Open Access. Powered by Scholars. Published by Universities.®
Analytical, Diagnostic and Therapeutic Techniques and Equipment Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Discipline
- Keyword
-
- Automatic classification (1)
- Automatic diagnosis (1)
- Automatic indexing (1)
- Biological radiation effects (1)
- Biopsy samples (1)
-
- Block matching (1)
- CAD development (1)
- Classification accuracy (1)
- Collaborative filtering (1)
- Computer aided diagnosis (1)
- Corrupted images (1)
- Cost functions (1)
- Costs (1)
- Cross validation (1)
- Denoising methods (1)
- Digitized images (1)
- Evolutionary algorithms (1)
- Feature selection algorithm (1)
- Gray-level (1)
- Histological images (1)
- Histology (1)
- Histopathological images (1)
- Histopathology (1)
- Image de-noising (1)
- Image denoising methods (1)
- Imaging systems (1)
- MRI image (1)
- Matrix (1)
- Matrix methods (1)
Articles 1 - 2 of 2
Full-Text Articles in Analytical, Diagnostic and Therapeutic Techniques and Equipment
Parameter Optimization For Image Denoising Based On Block Matching And 3d Collaborative Filtering, Ramu Pedada, Emin Kugu, Jiang Li, Zhanfeng Yue, Yuzhong Shen, Josien P.W. Pluim (Ed.), Benoit M. Dawant (Ed.)
Parameter Optimization For Image Denoising Based On Block Matching And 3d Collaborative Filtering, Ramu Pedada, Emin Kugu, Jiang Li, Zhanfeng Yue, Yuzhong Shen, Josien P.W. Pluim (Ed.), Benoit M. Dawant (Ed.)
Electrical & Computer Engineering Faculty Publications
Clinical MRI images are generally corrupted by random noise during acquisition with blurred subtle structure features. Many denoising methods have been proposed to remove noise from corrupted images at the expense of distorted structure features. Therefore, there is always compromise between removing noise and preserving structure information for denoising methods. For a specific denoising method, it is crucial to tune it so that the best tradeoff can be obtained. In this paper, we define several cost functions to assess the quality of noise removal and that of structure information preserved in the denoised image. Strength Pareto Evolutionary Algorithm 2 (SPEA2) …
Automatic Diagnosis For Prostate Cancer Using Run-Length Matrix Method, Xiaoyan Sun, Shao-Hui Chuang, Jiang Li, Frederic Mckenzie, Nico Karssemeijer (Ed.), Maryellen L. Giger (Ed.)
Automatic Diagnosis For Prostate Cancer Using Run-Length Matrix Method, Xiaoyan Sun, Shao-Hui Chuang, Jiang Li, Frederic Mckenzie, Nico Karssemeijer (Ed.), Maryellen L. Giger (Ed.)
Electrical & Computer Engineering Faculty Publications
Prostate cancer is the most common type of cancer and the second leading cause of cancer death among men in US1. Quantitative assessment of prostate histology provides potential automatic classification of prostate lesions and prediction of response to therapy. Traditionally, prostate cancer diagnosis is made by the analysis of prostate-specific antigen (PSA) levels and histopathological images of biopsy samples under microscopes. In this application, we utilize a texture analysis method based on the run-length matrix for identifying tissue abnormalities in prostate histology. A tissue sample was collected from a radical prostatectomy, H&E fixed, and assessed by a pathologist …