Visual character n grams for classification and retrieval of radiological images
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Diagnostic radiology struggles to maintain high interpretation accuracy. Retrieval of past similar cases ...
Diagnostic radiology struggles to maintain high interpretation accuracy. Retrieval of past similar cases
would help the inexperienced radiologist in the interpretation process. Character n-gram model has been
effective in text retrieval context in languages such as Chinese where there are no clear word boundaries.
We propose the use of visual character n-gram model for representation of image for classification and
retrieval purposes. Regions of interests in mammographic images are represented with the character ngram
features. These features are then used as input to back-propagation neural network for classification
of regions into normal and abnormal categories. Experiments on miniMIAS database show that character
n-gram features are useful in classifying the regions into normal and abnormal categories. Promising
classification accuracies are observed (83.33%) for fatty background tissue warranting further
investigation. We argue that Classifying regions of interests would reduce the number of comparisons
necessary for finding similar images from the database and hence would reduce the time required for
retrieval of past similar cases.
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