Shape drawing tests are widely used by practitioners to assess the neuropsychological conditions of patients. Most of these neuropsychological figure drawing tests comprise a set of figures drawn on a single sheet of paper which are inspected to analyze the presence or absence of certain properties and are scored accordingly. An automated scoring system for such a test requires the extraction and identification of a particular shape from the set of figures as a vital preprocessing step. This paper presents a system for effective segmentation and recognition of shapes for a well-known clinical test, the Bender Gestalt Test (BGT). The segmentation is based on connected component analysis, morphological processing and spatial clustering while the recognition is carried out using shape context matching. Experiments carried out on offline images of hand drawn samples contributed by different subjects realize promising segmentation and classification results validating the ideas put forward in this study.
This paper aims to develop a computer aided diagnosis (CAD) based on a two-step methodology to register and analyze pairs of temporal mammograms. The concept of "medical file", including all the previous medical information on a patient, enables joint analysis of different acquisitions taken at different times, and the detection of significant modifications. The developed registration method aims to superimpose at best the different anatomical structures of the breast. The registration is designed in order to get rid of deformation undergone by the acquisition process while preserving those due to breast changes indicative of malignancy. In order to reach this goal, a referent image is computed from control points based on anatomical features that are extracted automatically. Then the second image of the couple is realigned on the referent image, using a coarse-to-fine approach according to expert knowledge that allows both rigid and non-rigid transforms. The joint analysis detects the evolution between two images representing the same scene. In order to achieve this, it is important to know the registration error limits in order to adapt the observation scale. The approach used in this paper is based on an image sparse representation. Decomposed in regular patterns, the images are analyzed under a new angle. The evolution detection problem has many practical applications, especially in medical images. The CAD is evaluated using recall and precision of differences in mammograms.
This paper aims to detect the evolution between two images representing the same scene. The evolution detection
problem has many practical applications, especially in medical images. Indeed, the concept of a patient “file” implies the joint analysis of different acquisitions taken at different times, and the detection of significant modifications. The
research presented in this paper is carried out within the application context of the development of computer assisted
diagnosis (CAD) applied to mammograms. It is performed on already registered pair of images. As the registration is
never perfect, we must develop a comparison method sufficiently adapted to detect real small differences between
comparable tissues. In many applications, the assessment of similarity used during the registration step is also used for
the interpretation step that yields to prompt suspicious regions. In our case registration is assumed to match the spatial coordinates of similar anatomical elements. In this paper, in order to process the medical images at tissue level, the image representation is based on elementary patterns, therefore seeking patterns, not pixels. Besides, as the studied images have low entropy, the decomposed signal is expressed in a parsimonious way. Parsimonious representations are known to help extract the significant structures of a signal, and generate a compact version of the data. This change of representation should allow us to compare the studied images in a short time, thanks to the low weight of the images thus represented, while maintaining a good representativeness. The good precision of our results show the approach efficiency.