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We present a visual dictionary based method for content based image retrieval in cervical microscopy images using texture features. The nucleus region in each image is identified by a simple and reliable segmentation algorithm and texture features are extracted from blocks of the region. These features from the entire database are clustered to build a visual dictionary. The histogram of the visual words present in an image is used as the representation of the image. Histogram intersection serves as the distance measure to do content based image retrieval. Experiments were conducted for various block sizes and number of clusters and the results are presented. The task was to identify images of cancerous cells from normal ones. The method offers encouraging results to utmost 90% accuracy. A brief discussion of the results and possible future directions are given

Detection of Cancer in Pap smear Cytological Images Using Bag of Texture Features