Lin Yang1, Xin Qi, Fuyong Xing, Tahsin Kurc, Joel Saltz, David J Foran. 1. Division of Biomedical Informatics, Department of Biostatistics and Department of Computer Science, University of Kentucky, Lexington, KY, Center for Biomedical Imaging and Informatics, The Cancer Institute of New Jersey, Rutgers University, New Brunswick, NJ, Center for Comprehensive Informatics, Emory University, Atlanta, GA and Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA, USA.
Abstract
MOTIVATION: The capacity to systematically search through large image collections and ensembles and detect regions exhibiting similar morphological characteristics is central to pathology diagnosis. Unfortunately, the primary methods used to search digitized, whole-slide histopathology specimens are slow and prone to inter- and intra-observer variability. The central objective of this research was to design, develop, and evaluate a content-based image retrieval system to assist doctors for quick and reliable content-based comparative search of similar prostate image patches. METHOD: Given a representative image patch (sub-image), the algorithm will return a ranked ensemble of image patches throughout the entire whole-slide histology section which exhibits the most similar morphologic characteristics. This is accomplished by first performing hierarchical searching based on a newly developed hierarchical annular histogram (HAH). The set of candidates is then further refined in the second stage of processing by computing a color histogram from eight equally divided segments within each square annular bin defined in the original HAH. A demand-driven master-worker parallelization approach is employed to speed up the searching procedure. Using this strategy, the query patch is broadcasted to all worker processes. Each worker process is dynamically assigned an image by the master process to search for and return a ranked list of similar patches in the image. RESULTS: The algorithm was tested using digitized hematoxylin and eosin (H&E) stained prostate cancer specimens. We have achieved an excellent image retrieval performance. The recall rate within the first 40 rank retrieved image patches is ∼90%. AVAILABILITY AND IMPLEMENTATION: Both the testing data and source code can be downloaded from http://pleiad.umdnj.edu/CBII/Bioinformatics/.
MOTIVATION: The capacity to systematically search through large image collections and ensembles and detect regions exhibiting similar morphological characteristics is central to pathology diagnosis. Unfortunately, the primary methods used to search digitized, whole-slide histopathology specimens are slow and prone to inter- and intra-observer variability. The central objective of this research was to design, develop, and evaluate a content-based image retrieval system to assist doctors for quick and reliable content-based comparative search of similar prostate image patches. METHOD: Given a representative image patch (sub-image), the algorithm will return a ranked ensemble of image patches throughout the entire whole-slide histology section which exhibits the most similar morphologic characteristics. This is accomplished by first performing hierarchical searching based on a newly developed hierarchical annular histogram (HAH). The set of candidates is then further refined in the second stage of processing by computing a color histogram from eight equally divided segments within each square annular bin defined in the original HAH. A demand-driven master-worker parallelization approach is employed to speed up the searching procedure. Using this strategy, the query patch is broadcasted to all worker processes. Each worker process is dynamically assigned an image by the master process to search for and return a ranked list of similar patches in the image. RESULTS: The algorithm was tested using digitized hematoxylin and eosin (H&E) stained prostate cancer specimens. We have achieved an excellent image retrieval performance. The recall rate within the first 40 rank retrieved image patches is ∼90%. AVAILABILITY AND IMPLEMENTATION: Both the testing data and source code can be downloaded from http://pleiad.umdnj.edu/CBII/Bioinformatics/.
Authors: David J Foran; Lin Yang; Wenjin Chen; Jun Hu; Lauri A Goodell; Michael Reiss; Fusheng Wang; Tahsin Kurc; Tony Pan; Ashish Sharma; Joel H Saltz Journal: J Am Med Inform Assoc Date: 2011-05-23 Impact factor: 4.497
Authors: Tahsin Kurc; Xin Qi; Daihou Wang; Fusheng Wang; George Teodoro; Lee Cooper; Michael Nalisnik; Lin Yang; Joel Saltz; David J Foran Journal: BMC Bioinformatics Date: 2015-12-01 Impact factor: 3.169
Authors: David J Foran; Eric B Durbin; Wenjin Chen; Evita Sadimin; Ashish Sharma; Imon Banerjee; Tahsin Kurc; Nan Li; Antoinette M Stroup; Gerald Harris; Annie Gu; Maria Schymura; Rajarsi Gupta; Erich Bremer; Joseph Balsamo; Tammy DiPrima; Feiqiao Wang; Shahira Abousamra; Dimitris Samaras; Isaac Hands; Kevin Ward; Joel H Saltz Journal: J Pathol Inform Date: 2022-01-05