Design and analysis of a content-based pathology image retrieval system

Zheng, Lei; Wetzel, Arthur W.; Gilbertson, John R.; Becich, Michael J.

doi:10.1109/titb.2003.822952

Cited by 103 publications

(54 citation statements)

References 23 publications

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“…Although text-based retrieval is capable of supporting a high degree of image-content semantics, it is likely that text-based retrieval is unable to sufficiently describe the visual features of the images [6], [7]. The content-based image retrieval (CBIR) of medical images according to its domain-specific image features is an important alternative and complement to traditional text-based retrieval using keywords [3], [6], [8]- [16]. In recent years, various CBIR systems have been introduced for medical images.…”

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confidence: 99%

A New Way for Multidimensional Medical Data Management: Volume of Interest (VOI)-Based Retrieval of Medical Images With Visual and Functional Features

Kim

Cai

Feng

et al. 2006

IEEE Trans. Inform. Technol. Biomed.

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Abstract-The advances in digital medical imaging and storage in integrated databases are resulting in growing demands for efficient image retrieval and management. Content-based image retrieval (CBIR) refers to the retrieval of images from a database, using the visual features derived from the information in the image, and has become an attractive approach to managing large medical image archives. In conventional CBIR systems for medical images, images are often segmented into regions which are used to derive two-dimensional visual features for region-based queries. Although such approach has the advantage of including only relevant regions in the formulation of a query, medical images that are inherently multidimensional can potentially benefit from the multidimensional feature extraction which could open up new opportunities in visual feature extraction and retrieval. In this study, we present a volume of interest (VOI) based content-based retrieval of four-dimensional (three spatial and one temporal) dynamic PET images. By segmenting the images into VOIs consisting of functionally similar voxels (e.g., a tumor structure), multidimensional visual and functional features were extracted and used as region-based query features. A prototype VOI-based functional image retrieval system (VOI-FIRS) has been designed to demonstrate the proposed multidimensional feature extraction and retrieval. Experimental results show that the proposed system allows for the retrieval of related images that constitute similar visual and functional VOI features, and can find potential applications in medical data management, such as to aid in education, diagnosis, and statistical analysis.Index Terms-Functional imaging, image segmentation, multidimensional features, region-based image retrieval.

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confidence: 99%

A New Way for Multidimensional Medical Data Management: Volume of Interest (VOI)-Based Retrieval of Medical Images With Visual and Functional Features

Kim

Cai

Feng

et al. 2006

IEEE Trans. Inform. Technol. Biomed.

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“…Content-based image retrieval has been an active area of research in the medical imaging field with many applications [72][73][74] including pathology. 75,76 Toward that goal, images containing certain desired color stains would be used to train the algorithm. The algorithm would then be applied to a data set, resulting, for instance, in a ranked list of retrieved images sharing similar color, based on the algorithm output.…”

Section: Commentmentioning

confidence: 99%

Quantitative Assessment and Classification of Tissue-Based Biomarker Expression With Color Content Analysis

Keller¹,

Chen²,

Gavrielides³

2012

Archives of Pathology &Amp; Laboratory Medicine

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N Context.-The use of computer aids has been suggested as a way to reduce interobserver variability that is known to exist in the interpretation of immunohistochemical staining in pathology. Such computer aids should be automated in their usage but also they should be trained in an automated and reproducible fashion.Objective.-To present a computer aid for the quantitative analysis of tissue-based biomarkers, based on color content analysis.Design.-The developed system incorporates an automated algorithm to allow retraining based on the color properties of different training sets. The algorithm first generates a color palette containing the colors present in a training subset. Based on the palette, color histograms are derived and are used as feature vectors to a pattern recognition system, which returns an output proportional to biomarker continuous expression or a categorical classification. The method was evaluated on a database of HER2/neu digital breast cancer slides, for which expression scores from a pathologist panel were available. The system was retrained and evaluated on different transformations of the database, including compression, blurring, and changes in illumination, to examine its robustness to different imaging conditions frequently met in digital pathology.Results.-Results showed high agreement between the results of the algorithm and the truth from the pathologist panel as well as robustness to image transformations.Conclusions.-The results of the study are encouraging for the potential of this method as a computer aid to assess biomarker expression in a consistent and reproducible manner.

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“…They soften the matching by allowing one region of an image to be matched to several regions of another image. Zheng et al [9] presented a CBIR system employing a client/server architecture and utilized four image feature types: color histogram, image texture, Fourier coefficients, and wavelet coefficients, using the vector dot product as a distance metric for similarity measurement. Comaniciu et al [10] presented a CBIR system for clinical application on whole image similarity by retrieving similar cases and not based on sub images as in our case.…”

Section: Related Workmentioning

confidence: 99%

Content based sub-image retrieval system for high resolution pathology images using salient interest points

Mehta

Alomari

Chaudhary

2009

2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Content-based image retrieval systems for digital pathology require sub-image retrieval rather than the whole image retrieval for the system to be of clinical use. Digital pathology images are huge in size and thus the pathologist is interested in retrieving specific structures from the whole images in the database along with the previous diagnosis of the retrieved sub-image. We propose a content-based sub-image retrieval system (sCBIR) framework for high resolution digital pathology images. We utilize scale-invariant feature extraction and present an efficient and robust searching mechanism for indexing the images as well as for query execution of sub-image retrieval. We present a working sCBIR system and show results of testing our system on a set of queries for specific structures of interest for pathologists in clinical use. The outcomes of the sCBIR system are compared to manual search and there is an 80% match in the top five searches.

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Design and analysis of a content-based pathology image retrieval system

Cited by 103 publications

References 23 publications

A New Way for Multidimensional Medical Data Management: Volume of Interest (VOI)-Based Retrieval of Medical Images With Visual and Functional Features

A New Way for Multidimensional Medical Data Management: Volume of Interest (VOI)-Based Retrieval of Medical Images With Visual and Functional Features

Quantitative Assessment and Classification of Tissue-Based Biomarker Expression With Color Content Analysis

Content based sub-image retrieval system for high resolution pathology images using salient interest points

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