Evolutionary Projection Selection for Radon Barcodes
Recently, Radon transformation has been used to generate barcodes for tagging medical images. The under-sampled image is projected in certain directions, and each projection is binarized using a local threshold. The concatenation of the thresholded projections creates a barcode that can be used for tagging or annotating medical images. A small number of equidistant projections, e.g., 4 or 8, is generally used to generate short barcodes. However, due to the diverse nature of digital images, and since we are only working with a small number of projections (to keep the barcode short), taking equidistant projections may not be the best course of action. In this paper, we proposed to find $n$ optimal projections, whereas $n\!<\!180$, in order to increase the expressiveness of Radon barcodes. We show examples for the exhaustive search for the simple case when we attempt to find 4 best projections out of 16 equidistant projections and compare it with the evolutionary approach in order to establish the benefit of the latter when operating on a small population size as in the case of micro-DE. We randomly selected 10 different classes from IRMA dataset (14,400 x-ray images in 58 classes) and further randomly selected 5 images per class for our tests.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
MinMax Radon Barcodes for Medical Image Retrieval
Content-based medical image retrieval can support diagnostic decisions by clinical experts. Examining similar images may provide clues to the expert to remove uncertainties in his/her final diagnosis. Beyond conventional…
DiagnosticImage RetrievalMedical Image RetrievalRetrieval+1Retrieving Similar X-Ray Images from Big Image Data Using Radon Barcodes with Single Projections
The idea of Radon barcodes (RBC) has been introduced recently. In this paper, we propose a content-based image retrieval approach for big datasets based on Radon barcodes. Our method (Single Projection Radon Barcode, or …
Content-Based Image RetrievalImage RetrievalRetrievalBarcodes for Medical Image Retrieval Using Autoencoded Radon Transform
Using content-based binary codes to tag digital images has emerged as a promising retrieval technology. Recently, Radon barcodes (RBCs) have been introduced as a new binary descriptor for image search. RBCs are generated…
BinarizationImage RetrievalMedical Image RetrievalRetrieval+1Radon Features and Barcodes for Medical Image Retrieval via SVM
For more than two decades, research has been performed on content-based image retrieval (CBIR). By combining Radon projections and the support vector machines (SVM), a content-based medical image retrieval method is pres…
Content-Based Image RetrievalImage RetrievalMedical Image RetrievalRetrieval+1Radon-Gabor Barcodes for Medical Image Retrieval
In recent years, with the explosion of digital images on the Web, content-based retrieval has emerged as a significant research area. Shapes, textures, edges and segments may play a key role in describing the content of …
Image RetrievalMedical Image RetrievalRetrieval