Histology Image Retrieval in Optimized Multifeature Spaces

Content-based histology image retrieval systems have shown great potential in supporting decision making in clinical activities, teaching, and biological research. In content-based image retrieval, feature combination plays a key role. It aims at enhancing the descriptive power of visual features corresponding to semantically meaningful queries. It is particularly valuable in histology image analysis where intelligent mechanisms are needed for interpreting varying tissue composition and architecture into histological concepts. This paper presents an approach to automatically combine heterogeneous visual features for histology image retrieval. The aim is to obtain the most representative fusion model for a particular keyword that is associated with multiple query images. The core of this approach is a multiobjective learning method, which aims to understand an optimal visual-semantic matching function by jointly considering the different preferences of the group of query images. The task is posed as an optimization problem, and a multiobjective optimization strategy is employed in order to handle potential contradictions in the query images associated with the same keyword. Experiments were performed on two different collections of histology images. The results show that it is possible to improve a system for content-based histology image retrieval by using an appropriately defined multifeature fusion model, which takes careful consideration of the structure and distribution of visual features.

[1]  Robert M. Nishikawa,et al.  Microcalcification Classification Assisted by Content-Based Image Retrieval for Breast Cancer Diagnosis , 2007, 2007 IEEE International Conference on Image Processing.

[2]  Anant Madabhushi,et al.  A boosted distance metric: application to content based image retrieval and classification of digitized histopathology , 2009, Medical Imaging.

[3]  Noël Bonnet,et al.  Some trends in microscope image processing. , 2004, Micron.

[4]  Cordelia Schmid,et al.  Local Grayvalue Invariants for Image Retrieval , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Ke Chen,et al.  Content-Based Medical Ultrasound Image Retrieval Using a Hierarchical Method , 2009, 2009 2nd International Congress on Image and Signal Processing.

[6]  James Ze Wang,et al.  Image retrieval: Ideas, influences, and trends of the new age , 2008, CSUR.

[7]  Dorin Comaniciu,et al.  Image-guided decision support system for pathology , 1999, Machine Vision and Applications.

[8]  Henning Müller,et al.  Overview of the ImageCLEFmed 2008 Medical Image Retrieval Task , 2008, CLEF.

[9]  Constantinos G Loukas,et al.  A survey on histological image analysis-based assessment of three major biological factors influencing radiotherapy: proliferation, hypoxia and vasculature , 2004, Comput. Methods Programs Biomed..

[10]  Marcel Worring,et al.  Content-Based Image Retrieval at the End of the Early Years , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Francesco G. B. De Natale,et al.  Content-Based Image Retrieval by Feature Adaptation and Relevance Feedback , 2007, IEEE Transactions on Multimedia.

[12]  David W. Corne,et al.  Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy , 2000, Evolutionary Computation.

[13]  Hao Wu,et al.  A New Way for Multidimensional Medical Data Management: Volume of Interest (VOI)-Based Retrieval of Medical Images With Visual and Functional Features , 2006, IEEE Transactions on Information Technology in Biomedicine.

[14]  Alan F. Smeaton,et al.  A Comparison of Score, Rank and Probability-Based Fusion Methods for Video Shot Retrieval , 2005, CIVR.

[15]  Rudolf Hanka,et al.  Histological image retrieval based on semantic content analysis , 2003, IEEE Transactions on Information Technology in Biomedicine.

[16]  Nikolas P. Galatsanos,et al.  A similarity learning approach to content-based image retrieval: application to digital mammography , 2004, IEEE Transactions on Medical Imaging.

[17]  James Ze Wang,et al.  SHIRAZ: an automated histology image annotation system for zebrafish phenomics , 2010, Multimedia Tools and Applications.

[18]  Lei Zheng,et al.  Design and analysis of a content-based pathology image retrieval system , 2003, IEEE Transactions on Information Technology in Biomedicine.

[19]  Philippe Mulhem,et al.  LIG at ImageCLEF 2008, Evaluating Systems for Multilingual and Multimodal Information Access , 2008 .

[20]  Kilian Q. Weinberger,et al.  Distance Metric Learning for Large Margin Nearest Neighbor Classification , 2005, NIPS.

[21]  Ebroul Izquierdo,et al.  Combining Low-level Features for Improved Classification and Retrieval of Histology Images , 2010, Trans. Mass Data Anal. Images Signals.

[22]  Joo-Hwee Lim,et al.  Medical-Image Retrieval Based on Knowledge-Assisted Text and Image Indexing , 2007, IEEE Transactions on Circuits and Systems for Video Technology.

[23]  Gabriela Csurka,et al.  Visual categorization with bags of keypoints , 2002, eccv 2004.

[24]  David Dagan Feng,et al.  Content-based retrieval of dynamic PET functional images , 2000, IEEE Transactions on Information Technology in Biomedicine.

[25]  Michael Isard,et al.  Descriptor Learning for Efficient Retrieval , 2010, ECCV.

[26]  Hong Zhao,et al.  Automatic feature weight assignment based on genetic algorithm for image retrieval , 2003, IEEE International Conference on Robotics, Intelligent Systems and Signal Processing, 2003. Proceedings. 2003.

[27]  Ahmet Ekin,et al.  Local Structure-Based Region-of-Interest Retrieval in Brain MR Images , 2010, IEEE Transactions on Information Technology in Biomedicine.

[28]  A. Mojsilovi.  A Computational Model for Color Naming and Describing Color Composition of Images , 2022 .

[29]  Antoine Geissbühler,et al.  A Review of Content{Based Image Retrieval Systems in Medical Applications { Clinical Bene(cid:12)ts and Future Directions , 2022 .

[30]  Nicholas Ayache,et al.  Learning Semantic and Visual Similarity for Endomicroscopy Video Retrieval , 2012, IEEE Transactions on Medical Imaging.

[31]  Lior Shamir,et al.  WND-CHARM: Multi-purpose image classification using compound image transforms , 2008, Pattern Recognit. Lett..

[32]  Wei He,et al.  Image mining for investigative pathology using optimized feature extraction and data fusion , 2005, Comput. Methods Programs Biomed..

[33]  Mikhail Teverovskiy,et al.  Multifeature Prostate Cancer Diagnosis and Gleason Grading of Histological Images , 2007, IEEE Transactions on Medical Imaging.

[34]  Cordelia Schmid,et al.  A performance evaluation of local descriptors , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[35]  David A. Forsyth,et al.  Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary , 2002, ECCV.

[36]  Rudolf Hanka,et al.  Similarity measures for histological image retrieval , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[37]  Yongwang Zhao,et al.  Medical Image Retrieval with Query-Dependent Feature Fusion Based on One-Class SVM , 2010, 2010 13th IEEE International Conference on Computational Science and Engineering.

[38]  Fabio A. González,et al.  Content-based histopathology image retrieval using a kernel-based semantic annotation framework , 2011, J. Biomed. Informatics.

[39]  R. S. Laundy,et al.  Multiple Criteria Optimisation: Theory, Computation and Application , 1989 .

[40]  Metin Nafi Gürcan,et al.  Content-Based Microscopic Image Retrieval System for Multi-Image Queries , 2012, IEEE Transactions on Information Technology in Biomedicine.