Title
Assessing estrogen receptors' status by texture analysis of breast tissue specimens and pattern recognition methods
Abstract
An image analysis system (IAS) was developed for the quantitative assessment of estrogen receptor's (ER) positive status from breast tissue microscopy images. Twenty-four cases of breast cancer biopsies, immunohisto-chemically (IHC) stained for ER, were microscopically assessed by a histopathologist, following a clinical routine scoring protocol. Digitized microscopy views of the specimens were used in the IAS's design. IAS comprised a/image segmentation, for nuclei determination, b/extraction of textural features, by processing of nuclei-images utilizing the Laws and Gabor filters and by calculating textural features from the processed nuclei-images, and c/PNN and SVM classifiers design, for discriminating positively stained nuclei. The proportion of the latter in each case's images was compared against the physician's score. Using Spearman's rank correlation, high correlation was found between the histo-pathogist's and IAS's scores (rho=0.89, p
Year
DOI
Venue
2007
10.1007/978-3-540-74272-2_28
CAIP
Keywords
Field
DocType
breast cancer biopsy,svm classifiers design,pattern recognition method,breast tissue specimen,nuclei determination,processed nuclei-images,texture analysis,estrogen receptor,digitized microscopy view,image analysis system,high correlation,textural feature,breast tissue microscopy image,image segmentation,histopathology,image analysis,pattern recognition,breast cancer,rank correlation
Rank correlation,Computer vision,Breast cancer,Pattern recognition,Computer science,Histopathology,Image segmentation,Correlation,Artificial intelligence,Quantitative assessment,Immunohistochemistry,Estrogen receptor
Conference
Volume
ISSN
Citations 
4673
0302-9743
5
PageRank 
References 
Authors
0.73
5
8