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Descripción
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| International Journal of Intelligent Computing in Medical Sciences and Image Processing (IC-MED). This paper presents a novel method for the automatic detection of microcalcifications in regions of interest images. Automatic detection method is implemented by feature extraction and sub-segmentation steps. The feature extraction step is improved using a top-hat transform such that microcalcifications can be highlighted. In a second step a sub-segmentation method based on the possibilistic fuzzy c-means clustering algorithm is applied in order to segment the images and as a way to identify the atypical pixels inside the regions of interest as the pixels representing microcalcifications. Once the pixels representing these objects have been identified, an artificial neural network model is used to learn the relations between atypical pixels and microcalcifications, such that the model can be used for aid diagnosis, and a medical could determine if these regions of interest are benign or malignant. So, as the results show, the proposed approach is a good alternative for the detection of suspicious regions, which could be of great help for medical diagnosis. | |
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Internacional
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Si |
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JCR del ISI
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No |
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Título de la revista
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International Journal of Intelligent Computing in Medical Sciences and Image Processing (IC-MED) |
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ISSN
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1931-308X |
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Factor de impacto JCR
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Información de impacto
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Volumen
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5 |
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DOI
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10.1080/1931308X.2013.838070 |
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Número de revista
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2 |
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Desde la página
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161 |
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Hasta la página
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174 |
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Mes
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OCTUBRE |
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Ranking
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