Bajger, Mariusz

Ma, Fei

Susukida, Hirotaka

Crossref

Automatic tuning of a graph-based image segmentation method for digital mammography applications

Mammogram segmentation tasks underpin a wide range of regis-tration, temporal analysis and detection algorithms. Unfortunately, finding an accurate, robust and efficient segmentation still remains a challenging problem in mammography. A recent segmentation tech-nique, based on minimum spanning trees (MST segmentation), is known to be robust to typical mammogram distortions and computa-tionally efficient. This method captures both local and global image information but the balance requires choosing a parameter. So far no automatic procedure to estimate this parameter has been proposed and the value was determined experimentally. In this paper a seg-mentation evaluation criterion, based on a measure of image entropy, is used to automatically optimize the granularity of an MST-based segmentation. The method is tested on a set of 82 random images taken from a commonly used mammogram database. The results show a dramatic improvement in the accuracy of a MST segmenta-tion tuned up using the entropy-based criterion. Index Terms — Image segmentation, entropy, minimum span-ning tree, mammograph

Hirotaka Susukida

Fei Ma

Mariusz Bajger

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