Assessment of performance and reproducibility of applying a content-based image retrieval scheme for classification of XXXX .

@entity5 @entity1558 @entity146
PURPOSE: To develop a new @entity146 ( @entity146 ) scheme using a content-based image retrieval (CBIR) approach for classification between the malignant and benign @entity1558 depicted on the digital mammograms and assess @entity146 performance and reproducibility. METHODS: An image dataset including 820 regions of interest (ROIs) was used. Among them, 431 ROIs depict malignant lesions and 389 depict benign lesions. After applying an image preprocessing process to define the lesion center, two image features were computed from each ROI. The first feature is an average pixel value of a mapped region generated using a watershed algorithm. The second feature is an average pixel value difference between a ROI's center region and the rest of the image. A two-step CBIR approach uses these two features sequentially to search for ten most similar reference ROIs for each queried ROI. A similarity based classification score was then computed to predict the likelihood of the queried ROI depicting a @entity5 . To assess the reproducibility of the @entity146 scheme, we selected another independent testing dataset of 100 ROIs. For each ROI in the testing dataset, we added four randomly queried lesion center pixels and examined the variation of the classification scores. RESULTS: The area under the ROC curve (AUC) = 0.962    0.006 was obtained when applying a leave-one-out validation method to 820 ROIs. Using the independent testing dataset, the initial AUC value was 0.832    0.040, and using the median classification score of each ROI with five queried seeds, AUC value increased to 0.878    0.035. CONCLUSIONS: The authors demonstrated that (1) a simple and efficient CBIR scheme using two lesion density distribution related features achieved high performance in classifying @entity1558 without actual lesion segmentation and (2) similar to the conventional @entity146 schemes using global optimization approaches, improving reproducibility is also one of the challenges in developing @entity146 schemes using a CBIR based regional optimization approach.
