Detección de microcalcificaciones utilizando discriminantes lineales de Fisher
The main objective of this thesis is to develop a new method for detecting microcalcifications in digital mammograms, using machine learning and computer vision techniques; the method detects different shapes, sizes and intensities of microcalcifications, and also it´s able to recognize them not onl...
| Autor: | |
|---|---|
| Formato: | tesis de maestría |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2009 |
| País: | México |
| Recursos: | Instituto Nacional de Astrofísica, Óptica y Electrónica |
| Repositorio: | Repositorio Institucional del INAOE |
| Idioma: | español |
| OAI Identifier: | oai:inaoe.repositorioinstitucional.mx:1009/442 |
| Acesso em linha: | http://inaoe.repositorioinstitucional.mx/jspui/handle/1009/442 |
| Access Level: | acceso abierto |
| Palavra-chave: | info:eu-repo/classification/Segmentación de imagen/Image segmentation info:eu-repo/classification/Imagen de clasificación/Classification image info:eu-repo/classification/Visión/Vision info:eu-repo/classification/cti/1 info:eu-repo/classification/cti/12 info:eu-repo/classification/cti/1203 |
| Resumo: | The main objective of this thesis is to develop a new method for detecting microcalcifications in digital mammograms, using machine learning and computer vision techniques; the method detects different shapes, sizes and intensities of microcalcifications, and also it´s able to recognize them not only in fat breast but also in dense breast. To achieve this objective, the breast tissue is taken into account to detect microcalcifications even in dense breast, where the contrast difference between tissue and microcalcification is almost null. In the segmentation step the Fisher Linear Discriminants are used to segment the possible microcalcifications, to reduce the false positives generated in this step, some characteristics (morphological and intensity) are extracted from these regions. The method is tested; using ISSSTEP and MIAS databases, the ROC and FROC analysis were used as performance measures. |
|---|