Browsing by Subject "Image Interpretation, Computer-Assisted"

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Open Access
Content-based retrieval of historical Ottoman documents stored as textual images
(IEEE, 2004) Şaykol, E.; Sinop, A. K.; Güdükbay, Uğur; Ulusoy, Özgür; Çetin, A. Enis
There is an accelerating demand to access the visual content of documents stored in historical and cultural archives. Availability of electronic imaging tools and effective image processing techniques makes it feasible to process the multimedia data in large databases. In this paper, a framework for content-based retrieval of historical documents in the Ottoman Empire archives is presented. The documents are stored as textual images, which are compressed by constructing a library of symbols occurring in a document, and the symbols in the original image are then replaced with pointers into the codebook to obtain a compressed representation of the image. The features in wavelet and spatial domain based on angular and distance span of shapes are used to extract the symbols. In order to make content-based retrieval in historical archives, a query is specified as a rectangular region in an input image and the same symbol-extraction process is applied to the query region. The queries are processed on the codebook of documents and the query images are identified in the resulting documents using the pointers in textual images. The querying process does not require decompression of images. The new content-based retrieval framework is also applicable to many other document archives using different scripts.
Open Access
Current constrained voltage scaled reconstruction (CCVSR) algorithm for MR-EIT and its performance with different probing current patterns
(Institute of Physics Publishing, 2003) Birgül, Ö.; Eyüboğlu, B. M.; İder, Y. Z.
Conventional injected-current electrical impedance tomography (EIT) and magnetic resonance imaging (MRI) techniques can be combined to reconstruct high resolution true conductivity images. The magnetic flux density distribution generated by the internal current density distribution is extracted from MR phase images. This information is used to form a fine detailed conductivity image using an Ohm's law based update equation. The reconstructed conductivity image is assumed to differ from the true image by a scale factor. EIT surface potential measurements are then used to scale the reconstructed image in order to find the true conductivity values. This process is iterated until a stopping criterion is met. Several simulations are carried out for opposite and cosine current injection patterns to select the best current injection pattern for a 2D thorax model. The contrast resolution and accuracy of the proposed algorithm are also studied. In all simulation studies, realistic noise models for voltage and magnetic flux density measurements are used. It is shown that, in contrast to the conventional EIT techniques, the proposed method has the capability of reconstructing conductivity images with uniform and high spatial resolution. The spatial resolution is limited by the larger element size of the finite element mesh and twice the magnetic resonance image pixel size.
Open Access
Malignant-lesion segmentation using 4D co-occurrence texture analysis applied to dynamic contrast-enhanced magnetic resonance breast image data
(2007) Woods, B.J.; Clymer, B.D.; Kurc, T.; Heverhagen J.T.; Stevens, R.; Orsdemir, A.; Bulan O.; Knopp, M.V.
Purpose: To investigate the use of four-dimensional (4D) co-occurrence-based texture analysis to distinguish between nonmalignant and malignant tissues in dynamic contrast-enhanced (DCE) MR images. Materials and Methods: 4D texture analysis was performedon DCE-MRI data sets of breast lesions. A model-free neural network-based classification system assigned each voxel a "nonmalignant" or "malignant" label based on the textural features. The classification results were compared via receiver operating characteristic (ROC) curve analysis with the manual lesion segmentation produced by two radiologists (observers 1 and 2). Results: The mean sensitivity and specificity of the classifier agreed with the mean observer 2 performance when compared with segmentations by observer 1 for a 95% confidence interval, using a two-sided t-test with α = 0.05. The results show that an area under the ROC curve (Az) of 0.99948, 0.99867, and 0.99957 can be achieved by comparing the classifier vs. observer 1, classifier vs. union of both observers, and classifier vs. intersection of both observers, respectively. Conclusion: This study shows that a neural network classifier based on 4D texture analysis inputs can achieve a performance comparable to that achieved by human observers, and that further research in this area is warranted. © 2007 Wiley-Liss, Inc.
Open Access
Reduced field-of-view DWI with robust fat suppression and unrestricted slice coverage using tilted 2D RF excitation
(John Wiley and Sons Inc., 2016) Banerjee, S.; Nishimura, D. G.; Shankaranarayanan, A.; Saritas, E. U.
Purpose: Reduced field-of-view (rFOV) diffusion-weighted imaging (DWI) using 2D echo-planar radiofrequency (2DRF) excitation has been widely and successfully applied in clinical settings. The purpose of this work is to further improve its clinical utility by overcoming slice coverage limitations without any scan time penalty while providing robust fat suppression. Theory and Methods: During multislice imaging with 2DRF pulses, periodic sidelobes in the slice direction cause partial saturation, limiting the slice coverage. In this work, a tilting of the excitation plane is proposed to push the sidelobes out of the imaging section while preserving robust fat suppression. The 2DRF pulse is designed using Shinnar-Le Roux algorithm on a rotated excitation k-space. The performance of the method is validated via simulations, phantom experiments, and high in-plane resolution in vivo DWI of the spinal cord. Results: Results show that rFOV DWI using the tilted 2DRF pulse provides increased signal-to-noise ratio, extended coverage, and robust fat suppression, without any scan time penalty. Conclusion: Using a tilted 2DRF excitation, a high-resolution rFOV DWI method with robust fat suppression and unrestricted slice coverage is presented. This method will be beneficial in clinical applications needing large slice coverage, for example, axial imaging of the spine, prostate, or breast. Magn Reson Med 76:1668–1676, 2016. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine