Cláudio Eduardo Góes

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This paper presents a method to provide contrast enhancement in dense breast digitized images, which are difficult cases in testing of computer-aided diagnosis (CAD) schemes. Three techniques were developed, and data from each method were combined to provide a better result in relation to detection of clustered microcalcifications. Results obtained during(More)
Some processing techniques cited in the literature used for microcalcifications detection in digitized mammograms are evaluated here with regard to dense breast images. Three techniques were investigated: Nappi et al.'s, Nishikawa et al.'s and Wallet et al.'s. The methods were tested with low-contrast phantom images, simulating dense breast images. The(More)
This article describes a segmentation technique that uses the watershed transformation to provide mass detection in dense breast digitized images. The technique consists of four steps: preprocessing, which isolates the breast from the image background; histogram equalization to enhance the contrast; watershed transformation, which calculates the gradient(More)
Clustered microcalcifications, which are frequently an important signal of possible cancer, are usually hidden in dense breast images, adding more difficulty in mammogram medical analysis. In this work we evaluate the performance of a previously developed computer-aided detection scheme, modified for application to dense breast images. The main focus of(More)
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