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A Boosting Cascade for Automated Detection of Prostate Cancer from Digitized Histology
- Source :
- Medical Image Computing and Computer-Assisted Intervention – MICCAI 2006 ISBN: 9783540447276, MICCAI (2)
- Publication Year :
- 2006
- Publisher :
- Springer Berlin Heidelberg, 2006.
-
Abstract
- Current diagnosis of prostatic adenocarcinoma is done by manual analysis of biopsy tissue samples for tumor presence. However, the recent advent of whole slide digital scanners has made histopathological tissue specimens amenable to computer-aided diagnosis (CAD). In this paper, we present a CAD system to assist pathologists by automatically detecting prostate cancer from digitized images of prostate histological specimens. Automated diagnosis on very large high resolution images is done via a multi-resolution scheme similar to the manner in which a pathologist isolates regions of interest on a glass slide. Nearly 600 image texture features are extracted and used to perform pixel-wise Bayesian classification at each image scale to obtain corresponding likelihood scenes. Starting at the lowest scale, we apply the AdaBoost algorithm to combine the most discriminating features, and we analyze only pixels with a high combined probability of malignancy at subsequent higher scales. The system was evaluated on 22 studies by comparing the CAD result to a pathologist’s manual segmentation of cancer (which served as ground truth) and found to have an overall accuracy of 88%. Our results show that (1) CAD detection sensitivity remains consistently high across image scales while CAD specificity increases with higher scales, (2) the method is robust to choice of training samples, and (3) the multi-scale cascaded approach results in significant savings in computational time.
- Subjects :
- Ground truth
Boosting (machine learning)
Pixel
medicine.diagnostic_test
Computer science
Prostatic adenocarcinoma
business.industry
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Cancer
Histology
medicine.disease
Malignancy
Prostate cancer
medicine.anatomical_structure
Image texture
Prostate
Glass slide
Biopsy
medicine
Computer vision
Artificial intelligence
business
Subjects
Details
- ISBN :
- 978-3-540-44727-6
- ISBNs :
- 9783540447276
- Database :
- OpenAIRE
- Journal :
- Medical Image Computing and Computer-Assisted Intervention – MICCAI 2006 ISBN: 9783540447276, MICCAI (2)
- Accession number :
- edsair.doi...........63850668e7751f9eaf1210afb359c291