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SHIRAZ Project: Publications

  1. Brian A. Canada, Georgia Thomas, Keith Cheng and James Z. Wang, "SHIRAZ: an automated histology image annotation system for zebrafish phenomics," Multimedia Tools and Applications, Special Issue on Hot Research Topics in Multimedia, vol. 51, no. 2, pp. 401-440, 2011. (download) (g-scholar)

    Abstract:Histological characterization is used in clinical and research contexts as a highly sensitive method for detecting the morphological features of disease and abnormal gene function. Histology has recently been accepted as a phenotyping method for the forthcoming Zebrafish Phenome Project, a large-scale community effort to characterize the morphological, physiological, and behavioral phenotypes resulting from the mutations in all known genes in the zebrafish genome. In support of this project, we present a novel content-based image retrieval system for the automated annotation of images containing histological abnormalities in the developing eye of the larval zebrafish.

  2. Brian A. Canada, Georgia K. Thomas, Keith C. Cheng, James Z. Wang and Yanxi Liu, "Automatic Lattice Detection in Near-Regular Histology Array Images,'' Proceedings of the IEEE International Conference on Image Processing (ICIP), San Diego, California, IEEE, October 2008. (download) (g-scholar)

    Abstract:
    Near-regular texture (NRT), denoting deviations from otherwise symmetric wallpaper patterns, is commonly observable in the real world. Existing lattice detection algorithms capture the underlying lattice of an NRT pattern and all of its individual texels, facilitating an automated analysis of NRT. Many real world images, as in those of zebrafish larval histology arrays, depart significantly from regularity and challenge the current state of the art wallpaper group theory-based lattice detection methods. We propose an alternative 2D lattice detection algorithm that exploits translation and reflection symmetries and specific imaging cues. By outperforming existing methods on histology array images, our algorithm leads us towards complete automation of high-throughput histological image processing while broadening the spectrum of NRT computation.

  3. Brian A. Canada, Georgia K. Thomas, Keith C. Cheng, James Z. Wang and Yanxi Liu, "Towards Efficient Automated Characterization of Irregular Histology Images via Transformation to Frieze-Like Patterns,'' Proceedings of the ACM International Conference on Image and Video Retrieval (CIVR 2008), pp. 581-590, Niagara Falls, Canada, July 2008. (download) (g-scholar)

    Abstract: Histology is used in both clinical and research contexts as a highly sensitive method for detecting morphological abnormalities in organ tissues. Although modern scanning equipment has enabled high-throughput digitization of high-resolution histology slides, the manual scoring and annotation of these images is a tedious, subjective, and sometimes error-prone process. A number of methods have been proposed for the automated characterization of histology images, most of which rely on the extraction of texture features used for classifier training. The irregular, nonlinear shapes of certain types of tissues can obscure the implicit symmetries observed within them, making it difficult or cumbersome for automated methods to extract texture features quickly and reliably. Using larval zebrafish eye and gut tissues as a pilot model, we present a prototype method for transforming the appearance of these irregularly-shaped tissues into onedimensional, frieze-like patterns. We show that the reduced dimensionality of the patterns may allow them to be characterized with greater efficiency and accuracy than by previous methods of image analysis, which in turn enables potentially greater accuracy in the retrieval of histology images exhibiting abnormalities of interest to pathologists and researchers.

  4. Brian A. Canada, Georgia K. Thomas, Keith C. Cheng, and James Z. Wang, "Automated Segmentation and Classification of Zebrafish Histology Images for High-Throughput Phenotyping,'' Proceedings of the Third IEEE-NIH Life Science Systems and Applications Workshop (LISSA 2007), pp. 245-248, Bethesda, MD, November 2007. (download) (g-scholar)

    Abstract: Because of its small size and rapid development the larval zevrafish is an ideal model organism for studying mutant phenotypes using high-thoughput histological analysis. Although the preparation and subsequent digitization of zebrafish larval histology specimens can be conducted in parallel, the scoring and annotation of the resulting virtual slides is largely manual and therefore rate limiting, which motivates the development of systems for automated characterization of histology images. We present a prototype for automated segmentation and classification of histology images in animal models, with a pilot study focusing on larval zebrafish eye and gut images. We show that the segmentation of the images into regions of individual cell layers can be conducted with good precision using combinations of widely-used image processing operations, and that the resulting classification system, based on a decision tree algorithm, exhibits promising performance.


  5. Brian A. Canada, Keith C. Cheng, and James Z. Wang, "QCHARM: A Novel Computational and Scientific Visualization Framework for Facilitating Discovery and Improving Diagnostic Reliability in Medicine,'' Proceedings of the American Medical Informatics Association Annual Symposium, pp. 870, Washington, D.C., November 2006. (download) (g-scholar)

Related Publications – Image Processing, Retrieval, and Annotation

  1. Dhiraj Joshi, Jia Li and James Z. Wang, "A Computationally Efficient Approach to the Estimation of Two- and Three-dimensional Hidden Markov Models,'' IEEE Transactions on Image Processing, vol. 15, no. 7, pp. 1871-1886, 2006. [Abstracts were published in Proc. ICIP, 2004, 2005] (download) (g-scholar)

  2. Dhiraj Joshi, Jia Li and James Z. Wang, "A Stochastic Modeling Approach to 3-D Image Modeling,'' Proceedings of the IEEE/NLM Life Science Systems and Application Workshop, pp. 120-121, IEEE, Bethesda, MD, July 2006. (download) (g-scholar)

  3. Dhiraj Joshi, Jia Li and James Z. Wang, "Parameter Estimation of Multi-Dimensional Hidden Markov Models - A Scalable Approach,'' Proceedings of the IEEE International Conference on Image Processing (ICIP), Genova, Italy, vol. 3, pp. 149-152, IEEE, September 2005. (download) (g-scholar)

  4. Jia Li, Dhiraj Joshi and James Z. Wang, "Stochastic Modeling of Volume Images with a 3-D Hidden Markov Model,'' Proceedings of the IEEE International Conference on Image Processing (ICIP), pp. 2359-2362, IEEE, Singapore, October 2004. (download) (g-scholar)

  5. Ya Zhang and James Z. Wang, "Progressive Display of Very High Resolution Images Using Wavelets,'' Journal of the American Medical Informatics Association, Proceedings of the AMIA Annual Symposium, vol. 2002 symposium suppl., pp. 944-948, San Antonio, TX, November 2002. [Selected to present at the plenary session at the AMIA Annual Symposium and nominated for the Best Paper Award] (download) (g-scholar)

  6. James Z. Wang, "Wavelets and Imaging Informatics,'' Journal of Biomedical Informatics (formerly Computers and Biomedical Research), vol. 34, no. 2, pp. 129-141, 2001. (download) (g-scholar)

Related Publications – Zebrafish Histology, Genetics, and Functional Genomics

  1. Mohideen M-APK, Beckwith LG, Tsao-Wu GS, Moore JL, Wong ACC, Chinoy MR, Cheng KC. "Histology-based screen for zebrafish mutants with abnormal cell differentiation," Developmental Dynamics 228:414-423, 2003. PMID: 14579380 [View PDF]

  2. Moore JL, Aros M, Steudel KG, Cheng KC. "Fixation and decalcification of adult zebrafish for histological, immunocytochemical, and genotypic analysis," Biotechniques 32:296-298, 2002. PMID: 11848405 [View PDF]

  3. Sabaliauskas NA, Foutz CA, Mest JR, Budgeon LR, Sidor A, Gershenson J, Joshi S, Cheng KC. "High-throughput zebrafish histology," Methods 39:246-254, 2006. PMID: 16870470 [View PDF]

  4. Lamason RL, Mohideen M-AP, Mest JR, Wong AC, Norton HL, Aros MC, Juurynec MJ, Mao X, Humphreville VR, Humbert JE, Sinha S, Moore JL, Jagadeeswaran P, Ning G, Makalowska I, Zhao W, McKeigue PM, O'Donnell D, Kittles R, Parra EJ, Mangini NJ, Grunwald DJ, Shriver MD, Canfield VA, Cheng KC. "SLC24A5, a Putative Cation Exchanger, Affects Pigmentation in Zebrafish and Humans," Science 310:1782-1786, 2005. (cover article) PMID: 16357253 [due to the large file size of the supplement and cover, we offer three versions: SciencePaperNoSuppl.pdf (the paper as presented in the journal; 457kb), SciencePaper&Suppl.pdf (the paper in the journal, including the supplementary data; 2857kb), and Science16Dec2005cover.pdf (just the cover, in high resolution, originally submitted with the caption, "Skin color is only gene deep"; 5814kb)]. Science 16 December 2005 "News of the Week".

  5. Moore JL, Rush LM, Breneman C, Mohideen M-AP, Cheng, KC. "Zebrafish genomic instability mutants and cancer susceptibility," Genetics 174:585-600, 2006 (October cover article), 2006. PMID: 16888336 [View PDF]

  6. Croushore JA, Blasiole B, Riddle RC, Thisse C, Thisse B, Canfield VA, Robertson GP, Cheng KC, Levenson R. "ptena and ptenb genes play distinct roles in zebrafish embryogenesis," Developmental Dynamics 234:911-921, 2005. PMID: 16193492 [View PDF]

Related Publications – Bioinformatics

  1. Ya Zhang, Hongyuan Zha, James Z. Wang and Chao-Hsien Chu, "Gene Co-regulation vs. Co-expression,'' Poster Proceedings of the International Conference on Research in Computational Molecular Biology (RECOMB), pp. 232-233, San Diego, CA, March 2004. (download) (g-scholar)

  2. Ya Zhang, Hongyuan Zha, James Z. Wang and Chao-Hsien Chu, "Clustering of Time-Course Gene Expression Data,'' Poster Proceedings of the International Conference on Research in Computational Molecular Biology (RECOMB), pp. 240-241, San Diego, CA, March 2004. (download) (g-scholar)

  3. Eldar Giladi, Michael G. Walker, James Z. Wang and Wayne Volkmuth, "SST: An Algorithm for Finding Near-Exact Sequence Matches in Time Proportional to the Logarithm of the Database Size,'' Bioinformatics, vol. 18, no. 6, pp. 873-879, 2002. [An abstract was published in RECOMB 2000] (download) (g-scholar)