KLH Dataset I » History » Revision 6
Revision 5 (Eric Hou, 07/12/2010 01:39 PM) → Revision 6/52 (Eric Hou, 07/12/2010 01:42 PM)
h1. Anontated Dataset of Images of Keyhole Limpet Hemocyanin Particles I h2. 1. Imaging Conditions Using a Philips CM200 TEM equipped with a 2Kx2K CCD Tietz camera, images are acquired in defocus pairs at a nominal magnification of 66,000 x and a voltage of 120 KeV, using the Leginon system (Potter et al., 1999; Carragher et al., 2000). The first image (named *.001.mrc) is acquired at very near to focus (NTF) conditions (e.g., -1µm) and the second one (named *.002.mrc) at farther from focus (FFF) conditions (e.g., -3µm). The time interval between the two exposures is approximately 20s due to the time required to read out the digital image from the camera. At this magnification, the pixel size is 2.2Å at the specimen scale and the accumulated dose for high magnification image area was about 10 e/Ų. Figure 1 shows an example pair of defocus images. Click on them to see the pictures in full size. p=. (a) !fig1a-small.jpg!:http://emg.nysbc.org/prtl_data/klh/klh_1k/fig1a.jpg (b) !fig1b-small.jpg!:http://emg.nysbc.org/prtl_data/klh/klh_1k/fig1b.jpg p=. *Figure 1: An example pair of high magnification images of KLH.* (a) Near to focus (NTF) image. (b) Far from focus (FFF) image. There are at least two major advantages of using a defocus pair of images. First, by combining the two images in the defocus pair, relatively high contrast at both low and high spatial frequencies can be attained. Second, the moderately strong low-resolution signals in the FFF images make it possible for us to develop algorithms to identify particles automatically. The idea of using a defocus pair of images has been explored by several other researchers. h2. 2. Downloading High Magnification Images Image files are in MRC format. We also provide a JPEG file for each image for your convenience in viewing them. You may use one of the following options to download the set of high magnification images. * Download all image files (957MB) by one click (link removed due to the large size of the tar file). * Download only far-from-focus (FFF) images: "MRC files":http://emg.nysbc.org/prtl_data/klh/klh_1k/exposure2.mrc.tar.gz MRC files (384MB), "JPEG files":http://emg.nysbc.org/prtl_data/klh/klh_1k/exposure2.jpg.tar JPEG files (94MB). * Download only near-to-focus (NTF) images: "MRC files":http://emg.nysbc.org/prtl_data/klh/klh_1k/exposure1.mrc.tar.gz MRC files (383MB), "JPEG files":http://emg.nysbc.org/prtl_data/klh/klh_1k/exposure1.jpg.tar JPEG files (94MB). * Download images one by one. Nevertheless, there is a freely available tool, called "em2em":http://www.imagescience.de/em2em, which might be able to convert MRC files to your favorite formats. h2. 3. Positions of the Picked Particles in the Images Since the NTF image in a defocus pair covers almost the same specimen area as the FFF image, the relative distance between particles within the NTF image should be the same as that in the FFF image. Using phase correlation, we are able to accurately align the NTF image to the FFF image in a defocus pair (Zhu et al., 2001). Therefore particles in the NTF image can be then extracted using the positions of the particles identified in the FFF image shifted according to the results of the alignment. Although only the particles selected in NTF images will be passed to the later reconstruction stage, we provide in the following only the positions of particles either manually or automatically picked in the FFF images. *Figure 2: An example pair of images outlined with particles automatically picked by Selexon. Each "+" indicate a detected particle.* As we mentioned in the introduction, particle picking is an open, unresolved problem. Even for biological experts, the final picks may vary from person to person. We therefore posted here more than one set of man or machine's picks. Besides posting the particles picked by our own program Selexon, we will also post other automated picks, such as those generated by Spider, EMAN, etc., upon available. For each set of picked particles, we give a brief description of the criteria for manual picks, or the algorithm for automated picks. Links to more detailed descriptions will be provided when available. h3. Table 1: Positions of Manual Picked Particles. h3. Table 2: Positions of Automatically Picked Particles. We have built a Tcl script to compare one person/machine's pick against another's, taking the first one's pick as the truth information. Using this tool, we build a confusion matrix among the current man/machine picks, listed at the bottom of this section. h3. Table 3: Confusion matrix obtained when comparing one pick against the others. h2. 4. Sample 3D Reconstructions and a Preliminary 3D Map h3. Table 4: Sample 3D reconstructions generated using particles selected either manually or automatically. *Note: A preliminary 3-D map of the particle is also available in two different formats: MRC format and SPIDER format.* (The size of the map files are about 46 MB after being gzip'ed). h2. 5. References # Boisset, N., et al. (1998) Overabundant single-particle electron microscope views induce a three-dimensional reconstruction artifact. Ultramicroscopy *74*: 201-207. # Canny, J. (1986) A computation approach for edge detection. IEEE Trnas. Patt. Analy. and Machine Intell. *8*: 679-698. # Carragher, B., Kisseberth, N., Kriegman, D., Milligan, R. A., Potter, C. S., Pulokas, J., and Reilein, A. (2000) Leginon: An automated system for acquisition of images from vitreous ice specimens. J. Struct. Biol. *132*: 33-45. # Frank, J., et al. (1996) SPIDER and WEB: processing and visualization of images in 3D electron microscopy and related fields. J. Struct. Biol. *116(1)*: 190-9. # Potter, C. S., Chu, H., Frey, B., Green, C., Kisseberth, N., Mad-den, T. J., Miller, K. L., Nahrstedt, K., Pulokas, J., Reilein, A., Tcheng, D., Weber, D., and Carragher, B. (1999) Leginon: A system for fully automated acquisition of 1000 micrographs a day. Ultramicroscopy 77: 153-161. # Zhu, Y., B. Carragher, D. Kriegman, R. Milligan, and C. Potter (2001) Automated Identification of Filaments in Cryo-electron Microscopy Images. J. Struct. Biol. *135*: 302-312. # Zhu, Y., Carragher, B., and Potter, C. S. (2003) Automatic Particle Detection Through Efficient Hough Transforms. IEEE Transactions on Medical Imaging 22(9): 1053-1062.