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Curvelet-based classification of prostate cancer histological images of critical Gleason scores
- Source :
- ISBI
- Publication Year :
- 2015
- Publisher :
- IEEE, 2015.
-
Abstract
- This paper is aimed at the development of an approach of applying the curvelet transform to images of prostatectomy pathological specimens of critical Gleason grades for computer-aided classification. A set of Tissue MicroArray (TMA) images from the Johns Hopkins University have been used as the data base. We utilize a moving window to sample multiple patches of a given image leading to a majority decision by the patches for image class assignment. The curvelet-based feature extraction may capture both textural and, implicitly, structural information in an image patch. A tree-structured classifier consisting of three Gaussian-kernel support vector machines each with an embedded voting mechanism has been successfully trained and tested yielding high accuracy to classify tissue images of four critical Gleason scores (GS) 3+3, 3+4, 4+3 and 4+4. The experimental result has demonstrated an enhanced performance as compared to other reported works.
- Subjects :
- Tissue microarray
Contextual image classification
Computer science
business.industry
Prostatectomy
medicine.medical_treatment
Feature extraction
Cancer
Pattern recognition
medicine.disease
Support vector machine
Prostate cancer
Image texture
medicine
Curvelet
Computer vision
Artificial intelligence
business
Classifier (UML)
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI)
- Accession number :
- edsair.doi...........8e2b7a080ee7e78f3d6c547495a90880