Computer-Aided Mammogram Mass Diagnosis Using Scalable Image Retrieval
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Computer-Aided Mammogram Mass Diagnosis Using Scalable Image Retrieval
- 1. COMPUTER-AIDED DIAGNOSIS OF MAMMOGRAPHIC MASSES USING SCALABLE IMAGE RETRIEVAL ABSTRACT Computer-aided diagnosis of masses in mammograms is important to the prevention of breast cancer. Manyapproaches tackle this problem through content-based imageretrieval techniques. However, most of them fall short of scalabilityin the retrieval stage, and their diagnostic accuracy is, therefore,restricted. To overcome this drawback, we propose a scalablemethod for retrieval and diagnosis of mammographic masses.Specifically, for a query mammographic region of interest (ROI),scale-invariant feature transform (SIFT) features are extracted andsearched in a vocabulary tree, which stores all the quantized featuresof previously diagnosed mammographic ROIs. In addition,to fully exert the discriminative power of SIFT features, contextualinformation in the vocabulary tree is employed to refine theweights of tree nodes. The retrieved ROIs are then used to determinewhether the query ROI contains a mass. The presentedmethod has excellent scalability due to the low spatial-temporalcost of vocabulary tree. Extensive experiments are conducted on alarge dataset of 11 553 ROIs extracted from the digital databasefor screeningammongraph, which demonstrate the accuracy andscalability of our approach.