Multi-Scale Regularized Deep Network for Retinal Vessel Segmentation
| Autor: | Venkateswararao Cherukuri, Raja Bala, Vishal Monga, Vijay Kumar B G |
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| Rok vydání: | 2019 |
| Předmět: |
Orientation (computer vision)
Computer science business.industry Deep learning Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition 02 engineering and technology Image segmentation 030218 nuclear medicine & medical imaging Domain (software engineering) 03 medical and health sciences 0302 clinical medicine 0202 electrical engineering electronic engineering information engineering Benchmark (computing) 020201 artificial intelligence & image processing Segmentation Artificial intelligence Representation (mathematics) business |
| Zdroj: | ICIP |
| Popis: | Vessel segmentation of retinal images is a key diagnostic capability in ophthalmology. Early approaches addressing this problem employed hand-crafted filters to capture vessel structures, accompanied by morphological processing. More recently, deep learning techniques have been employed to significantly enhance segmentation accuracy. We propose a novel domain enriched deep network that consists of two components: 1) a representation network which learns geometric (specifically curvilinear) features that are tailored to retinal images, followed by 2) a task network that utilizes the features obtained from the representation layer to perform pixel-level segmentation. The representation and task networks are learned jointly for any given training set. To obtain effective representation filters, we develop a new orientation constraint that enables geometric diversity of curvilinear features. A multi-scale extension is further developed to enhance segmentation of thin vessels. Experiments performed on two challenging benchmark databases reveal that the proposed regularized deep network can outperform state of the art alternatives as measured by common evaluation metrics. Further, the proposed method exhibits a more graceful decay in performance as training data is reduced. |
| Databáze: | OpenAIRE |
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