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A.: Automatic normal-abnormal video frame classification for colonoscopy
| Content Provider | CiteSeerX |
|---|---|
| Author | Manivannan, Siyamalan Wang, Ruixuan Trucco, Emanuele Hood, Adrian |
| Description | Two novel schemes are proposed to represent intermediate-scale features for normal-abnormal classification of colonoscopy images. The first scheme works on the full-resolution image, the second on a multi-scale pyramid space. Both schemes support any feature descriptor; here we use multi-resolution local binary patterns which outperformed other features reported in the literature in our com-parative experiments. We also compared experimentally two types of features not previously used in colonoscopy image classification, bag of features and sparse coding, each with and without spatial pyramid matching (SPM). We find that SPM improves performance, therefore supporting the importance of intermediate-scale features as in the proposed schemes for classification. Within normal-abnormal frame classification, we show that our representational schemes out-performs other features reported in the literature in leave-N-out tests with a database of 2100 colonoscopy images. 1. |
| File Format | |
| Language | English |
| Publisher Institution | In: IEEE Int. Symposium on Biomedical Imaging. (2013 |
| Access Restriction | Open |
| Subject Keyword | Sparse Coding Normal-abnormal Classification Automatic Normal-abnormal Video Frame Classification Colonoscopy Image Feature Descriptor Intermediate-scale Feature Colonoscopy Image Classification Multi-resolution Local Binary Pattern Representational Scheme Multi-scale Pyramid Space Novel Scheme Full-resolution Image Com-parative Experiment Scheme Support Leave-n-out Test First Scheme Normal-abnormal Frame Classification Spatial Pyramid Matching |
| Content Type | Text |
| Resource Type | Article |
