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There exist a number of general purpose lossless compression programs, e.g., gzip and compress. These programs seldom make a very efficient compression of neuroimaging data. SmallTime [Cohen, 2000], (http://www.brainmapping.org/SmallTime/) is a program specifically designed for MRI sequences and works with differences of 16 bit images. Other algorithms have been described [Thiran et al., 1996,Adamson, 2002,Wu and Forchhammer, 2004], some based on wavelets.
The DICOM standard specifies lossless compression via the JPEG-LS (ISO 14495-1) and JPEG 2000 (15444-1) standards.
Most databases management systems (DBMS) are relational. These consist of tables and links between tables. A wide-spread query language is SQL. Early relation database management systems (RDBMS) had very simple types: integer, floats, string, etc. and would not represent 3D neuroimaging data well. Extensible DBMS such as so-called object-relation DBMS (ORDBMS) augment the ordinary relation DBMS with (complex) user-defined types (objects). One of the first ORDBMS was POSTGRES, see, e.g., [Stonebraker and Kemnitz, 1991]. This database was commercialized by Illustra, which in December 1995 was bought by Informix. Informix was in turn bought by IBM in April 2001. Another extensible DBMS is/was the Starburst DBMS, see, e.g., [Lohman et al., 1991]. It's query language ``hydrogen'' is based on SQL. Open source ORDBMS version exists with PostgreSQL (http://www.postgresql.org/). EXODUS is yet another extensible database system (Carey et al, 1991).
Content-based image retrieval (CBRI) uses image feature, e.g., for finding similar images in a database.
One finds descriptions of general systems (not necessarily for neuroscience images) with Photobook [Pentland et al., 1993], http://vismod.media.mit.edu/vismod/demos/photobook/, QBIC [Flickner et al., 1995] and Virage [Bach et al., 1996].
[Tagare et al., 1997] discusses some of the issues in content-based retrieval approaches in medical database.
Clinical medical image databases are often refered to as ``picture archiving and communication systems'' PACS [Huang and Taira, 1992]. There are probably many commercial systems in this area. One such is EasyViz by Medical Insight.
[Bjaalie, 2002] discusses neuroscientific databases, particularly the NeSys database, and [Kötter, 2002] is an edited book about neuroscience databases. Other methods of image retrieval are presented in [Liu and Dellaert, 1998a,Liu and Dellaert, 1998b,Liu et al., 1998,Liu et al., 2001b,Liu et al., 2002].
A review of databases for neuroimaging appears in [Diallo et al., 1999b,Diallo et al., 1997b].
Image archives and data sharing in research oriented brain mapping have been discussed in a number of articles [Chicurel, 2000,Aldhous, 2000,Marshall, 2000,Nature editorial, 2000,Cohen et al., 2001,Barinaga, 2003,Toga, 2002b]. This discussion was initiated after suggestion for required submission to the fMRI Data center [Van Horn et al., 2001a,Van Horn and Gazzaniga, 2002]. A review of neuroscience atlas/databases is [Van Essen, 2002].
[Wang et al., 2006] implemented content-based image retrieval on fMRI contrast maps with a combination of wavelets and image feature extraction. [Cho, 2005] discusses a retrieval system with fMRI and single unit recordings. The CCVT tool also enables query-by-example [Cornea, 2005]. NeuroServ and NIRV are software tools mentioned by [Carley-Spencer et al., 2006]. Table 17 lists other content-based image retrieval systems in the neuroimaging context.
| Name | Area | Features and description | Reference |
| -- | Lymphoproliferative disorders | Shape (Fourier descriptors), texture (MRSAR), color. Image-based queries, trained classifier | [Comaniciu et al., 1999,Comaniciu et al., 1998]. |
| -- | (Synthetic) MRI | Topographical relations, size, roundness, orientation. Images represented as ``attributed relational graphs'' | [Petrakis and Faloutsos, 1997,Petrakis, 2002] |
| Brede Database | Functional neuroimaging | Finding nearest Talairach coordinate to a given location. ``Grey-level'' volume from voxelized locations. Determination of related volumes in the Brede Database. | [Nielsen and Hansen, 2004b,Nielsen, 2003,Nielsen and Hansen, 2002b], http://hendrix.imm.dtu.dk/services/jerne/brede/ |
| Dermatlas | Clinical dermatological images (photographs, histological, biopsy) | Text-based (keywords, diagnosis, body site, pigmentation, ...). Internet-based with collaboration (users are able to submit new images). | http://www.dermatlas.org |
I Cnet |
MRI, ... | ROI (location, shape, size: roundness, compactness, area, orientation), texture (maximum probability, angular second moment, contrast, inverse difference moment, entropy, correlation, variance, cluster shade, diagonal moment, k statistics, fractal signature). Internet-based. | [Orphanoudakis et al., 1996] |
| (Jerne, ``Related volumes'') | Functional neuroimaging | ``Grey-level'' volume: Voxelized locations. Determination of related volumes in the BrainMapTM database by voxelization | [Nielsen and Hansen, 2004b,Nielsen and Hansen, 2002b], http://hendrix.imm.dtu.dk/services/jerne/ninf/relvol.html |
| (KMeD) | MRI, x-ray, ... | Shape, size, texture, topological relations. Spatial temporal query language: (KSTL). Features in a hierarchical structure | [Chu et al., 1998] |
| MIMS | Image descriptors: file type (JPEG, GIF), device (MRI, radio), domain (radiology), ... Topographical relations. Java Internet-based. Stores images voice reports and general (text) data. Thesaurus on descriptors | [Chbeir et al., 1999,Chbeir et al., 2000] | |
| WebMIRS | X-rays | http://archive.nlm.nih.gov/proj/webmirs/ | |
| xBrain | Functional neuroimaging | Search for nearest Talairach coordinates. | http://www.xbrain.org/ |
In neuroimaging contexts the neuroimages themselves do not only have to be databased but all the associated data as well. These associated data might be subject information such as demographic, clinical and genetic. Complications for the construction of databases for such data might be extensibility and the secure transportation of data across multiple sites. Thoughts on the problems for such systems appear, e.g., in [Bockholt et al., 2006] (``The MIND Institute'') and [Keator et al., 2006] (``XCEDE'', ``BIRN'' ). Some form of relational database for neuroscience data is described by [Rudowsky et al., 2004].
Unique identifiers for authors are partially implemented in the Brede Database [Nielsen, 2003]. This has also been suggested by [Falagas, 2006] for more broader application.
The Blue Brain Project aims at modeling the neocortical column of the somatosensory cortex of young rats based on the NEURON program and using supercomputers, and is introduced in [Markram, 2006].
OSIRIS [Ligier et al., 1994]: This seems to be an image analysis/processing program rather than a database. http://www.expasy.ch/www/UIN/html1/projects/osiris/osiris.html
M. Bota and M. A. Arbib. The NeuroHomology Database. Neurocomputing, 38-40, pp. 1627-1631, 2001.
[Miller et al., 2001,Gardner et al., 2001b].
[Guimond et al., 1997]: retrieval of corresponding brain structures from a database of medical images.
Database Challenges and Solutions in Neuroscientific Applications (1997). Ali E. Dashti, Shahram Ghandeharizadeh, James Stone, Larry W. Swanson, Richard H. Thompson