarrowbiopsiesimagesbasedontexturefeaturesandgeneralizedregressionneuralnetworks
AT passoniluciaisabel semiautomatedsegmentationofbonemarrowbiopsiesimagesbasedontexturefeaturesandgeneralizedregressionneuralnetworks
bdutipo_str Repositorios
_version_ 1764820465640013825

Ejemplares similares