Flowchart illustration of the working of the algorithm
Computer-aided diagnosis (CAD) systems can automatically sort digitized medical images which can otherwise be a time-consuming process.
Researchers at Rutgers have developed a CAD method to automatically distinguish between tumor and benign regions in digitized prostate histology. The image analysis is performed in a multi-resolution framework, which is similar to the method in which a pathologist would conduct diagnosis.
The structure of interest is first segmented into smaller images to quickly scan and classify lower-resolution images. This allows for rapid elimination of benign pixels and retention of regions classified as tumors. Additionally, a hierarchical multi-scale classifier is used to enable efficient analysis of large digitized images (1-2 GB). This approach can rapidly detect prostate cancer from digitized data such as MRI images.
- Digital pathology
- Medical imaging
- Computer-aided diagnosis
- Image-guided therapy
- Classification of lower-resolution images
- Fully automated and reproducible
- Accurate distinction between tumor and benign pixels
- Analysis of large digitized file sizes
Intellectual Property & Development Status:
Issued US patent. US8280132. Available for licensing and/or research collaboration.
. Doyle, S., Feldman, M., Tomaszewski, J. & Madabhushi, A. (2012). IEEE transactions on bio-med. Engineering. 59, 1205-18.