Automatic annotation of histopathological images using a latent topic model based on non-negative matrix factorization
Histopathological images are an important resource for clinical diagnosis and biomedical research. From an image understanding point of view, the automatic annotation of these images is a challenging problem. This paper presents a new method for automatic histopathological image annotation based on...
- Autores:
-
Cruz Roa, Angel Alfonso
Díaz Cabrera, Gloria Mercedes
Romero Castro, Eduardo
González Osorio, Fabio Augusto
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2012
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/9021
- Palabra clave:
- 6 Tecnología (ciencias aplicadas) / Technology
61 Ciencias médicas; Medicina / Medicine and health
Basal Cell Carcinoma
Histopathology Images
Automatic Annotation
Visual Latent Semantic Analysis
Non-negative Matrix Factorization
Bag of Features
- Rights
- openAccess
- License
- Atribución-NoComercial 4.0 Internacional
| Summary: | Histopathological images are an important resource for clinical diagnosis and biomedical research. From an image understanding point of view, the automatic annotation of these images is a challenging problem. This paper presents a new method for automatic histopathological image annotation based on three complementary strategies, first, a part-based image representation, called the bag of features, which takes advantage of the natural redundancy of histopathological images for capturing the fundamental patterns of biological structures, second, a latent topic model, based on non-negative matrix factorization, which captures the high-level visual patterns hidden in the image, and, third, a probabilistic annotation model that links visual appearance of morphological and architectural features associated to 10 histopathological image annotations. The method was evaluated using 1,604 annotated images of skin tissues, which included normal and pathological architectural and morphological features, obtaining a recall of 74% and a precision of 50%, which improved a baseline annotation method based on support vector machines in a 64% and 24%, respectively. |
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