Light Field Image Dataset of Skin Lesions
| Autor: | Pedro M. M. Pereira, Luis M. N. Tavora, Jose N. Filipe, Pedro Assuncao, Rui Fonseca-Pinto, Victoria Guiote Dominguez, Martinha Henrique, Miguel O. Santos, Sergio M. M. de Faria, Felicidade Santiago |
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| Rok vydání: | 2019 |
| Předmět: |
Computer science
business.industry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Dermoscopy 020207 software engineering 02 engineering and technology Skin Diseases Field (computer science) Image (mathematics) Visualization Machine Learning 03 medical and health sciences 0302 clinical medicine Available light 030220 oncology & carcinogenesis 0202 electrical engineering electronic engineering information engineering Medical imaging Humans Computer vision Artificial intelligence business Spatial analysis Algorithms Light field |
| Zdroj: | EMBC |
| Popis: | Light field imaging technology has been attracting increasing interest because it enables capturing enriched visual information and expands the processing capabilities of traditional 2D imaging systems. Dense multiview, accurate depth maps and multiple focus planes are examples of different types of visual information enabled by light fields. This technology is also emerging in medical imaging research, like dermatology, allowing to find new features and improve classification algorithms, namely those based on machine learning approaches. This paper presents a contribution for the research community, in the form of a publicly available light field image dataset of skin lesions (named SKINL2 v1.0). This dataset contains 250 light fields, captured with a focused plenoptic camera and classified into eight clinical categories, according to the type of lesion. Each light field is comprised of 81 different views of the same lesion. The database also includes the dermatoscopic image of each lesion. A representative subset of 17 central view images of the light fields is further characterised in terms of spatial information (SI), colourfulness (CF) and compressibility. This dataset has high potential for advancing medical imaging research and development of new classification algorithms based on light fields, as well as in clinically-oriented dermatology studies. |
| Databáze: | OpenAIRE |
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