Segmentation of clock drawings based on spatial and temporal features

The Clock Drawing Test (CDT) is an inexpensive and effective measure for early detection of cognitive impairment in the elderly, which is important for timely diagnosis and initiation of appropriate treatment. Currently, medical experts assess the drawings based on their judgement and a number of available scoring systems. An automatic system for assessment of CDT drawings would simultaneously decrease the waiting time for a specialist appointment and improve accessibility of the test to the patients. Published research has only started to address the problem of automatic assessment of CDT drawings and existing systems require user intervention during the segmentation of the CDT drawing into its composing parts, such as numbers and clock hands. In this paper, a new set of temporal and spatial features automatically extracted from the CDT data acquired using a graphics tablet is proposed. Consequently, a Support Vector Machine (SVM) classifier is employed to segment the CDT drawings into their elements, such as numbers and clock hands, on the basis of the extracted features. The proposed algorithm is tested on two data sets, the first set consisting of 65 drawings made by healthy people, and the second consisting of 100 drawings reproduced from actual drawings of dementia patients. The test on both data sets shows that the proposed method outperforms the current state-of-the-art method for CDT drawing segmentation

Clock drawing test digit recognition using static and dynamic features

The clock drawing test (CDT) is a standard neurological test for detection of cognitive impairment. A computerised version of the test promises to improve the accessibility of the test in addition to obtaining more detailed data about the subject's performance. Automatic handwriting recognition is one of the first stages in the analysis of the computerised test, which produces a set of recognized digits and symbols together with their positions on the clock face. Subsequently, these are used in the test scoring. This is a challenging problem because the average CDT taker has a high likelihood of cognitive impairment, and writing is one of the first functional activities to be affected. Current handwritten digit recognition system perform less well on this kind of data due to its unintelligibility. In this paper, a new system for numeral handwriting recognition in the CDT is proposed. The system is based on two complementary sources of data, namely static and dynamic features extracted from handwritten data. The main novelty of this paper is the new handwriting digit recognition system, which combines two classifiers—fuzzy k-nearest neighbour for dynamic stroke-based features and convolutional neural network for static image- based features, which can take advantage of both static and dynamic data. The proposed digit recognition system is tested on two sets of data: first, Pendigits online handwriting digits; and second, digits from the actual CDTs. The latter data set came from 65 drawings made by healthy people and 100 drawings reproduced from the drawings by dementia patients. The test on both data sets shows that the proposed combination system can outperform each classifier individually in terms of recognition accuracy, especially when assessing the handwriting of people with dementi

A software solution for recording circadian oscillator features in time-lapse live cell microscopy

BACKGROUND: Fluorescent and bioluminescent time-lapse microscopy approaches have been successfully used to investigate molecular mechanisms underlying the mammalian circadian oscillator at the single cell level. However, most of the available software and common methods based on intensity-threshold segmentation and frame-to-frame tracking are not applicable in these experiments. This is due to cell movement and dramatic changes in the fluorescent/bioluminescent reporter protein during the circadian cycle, with the lowest expression level very close to the background intensity. At present, the standard approach to analyze data sets obtained from time lapse microscopy is either manual tracking or application of generic image-processing software/dedicated tracking software. To our knowledge, these existing software solutions for manual and automatic tracking have strong limitations in tracking individual cells if their plane shifts. RESULTS: In an attempt to improve existing methodology of time-lapse tracking of a large number of moving cells, we have developed a semi-automatic software package. It extracts the trajectory of the cells by tracking theirs displacements, makes the delineation of cell nucleus or whole cell, and finally yields measurements of various features, like reporter protein expression level or cell displacement. As an example, we present here single cell circadian pattern and motility analysis of NIH3T3 mouse fibroblasts expressing a fluorescent circadian reporter protein. Using Circadian Gene Express plugin, we performed fast and nonbiased analysis of large fluorescent time lapse microscopy datasets. CONCLUSIONS: Our software solution, Circadian Gene Express (CGE), is easy to use and allows precise and semi-automatic tracking of moving cells over longer period of time. In spite of significant circadian variations in protein expression with extremely low expression levels at the valley phase, CGE allows accurate and efficient recording of large number of cell parameters, including level of reporter protein expression, velocity, direction of movement, and others. CGE proves to be useful for the analysis of widefield fluorescent microscopy datasets, as well as for bioluminescence imaging. Moreover, it might be easily adaptable for confocal image analysis by manually choosing one of the focal planes of each z-stack of the various time points of a time series. AVAILABILITY: CGE is a Java plugin for ImageJ; it is freely available at: http://bigwww.epfl.ch/sage/soft/circadian/

Automatic interpretation of clock drawings for computerised assessment of dementia

The clock drawing test (CDT) is a standard neurological test for detection of cognitive impairment. A computerised version of the test has potential to improve test accessibility and accuracy. CDT sketch interpretation is one of the first stages in the analysis of the computerised test. It produces a set of recognised digits and symbols together with their positions on the clock face. Subsequently, these are used in the test scoring. This is a challenging problem because the average CDT taker has a high likelihood of cognitive impairment, and writing is one of the first functional activities to be affected. Current interpretation systems perform less well on this kind of data due to its unintelligibility. In this thesis, a novel automatic interpretation system for CDT sketch is proposed and developed. The proposed interpretation system and all the related algorithms developed in this thesis are evaluated using a CDT data set collected for this study. This data consist of two sets, the first set consisting of 65 drawings made by healthy people, and the second consisting of 100 drawings reproduced from drawings of dementia patients. This thesis has four main contributions. The first is a conceptual model of the proposed CDT sketch interpretation system based on integrating prior knowledge of the expected CDT sketch structure and human reasoning into the drawing interpretation system. The second is a novel CDT sketch segmentation algorithm based on supervised machine learning and a new set of temporal and spatial features automatically extracted from the CDT data. The evaluation of the proposed method shows that it outperforms the current state-of-the-art method for CDT drawing segmentation. The third contribution is a new v handwritten digit recognition algorithm based on a set of static and dynamic features extracted from handwritten data. The algorithm combines two classifiers, fuzzy k-nearest neighbour’s classifier with a Convolutional Neural Network (CNN), which take advantage both of static and dynamic data representation. The proposed digit recognition algorithm is shown to outperform each classifier individually in terms of recognition accuracy. The final contribution of this study is the probabilistic Situational Bayesian Network (SBN), which is a new hierarchical probabilistic model for addressing the problem of fusing diverse data sources, such as CDT sketches created by healthy volunteers and dementia patients, in a probabilistic Bayesian network. The evaluation of the proposed SBN-based CDT sketch interpretation system on CDT data shows highly promising results, with 100% recognition accuracy for heathy CDT drawings and 97.15% for dementia data. To conclude, the proposed automatic CDT sketch interpretation system shows high accuracy in terms of recognising different sketch objects and thus paves the way for further research in dementia and clinical computer-assisted diagnosis of dementia

Thick 2D Relations for Document Understanding

We use a propositional language of qualitative rectangle relations to detect the reading order from document images. To this end, we define the notion of a document encoding rule and we analyze possible formalisms to express document encoding rules such as LATEX and SGML. Document encoding rules expressed in the propositional language of rectangles are used to build a reading order detector for document images. In order to achieve robustness and avoid brittleness when applying the system to real life document images, the notion of a thick boundary interpretation for a qualitative relation is introduced. The framework is tested on a collection of heterogeneous document images showing recall rates up to 89%

The Architect Who Lost the Ability to Imagine: The Cerebral Basis of Visual Imagery

While the loss of mental imagery following brain lesions was first described more than a century ago, the key cerebral areas involved remain elusive. Here we report neuropsychological data from an architect (PL518) who lost his ability for visual imagery following a bilateral posterior cerebral artery (PCA) stroke. We compare his profile to three other patients with bilateral PCA stroke and another architect with a large PCA lesion confined to the right hemisphere. We also compare structural images of their lesions, aiming to delineate cerebral areas selectively lesioned in acquired aphantasia. When comparing the neuropsychological profile and structural magnetic resonance imaging (MRI) for the aphantasic architect PL518 to patients with either a comparable background (an architect) or bilateral PCA lesions, we find: (1) there is a large overlap of cognitive deficits between patients, with the very notable exception of aphantasia which only occurs in PL518, and (2) there is large overlap of the patients’ lesions. The only areas of selective lesion in PL518 is a small patch in the left fusiform gyrus as well as part of the right lingual gyrus. We suggest that these areas, and perhaps in particular the region in the left fusiform gyrus, play an important role in the cerebral network involved in visual imagery

The Architect Who Lost the Ability to Imagine: The Cerebral Basis of Visual Imagery.

While the loss of mental imagery following brain lesions was first described more than a century ago, the key cerebral areas involved remain elusive. Here we report neuropsychological data from an architect (PL518) who lost his ability for visual imagery following a bilateral posterior cerebral artery (PCA) stroke. We compare his profile to three other patients with bilateral PCA stroke and another architect with a large PCA lesion confined to the right hemisphere. We also compare structural images of their lesions, aiming to delineate cerebral areas selectively lesioned in acquired aphantasia. When comparing the neuropsychological profile and structural magnetic resonance imaging (MRI) for the aphantasic architect PL518 to patients with either a comparable background (an architect) or bilateral PCA lesions, we find: (1) there is a large overlap of cognitive deficits between patients, with the very notable exception of aphantasia which only occurs in PL518, and (2) there is large overlap of the patients' lesions. The only areas of selective lesion in PL518 is a small patch in the left fusiform gyrus as well as part of the right lingual gyrus. We suggest that these areas, and perhaps in particular the region in the left fusiform gyrus, play an important role in the cerebral network involved in visual imagery