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| Author(s): |
Alceu Ferraz Costa
Total Authors: 1
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| Document type: | Master's Dissertation |
| Press: | São Carlos. |
| Institution: | Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC/SB) |
| Defense date: | 2012-04-10 |
| Examining board members: |
Agma Juci Machado Traina;
Aparecido Nilceu Marana;
Paulo Mazzoncini de Azevedo Marques
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| Advisor: | Agma Juci Machado Traina |
| Abstract | |
Medical image databases represent a valuable source of data from which potential knowledge can be extracted. Image mining can be applied to knowledge discover from these data in order to help CAD (Computer Aided Diagnosis) systems. The typical set-up of a CAD system consists in the extraction of relevant visual features in the form of image feature vectors that are used as input to a classifier. Due to the semantic gap problem, which corresponds to the difference between the humans image perception and the features automatically extracted from the image, a challenging aspect of CAD is to obtain a set of features that is able to succinctly and efficiently represent the visual contents of medical images. To deal with this problem it was developed in this work a new feature extraction method entitled Fast Fractal Stack (FFS). FFS extracts shape features from objects and structures, which is a visual attribute that approximates the semantics expected by humans. Additionally, it was developed the Concept classification method, which employs association rules mining to the task of image class prediction. The innovative aspect of Concept refers to its image representation algorithm termed MFS-Map (Multi Feature Space Map). MFS-Map employs clustering in different feature spaces to maximize features usefulness in the classification process. Experiments performed employing computed tomography and mammography images indicate that both FFS and Concept methods for image representation can contribute to the improvement of CAD systems (AU) | |
| FAPESP's process: | 09/12905-2 - Medical Image Mining Supported by Content Based Image Retrieval Employing Shape Features |
| Grantee: | Alceu Ferraz Costa |
| Support Opportunities: | Scholarships in Brazil - Master |