Recent advances in technological solutions for automated high-speed and high-resolution whole slide imaging (WSI) have set the basis for a digital revolution in microscopy. This ability to observe and analyze entire specimens rather that single microscopic fields of view is affecting the way microscopic evaluation is practiced. However, WSI outputs quite huge multiple channel (at least three color channels) images (e.g. 30-40 GB) for a single slide and managing such amount of data is a unique challenge for this new era of digital microscopy. Currently, WSI workstations are mainly used to perform virtual microscopy, the practice of converting entire glass slides into high-resolution digital slides that can be viewed and managed across networks.
The aim of the proposed special issue is to present some of the cutting-edge works currently being done in Whole Slide Imaging and reveal the challenges that still lie ahead. The special issue will be a mix of invited and solicited papers. A perspective editorial written by the special issue guest editors will introduce the technology; describe potential applications and pitfalls. Invited papers are intended to provide reviews both from the medical and the image processing sides.
Further information can be found at: http://greyc.stlo.unicaen.fr/lezoray/CMIG-CFP/
Friday, January 29, 2010
Tuesday, January 26, 2010
CIALAB research is featured in OSU Research News
Our work on the segmentation of meniscus is featured in today’s OSU Research News. CIALAB is committed to developing state-of-the art image analysis techniques to help physicians and medical researchers. The article can be read the at the following address: http://researchnews.osu.edu/archive/kneesegment.htm
Friday, January 22, 2010
Dr. Gurcan to present a tutorial at IEEE ICASSP 2010
Dr. Gurcan will present a tutorial at IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2010 entitled “Biomedical Image Processing and Analysis Techniques.” ( http://www.icassp2010.org/Tutorial_03.asp)
Biomedical image processing and analysis requires coordinated efforts of medical professionals, algorithmic and software engineers, and statisticians. Basic image processing techniques are frequently used in every aspect of the development from initial pre-processing techniques for noise reduction, to segmentation of lesions, to registration of lesions. Recent advances in hardware and software have made it possible to create digital scans of whole slides. These images are relatively large (100k x 100k) and in color, hence processing them present new challenges. Similarly, new computed tomography and magnetic resonance imaging scanners produce thousands of slices of images. The processing need for these images are enormous. Although biomedical image analysis research is getting increasingly popular, it does not receive sufficient coverage in most curriculums. This tutorial will introduce the current challenges and recent advances and innovations in this newly developing area while reviewing several frequently used image processing techniques in this context. It will be taught from the perspective of a researcher, who carried out biomedical image analysis and processing research for over 10 years both in academia and industry.
While most images can be easily processed using a high-end computer, very large scale microscopic images require special processing techniques. This tutorial will talk about virtual microscopy, cluster/grid computing and parallel processing techniques. Some novel computational architectures such as general purpose GPUs and cell blades (e.g. those in Sony PlayStation™ 3) are extremely suitable to process these types of images, however, they require special coding techniques. It will also discuss how some of microscopic image processing can be done using these novel computational architectures, which can also be used for efficient processing of all kinds of images.
Biomedical image processing and analysis requires coordinated efforts of medical professionals, algorithmic and software engineers, and statisticians. Basic image processing techniques are frequently used in every aspect of the development from initial pre-processing techniques for noise reduction, to segmentation of lesions, to registration of lesions. Recent advances in hardware and software have made it possible to create digital scans of whole slides. These images are relatively large (100k x 100k) and in color, hence processing them present new challenges. Similarly, new computed tomography and magnetic resonance imaging scanners produce thousands of slices of images. The processing need for these images are enormous. Although biomedical image analysis research is getting increasingly popular, it does not receive sufficient coverage in most curriculums. This tutorial will introduce the current challenges and recent advances and innovations in this newly developing area while reviewing several frequently used image processing techniques in this context. It will be taught from the perspective of a researcher, who carried out biomedical image analysis and processing research for over 10 years both in academia and industry.
While most images can be easily processed using a high-end computer, very large scale microscopic images require special processing techniques. This tutorial will talk about virtual microscopy, cluster/grid computing and parallel processing techniques. Some novel computational architectures such as general purpose GPUs and cell blades (e.g. those in Sony PlayStation™ 3) are extremely suitable to process these types of images, however, they require special coding techniques. It will also discuss how some of microscopic image processing can be done using these novel computational architectures, which can also be used for efficient processing of all kinds of images.
Monday, January 11, 2010
CIALAB research is featured in The Lantern
CIALAB’s Osteoarthritis research is featured in The Lantern, The Ohio State University’s student newspaper. The full article can be found here: http://www.thelantern.com/campus/researchers-awarded-2-5-million-in-grants-1.1008026
The CIALAB develops unique imaging biomarkers for the detection, diagnosis, prognosis and treatment of diseases.
The CIALAB develops unique imaging biomarkers for the detection, diagnosis, prognosis and treatment of diseases.
Monday, December 21, 2009
Dr. Gurcan's Research Grant in OSU Research News
Dr. Gurcan's EUREKA (Exceptional, Unconventional Research Enabling Knowledge Acceleration) grant is discussed in a recent OSU research news article (http://researchnews.osu.edu/archive/2009eurekagrants.htm). Dr. Gurcan's Clinical Image Analysis Lab (www.bmi.osu.edu/cialab) is committed to developing imaging biomarkers for several diseases.
Friday, December 4, 2009
Classification of centroblast cells in follicular lymphoma method accepted for publication in the Analytical and Quantitative Cytology and Histology
A method for the classification of Centrablast cells versus Non-Centroblast in Follicular Lymphoma has been accepted for publication in the Analytical and Quantitative Cytology and Histology journal. The authors of the paper were Kamel Belkacem-Boussaid, Ph.D, and Michael. Pennell, Ph.D, Gerard. Lozanski, MD, Arwa. Shanaah, MD, and Metin Gurcan, Ph.D. The title of the manuscript is "Computer-aided classification of centroblast cells in follicular lymphoma". The abstract of the manuscript appears below.
Objective:
In this paper, we develop a novel automated method to distinguish centroblast (CB) cells from non-centroblast (non-CB) cells in follicular lymphoma cases and measure its performance on cases obtained by a consensus of six pathologists.
Study Design:
Geometric and color texture features were used in the training and testing of the supervised quadratic discriminant analysis (QDA) classifier. The technique was trained and tested on a data set composed of 218 CB images and 218 non-CB images. Computer performance was tested by measuring sensitivity and specificity among cells classified as centroblasts and non-centroblasts by consensus of six board-certified hematopathologists.
Results and Conclusion:
Automated classification distinguished centroblast cells (CB) from non-centroblast cells (non-CB) with a classification accuracy of 82.56% and sensitivity and specificity were 86.67% and 86.96%, respectively, when the approach was tested. The novelty of our approach is the identification of the CB cells with prior information, and the introduction of the principal component analysis (PCA) in the spectral domain to extract texture color features.
Objective:
In this paper, we develop a novel automated method to distinguish centroblast (CB) cells from non-centroblast (non-CB) cells in follicular lymphoma cases and measure its performance on cases obtained by a consensus of six pathologists.
Study Design:
Geometric and color texture features were used in the training and testing of the supervised quadratic discriminant analysis (QDA) classifier. The technique was trained and tested on a data set composed of 218 CB images and 218 non-CB images. Computer performance was tested by measuring sensitivity and specificity among cells classified as centroblasts and non-centroblasts by consensus of six board-certified hematopathologists.
Results and Conclusion:
Automated classification distinguished centroblast cells (CB) from non-centroblast cells (non-CB) with a classification accuracy of 82.56% and sensitivity and specificity were 86.67% and 86.96%, respectively, when the approach was tested. The novelty of our approach is the identification of the CB cells with prior information, and the introduction of the principal component analysis (PCA) in the spectral domain to extract texture color features.
OSU President Dr. Gee honors CIALAB scholar
The 4th Annual International Scholar Research Exposition Opening Reception took place in Bricker Hall on November 19, 2009 at The Ohio State University. Posters documenting the research efforts of visiting scholars are on display throughout November and December in the 2nd Floor Lobby of Bricker Hall.
Dr. Ahmet Alkan, an international scholar from Turkey, working at the CIALAB presented a poster at the exposition. The poster’s title is “Computerized Image Analysis of Thigh Muscles for Osteoarthritis.” President Gee and Dr. Whitacre, vice president for research, honored Dr. Alkan with a certificate.
Ahmet Alkan received his Ph.D. in Electrical & Electronics Engineering from Sakarya University in 2005. He is funded by a fellowship from The Scientific and Technological Research Council of Turkey (TÜBİTAK) in 2009. He is currently a visiting scholar in the Department of Biomedical Informatics at The Ohio State University, working with Dr. Metin N. Gurcan. His research interests include Signal Processing, Artificial Neural Networks and Biomedical Image Processing. He is an Assistant Professor in the Department of Electrical & Electronics Engineering at Kahramanmaras Sutcu Imam University/Turkey.
Dr. Ahmet Alkan, an international scholar from Turkey, working at the CIALAB presented a poster at the exposition. The poster’s title is “Computerized Image Analysis of Thigh Muscles for Osteoarthritis.” President Gee and Dr. Whitacre, vice president for research, honored Dr. Alkan with a certificate.
Ahmet Alkan received his Ph.D. in Electrical & Electronics Engineering from Sakarya University in 2005. He is funded by a fellowship from The Scientific and Technological Research Council of Turkey (TÜBİTAK) in 2009. He is currently a visiting scholar in the Department of Biomedical Informatics at The Ohio State University, working with Dr. Metin N. Gurcan. His research interests include Signal Processing, Artificial Neural Networks and Biomedical Image Processing. He is an Assistant Professor in the Department of Electrical & Electronics Engineering at Kahramanmaras Sutcu Imam University/Turkey.
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