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Biomedical Engineering and Bioengineering Commons™
Open Access. Powered by Scholars. Published by Universities.®
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- BCC skin cancer (1)
- Basal cell carcinoma (1)
- Brain (1)
- Computer aided analysis (1)
- Computer aided diagnosis (1)
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- Data modeling (1)
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- Dermatology (1)
- Diffusion tensor imaging (1)
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- Feature selection algorithm (1)
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- Geometrical optics (1)
- Gray level co-occurrence matrix (1)
- Gray level run length matrix (1)
- Haralick texture features (1)
- Image registration (1)
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- Matrix (1)
- Medical imaging (1)
- Multi layer perceptron (1)
- Neuroimaging (1)
- Optical image (1)
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- Sensitivity and specificity (1)
- Skin cancers (1)
Articles 1 - 2 of 2
Full-Text Articles in Biomedical Engineering and Bioengineering
Bcc Skin Cancer Diagnosis Based On Texture Analysis Techniques, Shao-Hui Chuang, Xiaoyan Sun, Wen-Yu Chang, Gwo-Shing Chen, Adam Huang, Jiang Li, Frederic D. Mckenzie
Bcc Skin Cancer Diagnosis Based On Texture Analysis Techniques, Shao-Hui Chuang, Xiaoyan Sun, Wen-Yu Chang, Gwo-Shing Chen, Adam Huang, Jiang Li, Frederic D. Mckenzie
Electrical & Computer Engineering Faculty Publications
In this paper, we present a texture analysis based method for diagnosing the Basal Cell Carcinoma (BCC) skin cancer using optical images taken from the suspicious skin regions. We first extracted the Run Length Matrix and Haralick texture features from the images and used a feature selection algorithm to identify the most effective feature set for the diagnosis. We then utilized a Multi-Layer Perceptron (MLP) classifier to classify the images to BCC or normal cases. Experiments showed that detecting BCC cancer based on optical images is feasible. The best sensitivity and specificity we achieved on our data set were 94% …
Prediction Of Brain Tumor Progression Using Multiple Histogram Matched Mri Scans, Debrup Banerjee, Loc Tran, Jiang Li, Yuzhong Shen, Frederic Mckenzie, Jihong Wang, Ronald M. Summers (Ed.), Bram Van Ginneken (Ed.)
Prediction Of Brain Tumor Progression Using Multiple Histogram Matched Mri Scans, Debrup Banerjee, Loc Tran, Jiang Li, Yuzhong Shen, Frederic Mckenzie, Jihong Wang, Ronald M. Summers (Ed.), Bram Van Ginneken (Ed.)
Electrical & Computer Engineering Faculty Publications
In a recent study [1], we investigated the feasibility of predicting brain tumor progression based on multiple MRI series and we tested our methods on seven patients' MRI images scanned at three consecutive visits A, B and C. Experimental results showed that it is feasible to predict tumor progression from visit A to visit C using a model trained by the information from visit A to visit B. However, the trained model failed when we tried to predict tumor progression from visit B to visit C, though it is clinically more important. Upon a closer look at the MRI scans …