Difference between revisions of "2008 Winter Project Week:microslicer 3"

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|[[Image:NAMIC-SLC.jpg|thumb|320px|Return to [[2008_Winter_Project_Week]] ]]
 
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|valign="top"|[[Image:Case24-coronal-tensors-edit.png |thumb|320px|The Cingulum Bundle Anchor Tract]]
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* The Ohio State University: Kishore Mosaliganti, Raghu Machiraju
 
* The Ohio State University: Kishore Mosaliganti, Raghu Machiraju
 
* Kitware: Brad Davis, Stephen Aylward
 
* Kitware: Brad Davis, Stephen Aylward
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* Harvard: Steve Pieper  
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* BWH: Steve Pieper  
  
 
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<h1>Progress</h1>
 
<h1>Progress</h1>
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We have currently implemented the cell segmentation algorithm using Geodesics Active Contours and Image-based Voronoi Tessellations in the ITK framework. We have also implemented the microstructure characterization algorithms using the N-Point Correlation functions in ITK. Currently, we are working on building a cell shape model to use in cell segmentations.
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====June 2007 Project Week====
 
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During this Project Week, we did a lot of algorithmic design work, focusing on leveraging optimal or geodesic path information to provide for volumetric segmentations of fiber bundles.  Working with Marek Kubicki and the Harvard DBP, we were able to begin the process of applying our algorithm to the full cingulum bundle with new labelmaps and to a new fiber bundle - Arcuate. We have recently achieved significant results in volumetric segmentations using a locally-constrained region-based technique (see the images above).
 
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====Jan 2007 Project Half Week====
 
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We finished the itkDirectionalIterator which will be needed in the Fast Sweeping implementation. Furthermore, we made progress in porting our Matlab code to ITK.
 
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===References===
 
===References===
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* J. Melonakos, M. Niethammer, V. Mohan, M. Kubicki, J. Miller, A. Tannenbaum. Locally-Constrained Region-Based Methods for DW-MRI Segmentation. Submitted to MMBIA 2007.
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* Tensor Classification of N-point Correlation Function features for Histology Tissue Segmentation. K. Mosaliganti, F. Janoos, O. Irfanoglu, R. Ridgway, R. Machiraju, K. Huang, J. Saltz, Gustavo Leone and M. Ostrowski. In review for the Special Issue on Medical Image Analysis with Applications in Biology, Journal of Medical Image Analysis.
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* V. Mohan, J. Melonakos, M. Niethammer, M. Kubicki, and A. Tannenbaum. Finsler Level Set Segmentation for Imagery in Oriented Domains. BMVC 2007.
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* Visualization of Cellular Biology Structures from Optical Microscopy Data. K. Mosaliganti, L. Cooper, R. Sharp, R. Machiraju, K. Huang and Gustavo Leone. In review at the IEEE Transactions in Visualization and Computer Graphics.
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* J. Melonakos, V. Mohan, M. Niethammer, K. Smith, M. Kubicki, and A. Tannenbaum. Finsler Tractography for White Matter Connectivity Analysis of the Cingulum Bundle. MICCAI 2007.
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* Geometry-driven Visualization of Microscopic Structures in Biology. K. Mosaliganti, R. Machiraju, K. Huang and Gustavo Leone. In review at the Workshop on Knowledge-Assisted Visualization, IEEE Visualization Conference, Sacramento, California, 2007.
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* J. Melonakos, E. Pichon, S. Angenet, and A. Tannenbaum. Finsler Active Contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, to appear in 2007.
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* Automated Quantification of Colony Growth in Clonogenic Assays. K. Mosaliganti, J. Chen, F. Janoos, R. Machiraju, W. Xia, X. Xu, K. Huang. Workshop on Medical Image Analysis with Applications in Biology, 2007, Piscatway, Rutgers, New Jersey, USA.
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* E. Pichon and A. Tannenbaum. Curve segmentation using directional information, relation to pattern detection. In IEEE International Conference on Image Processing (ICIP), volume 2, pages 794-797, 2005.
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* Histology Image Segmentation using the N-Point Correlation Functions. F. Janoos, O. Irfanoglu, K. Mosaliganti, R. Machiraju, K. Huang, P.Wenzel, A. de Bruin, G. Leone. In Proceedings of International Symposium of Biomedical Imaging (ISBI) 2007, Washington DC, USA.
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* E. Pichon, C-F Westin, and A. Tannenbaum. A Hamilton-Jacobi-Bellman approach to high angular resolution diffusion tractography. In International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), pages 180-187, 2005.
 

Latest revision as of 02:32, 27 December 2007

Home < 2008 Winter Project Week:microslicer 3
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Mammary Duct of Mouse


Key Investigators

  • The Ohio State University: Kishore Mosaliganti, Raghu Machiraju
  • Kitware: Brad Davis, Stephen Aylward
  • BWH: Steve Pieper

Objective

We have developed techniques to achieve cell/nuclei segmentations in microscopic images. Often, nuclei and cells appear as overlapping structures and splitting them apart is a non-trivial problem in image analysis. The microscopy modalities of interest include light, confocal and phase-contrast microscopy.

We have also work on characterizing biological micro-structure in terms of micro-components. This helps us to perform tissue segmentations, clonal population segmentation and tracking. Once again, we propose to incorporate such techniques borrowed from material science and spatial statistics into the microscopy image analysis workflows.

The objectives of this project are to port the ITK-based cell segmentation and micro-structure characterization code to Slicer3 framework.

Approach, Plan

Our approach is based upon using image tessellations and micro-structure characterization algorithms widely used in the material science community. These algorithms help us understand biological organization in terms of component packing densities, arrangements and spatial distributions. The algorithms are detailed in the references provided below. Our main purpose at the Project Week is to work with our collaborators in order to define the scope of this project, decide upon the details of the microSlicer framework in Slicer3, and implement our current developed code into the Slicer3 framework.

Progress

We have currently implemented the cell segmentation algorithm using Geodesics Active Contours and Image-based Voronoi Tessellations in the ITK framework. We have also implemented the microstructure characterization algorithms using the N-Point Correlation functions in ITK. Currently, we are working on building a cell shape model to use in cell segmentations.



References

  • Tensor Classification of N-point Correlation Function features for Histology Tissue Segmentation. K. Mosaliganti, F. Janoos, O. Irfanoglu, R. Ridgway, R. Machiraju, K. Huang, J. Saltz, Gustavo Leone and M. Ostrowski. In review for the Special Issue on Medical Image Analysis with Applications in Biology, Journal of Medical Image Analysis.
  • Visualization of Cellular Biology Structures from Optical Microscopy Data. K. Mosaliganti, L. Cooper, R. Sharp, R. Machiraju, K. Huang and Gustavo Leone. In review at the IEEE Transactions in Visualization and Computer Graphics.
  • Geometry-driven Visualization of Microscopic Structures in Biology. K. Mosaliganti, R. Machiraju, K. Huang and Gustavo Leone. In review at the Workshop on Knowledge-Assisted Visualization, IEEE Visualization Conference, Sacramento, California, 2007.
  • Automated Quantification of Colony Growth in Clonogenic Assays. K. Mosaliganti, J. Chen, F. Janoos, R. Machiraju, W. Xia, X. Xu, K. Huang. Workshop on Medical Image Analysis with Applications in Biology, 2007, Piscatway, Rutgers, New Jersey, USA.
  • Histology Image Segmentation using the N-Point Correlation Functions. F. Janoos, O. Irfanoglu, K. Mosaliganti, R. Machiraju, K. Huang, P.Wenzel, A. de Bruin, G. Leone. In Proceedings of International Symposium of Biomedical Imaging (ISBI) 2007, Washington DC, USA.
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