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| MORENO,J; CAICEDO,J; GONZÁLEZ,F. |
| This paper presents a novel strategy for building a high-dimensional feature space to represent histopathology image contents. Histogram features, related to colors, textures and edges, are combined together in a unique image representation space using kernel functions. This feature space is further enhanced by the application of Latent Semantic Analysis, to model hidden relationships among visual patterns. All that information is included in the new image representation space. Then, Support Vector Machine classifiers are used to assign semantic labels to images. Processing and classification algorithms operate on top of kernel functions, so that, the structure of the feature space is completely controlled using similarity measures and a dual... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Automatic image annotation. |
| Ano: 2010 |
URL: http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S0120-548X2010000300018 |
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| GONZÁLEZ,F; CAICEDO,JC; CRUZ-ROA,A; CAMARGO,J; SPINEL,C. |
| Histology images are an important resource for research, education and medical practice. The availability of image collections with reference purposes is limited to printed formats such as books and specialized journals. When histology image sets are published in digital formats, they are composed of some tens of images that do not represent the wide diversity of biological structures that can be found in fundamental tissues. Making a complete histology image collection available to the general public having a great impact on research and education in different areas such as medicine, biology and natural sciences. This work presents the acquisition process of a histology image collection with 20,000 samples in digital format, from tissue processing to... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Histological images; Virtual atlas; Content-based image retrieval. |
| Ano: 2010 |
URL: http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S0120-548X2010000300016 |
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