Journal of Pathology Informatics Journal of Pathology Informatics
Contact us | Home | Login   |  Users Online: 248  Print this pageEmail this pageSmall font sizeDefault font sizeIncrease font size 


RESEARCH ARTICLE
Year : 2011  |  Volume : 2  |  Issue : 1  |  Page : 33

Computer-aided identification of prostatic adenocarcinoma: Segmentation of glandular structures


1 Department of Radiology, The University of Chicago, Chicago, IL 60637, USA
2 Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
3 Pritzker School of Medicine, The University of Chicago, Chicago, IL 60637, USA
4 Department of Pathology, The University of Chicago, Chicago, IL 60637, USA

Correspondence Address:
Yahui Peng
Department of Radiology, The University of Chicago, Chicago, IL 60637
USA
Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/2153-3539.83193

Rights and Permissions

Background: Identification of individual prostatic glandular structures is an important prerequisite to quantitative histological analysis of prostate cancer with the aid of a computer. We have developed a computer method to segment individual glandular units and to extract quantitative image features, for computer identification of prostatic adenocarcinoma. Methods: Two sets of digital histology images were used: database I (n = 57) for developing and testing the computer technique, and database II (n = 116) for independent validation. The segmentation technique was based on a k-means clustering and a region-growing method. Computer segmentation results were evaluated subjectively and also compared quantitatively against manual gland outlines, using the Jaccard similarity measure. Quantitative features that were extracted from the computer segmentation results include average gland size, spatial gland density, and average gland circularity. Linear discriminant analysis (LDA) was used to combine quantitative image features. Classification performance was evaluated with receiver operating characteristic (ROC) analysis and the area under the ROC curve (AUC). Results: Jaccard similarity coefficients between computer segmentation and manual outlines of individual glands were between 0.63 and 0.72 for non-cancer and between 0.48 and 0.54 for malignant glands, respectively, similar to an interobserver agreement of 0.79 for non-cancer and 0.75 for malignant glands, respectively. The AUC value for the features of average gland size and gland density combined via LDA was 0.91 for database I and 0.96 for database II. Conclusions: Using a computer, we are able to delineate individual prostatic glands automatically and identify prostatic adenocarcinoma accurately, based on the quantitative image features extracted from computer-segmented glandular structures.


[FULL TEXT] [PDF]*
Print this article     Email this article
 Next article
 Previous article
 Table of Contents

 Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
 Citation Manager
 Access Statistics
 Reader Comments
 Email Alert *
 Add to My List *
 * Requires registration (Free)
 

 Article Access Statistics
    Viewed5108    
    Printed235    
    Emailed1    
    PDF Downloaded689    
    Comments [Add]    
    Cited by others 7    

Recommend this journal