Synapse classification and localization in Electron Micrographs

Classification and detection of biological structures in Electron Micrographs (EM) is a relatively new large scale image analysis problem. The primary challenges are in modeling diverse visual characteristics and development of scalable techniques. In this paper we propose novel methods for synapse detection and localization, an important problem in connectomics. We first propose an attribute based descriptor for characterizing synaptic junctions. These descriptors are task specific, low dimensional and can be scaled across large image sizes. Subsequently, techniques for fast localization of these junctions are proposed. Experimental results on images acquired from a mammalian retinal tissue compare favorably with state of the art descriptors used for object detection.

[1]  Vincent Lepetit,et al.  A Fully Automated Approach to Segmentation of Irregularly Shaped Cellular Structures in EM Images , 2010, MICCAI.

[2]  E. Díaz,et al.  Automatic Detection of Large Dense-Core Vesicles in Secretory Cells and Statistical Analysis of Their Intracellular Distribution , 2010, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[3]  Cordelia Schmid,et al.  Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[4]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[6]  Luc Van Gool,et al.  The Pascal Visual Object Classes (VOC) Challenge , 2010, International Journal of Computer Vision.

[7]  Andrew Zisserman,et al.  Texture classification: are filter banks necessary? , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[8]  T. Tasdizen,et al.  The Viking Viewer for Connectomics : Scalable Multiuser Annotation and Summarization of Large Volume Datasets Abbreviated Title : The Viking Viewer for Connectomics , 2010 .

[9]  Fred A. Hamprecht,et al.  Automated Detection and Segmentation of Synaptic Contacts in Nearly Isotropic Serial Electron Microscopy Images , 2011, PloS one.

[10]  Manik Varma,et al.  More generality in efficient multiple kernel learning , 2009, ICML '09.

[11]  Joachim M. Buhmann,et al.  Neuron geometry extraction by perceptual grouping in ssTEM images , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[12]  JR ANDERSON,et al.  The Viking viewer for connectomics: scalable multi-user annotation and summarization of large volume data sets , 2011, Journal of microscopy.

[13]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[14]  B. S. Manjunath,et al.  Texture Features for Browsing and Retrieval of Image Data , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Jiri Matas,et al.  Robust wide-baseline stereo from maximally stable extremal regions , 2004, Image Vis. Comput..

[16]  B. S. Manjunath,et al.  Multiple Structure Tracing in 3D Electron Micrographs , 2011, MICCAI.

[17]  Antonio Torralba,et al.  Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope , 2001, International Journal of Computer Vision.

[18]  Karel J. Zuiderveld,et al.  Contrast Limited Adaptive Histogram Equalization , 1994, Graphics Gems.