Designing reliable and fast segmentation algorithms of ancient documents has been a topic of major interest for many libraries and the prime issue of research in the document analysis community. Thus, we propose in this article a fast ancient document enhancement and segmentation algorithm based on using Simple Linear Iterative Clustering (SLIC) superpixels and Gabor descriptors in a multi-scale approach. Firstly, in order to obtain enhanced backgrounds of noisy ancient documents, a novel foreground/background segmentation algorithm based on SLIC superpixels, is introduced. Once, the SLIC technique is carried out, the background and foreground superpixels are classified. Then, an enhanced and non-noisy background is achieved after processing the background superpixels. Subsequently, Gabor descriptors are only extracted from the selected foreground superpixels of the enhanced gray-level ancient book document images by adopting a multi-scale approach. Finally, for ancient document image segmentation, a foreground superpixel clustering task is performed by partitioning Gabor-based feature sets into compact and well-separated clusters in the feature space. The proposed algorithm does not assume any <i>a priori</i> information regarding document image content and structure and provides interesting results on a large corpus of ancient documents. Qualitative and numerical experiments are given to demonstrate the enhancement and segmentation quality.
Recent progress in the digitization of heterogeneous collections of ancient documents has rekindled new challenges
in information retrieval in digital libraries and document layout analysis. Therefore, in order to control
the quality of historical document image digitization and to meet the need of a characterization of their content
using intermediate level metadata (between image and document structure), we propose a fast automatic layout
segmentation of old document images based on five descriptors. Those descriptors, based on the autocorrelation
function, are obtained by multiresolution analysis and used afterwards in a specific clustering method. The
method proposed in this article has the advantage that it is performed without any hypothesis on the document
structure, either about the document model (physical structure), or the typographical parameters (logical
structure). It is also parameter-free since it automatically adapts to the image content. In this paper, firstly,
we detail our proposal to characterize the content of old documents by extracting the autocorrelation features
in the different areas of a page and at several resolutions. Then, we show that is possible to automatically find
the homogeneous regions defined by similar indices of autocorrelation without knowledge about the number of
clusters using adapted hierarchical ascendant classification and consensus clustering approaches. To assess our
method, we apply our algorithm on 316 old document images, which encompass six centuries (1200-1900) of
French history, in order to demonstrate the performance of our proposal in terms of segmentation and characterization
of heterogeneous corpus content. Moreover, we define a new evaluation metric, the homogeneity measure,
which aims at evaluating the segmentation and characterization accuracy of our methodology. We find a 85%
of mean homogeneity accuracy. Those results help to represent a document by a hierarchy of layout structure
and content, and to define one or more signatures for each page, on the basis of a hierarchical representation of
homogeneous blocks and their topology.
Deep brain stimulation (DBS) is used to reduce the motor symptoms such as rigidity or bradykinesia, in patients
with Parkinson's disease (PD). The Subthalamic Nucleus (STN) has emerged as prime target of DBS in idiopathic PD.
However, DBS surgery is a difficult procedure requiring the exact positioning of electrodes in the pre-operative selected
targets. This positioning is usually planned using patients' pre-operative images, along with digital atlases, assuming that
electrode's trajectory is linear. However, it has been demonstrated that anatomical brain deformations induce electrode's
deformations resulting in errors in the intra-operative targeting stage. In order to meet the need of a higher degree of
placement accuracy and to help constructing a computer-aided-placement tool, we studied the electrodes' deformation in
regards to patients' clinical data (i.e., sex, mean PD duration and brain atrophy index). Firstly, we presented an automatic
algorithm for the segmentation of electrode's axis from post-operative CT images, which aims to localize the electrodes'
stimulated contacts. To assess our method, we applied our algorithm on 25 patients who had undergone bilateral STNDBS.
We found a placement error of 0.91±0.38 mm. Then, from the segmented axis, we quantitatively analyzed the
electrodes' curvature and correlated it with patients' clinical data. We found a positive significant correlation between
mean curvature index of the electrode and brain atrophy index for male patients and between mean curvature index of the
electrode and mean PD duration for female patients. These results help understanding DBS electrode' deformations and
would help ensuring better anticipation of electrodes' placement.