Content-based image retrieval (CBIR) has been widely researched for medical images. In application of histo- pathological images, there are two issues that need to be carefully considered. The one is that the digital slide is stored in a spatially continuous image with a size of more than 10K x 10K pixels. The other is that the size of query image varies in a large range according to different diagnostic conditions. It is a challenging work to retrieve the eligible regions for the query image from the database that consists of whole slide images (WSIs). In this paper, we proposed a CBIR framework for the WSI database and size-scalable query images. Each WSI in the database is encoded and stored in a matrix of binary codes. When retrieving, the query image is first encoded into a set of binary codes and analyzed to pre-choose a set of regions from database using hashing method. Then a multi-binary-code-based similarity measurement based on hamming distance is designed to rank proposal regions. Finally, the top relevant regions and their locations in the WSIs along with the diagnostic information are returned to assist pathologists in diagnoses. The effectiveness of the proposed framework is evaluated in a fine-annotated WSIs database of epithelial breast tumors. The experimental results show that proposed framework is both effective and efficiency for content-based whole slide image retrieval.
Digital pathological image retrieval plays an important role in computer-aided diagnosis for breast cancer. The retrieval
results of an unknown pathological image, which are generally previous cases with diagnostic information, can provide
doctors with assistance and reference. In this paper, we develop a novel pathological image retrieval method for breast
cancer, which is based on stain component and probabilistic latent semantic analysis (pLSA) model. Specifically, the
method firstly utilizes color deconvolution to gain the representation of different stain components for cell nuclei and
cytoplasm, and then block Gabor features are conducted on cell nuclei, which is used to construct the codebook.
Furthermore, the connection between the words of the codebook and the latent topics among images are modeled by
pLSA. Therefore, each image can be represented by the topics and also the high-level semantic concepts of image can be
described. Experiments on the pathological image database for breast cancer demonstrate the effectiveness of our method.
For building detection from single very high spatial resolution (VHR) satellite images, we take advantage of visual saliency and Bayesian model to rapidly locate roof-top areas. We firstly generate saliency map of an image by a salient contrast filter using low-level feature. This filter distinguishes salient pixels if a pixel is visually different from its surroundings in color or texture. Secondly, a Bayesian model is proposed to generate all closed rectangular contours as mid-level content in the image. We suggest the area enclosed by contour corresponds to high saliency values. Finally, the roof-top areas are extracted by fusing different level information mentioned above. Experimental results demonstrate the effectiveness of our algorithm.