Convolutional neural network propagation on electroencephalographic scalograms for detection of schizophrenia.

OBJECTIVE: Electroencephalographic analysis (EEG) has emerged as a powerful tool for brain state interpretation. Studies have shown distinct deviances of patients with schizophrenia in EEG activation at specific frequency bands. METHODS: Evidence is presented for the validation of a Convolutional Neural Network (CNN) model using transfer learning for scalp EEGs of patients and controls during the performance of a speeded sensorimotor task and a working memory task. First, we trained a CNN on EEG data of 41 schizophrenia patients (SCZ) and 31 healthy controls (HC). Secondly, we used a pretrained model for training. Both models were tested in an external validation set of 15 SCZ, 16 HC, and 12 first-degree relatives. RESULTS: Using the layer-wise relevance propagation on the classification decision, a heatmap was produced for each subject, specifying the pixel-wise relevance. The CNN model resulted in the first case in a balanced accuracy of 63.7% and 81.5% in the second case, on the external validation test 64.5% and 83.2%, respectively. CONCLUSIONS: The theta and alpha frequency bands of the EEG signals had significant relevance to the CNN classification decision and predict the first-degree relatives indicating potential heritable functional deviances. SIGNIFICANCE: The proposed methodology results in important advancements for the identification of biomarkers in schizophrenia heritability.

Assessing the performance of four different categories of histological criteria in brain tumours grading by means of a computer-aided diagnosis image analysis system.

Brain tumours are considered one of the most lethal and difficult to treat forms of cancer, with unknown aetiology and lack of any realistic screening. In this study, we examine, whether the combination of descriptive criteria, used by expert histopathologists in assessing histologic tissue samples, and quantitative image analysis features may improve the diagnostic accuracy of brain tumour grading. Data comprised 61 cases of brain cancers (astrocytomas, oligodendrogliomas, meningiomas) collected from the archives of the University Hospital of Patras, Greece. Incorporating physician's descriptive criteria and image analysis's quantitative features into a discriminant function, a computer-aided diagnosis system was designed for discriminating low-grade from high-grade brain tumours. Physician's descriptive features, when solely used in the system, proved of high discrimination accuracy (93.4%). When verbal descriptive features were combined with quantitative image analysis features in the system, discrimination accuracy improved to 98.4%. The generalization of the proposed system to unseen data converged to an overall prediction accuracy of 86.7% ± 5.4%. Considering that histological grading affects treatment selection and diagnostic errors may be notable in clinical practice, the utilization of the proposed system may safeguard against diagnostic misinterpretations in every day clinical practice.

Volumetric difference evaluation of registered three-dimensional pre-operative and post-operative CT dental data.

OBJECTIVE: The purpose of this study is to propose a complete methodology for automatically registering three-dimensional (3D) pre-operative and post-operative CT scan dental volumes as well as to provide a toolset for quantifying and evaluating their volumetric differences. METHODS: The proposed methodology was applied to cone beam CT (CBCT) data from 20 patients in order to assess the volume of augmented bone in the alveolar region. In each case, the pre-operative and post-operative data were registered using a 3D affine-based scheme. The performance of the 3D registration algorithm was evaluated by measuring the average distance between the edges of the registered sets. The differences between the registered sets were assessed through 3D subtraction radiography. The volume of the differences was finally evaluated by defining regions of interest in each slice of the subtracted 3D data and by combining all respective slices to model the desired volume of interest. The effectiveness of the algorithm was verified by applying it to several reference standard-shaped objects with known volumes. RESULTS: Satisfactory alignment was achieved as a low average offset of 1.483 ± 1.558 mm was recorded between the edges of the registered sets. Moreover, the estimated volumes closely matched the volumes of the reference objects used for verification, as the recorded volume differences were less than 0.4 mm(3) in all cases. CONCLUSION: The proposed method allows for automatic registration of 3D CBCT data sets and the volumetric assessment of their differences in particular areas of interest. The proposed approach provides accurate volumetric measurements in three dimensions, requiring minimal user interaction.

A contrast correction method for dental images based on histogram registration.

Contrast correction is often required in digital subtraction radiography when comparing medical data acquired over different time periods owing to dissimilarities in the acquisition process. This paper focuses on dental radiographs and introduces a novel approach for correcting the contrast in dental image pairs. The proposed method modifies the subject images by applying typical registration techniques on their histograms. The proposed histogram registration method reshapes the histograms of the two subject images in such a way that these images are matched in terms of their contrast deviation. The method was extensively tested over 4 sets of dental images, consisting of 72 registered dental image pairs with unknown contrast differences as well as 20 dental pairs with known contrast differences. The proposed method was directly compared against the well-known histogram-based contrast correction method. The two methods were qualitatively and quantitatively evaluated for all 92 available dental image pairs. The two methods were compared in terms of the contrast root mean square difference between the reference image and the corrected image in each case. The obtained results were also verified statistically using appropriate t-tests in each set. The proposed method exhibited superior performance compared with the well-established method, in terms of the contrast root mean square difference between the reference and the corrected images. After suitable statistical analysis, it was deduced that the performance advantage of the proposed approach was statistically significant.

Comparison of fractal dimension estimation algorithms for epileptic seizure onset detection.

Fractal dimension (FD) is a natural measure of the irregularity of a curve. In this study the performances of three waveform FD estimation algorithms (i.e. Katz's, Higuchi's and the k-nearest neighbour (k-NN) algorithm) were compared in terms of their ability to detect the onset of epileptic seizures in scalp electroencephalogram (EEG). The selection of parameters involved in FD estimation, evaluation of the accuracy of the different algorithms and assessment of their robustness in the presence of noise were performed based on synthetic signals of known FD. When applied to scalp EEG data, Katz's and Higuchi's algorithms were found to be incapable of producing consistent changes of a single type (either a drop or an increase) during seizures. On the other hand, the k-NN algorithm produced a drop, starting close to the seizure onset, in most seizures of all patients. The k-NN algorithm outperformed both Katz's and Higuchi's algorithms in terms of robustness in the presence of noise and seizure onset detection ability. The seizure detection methodology, based on the k-NN algorithm, yielded in the training data set a sensitivity of 100% with 10.10 s mean detection delay and a false positive rate of 0.27 h(-1), while the corresponding values in the testing data set were 100%, 8.82 s and 0.42 h(-1), respectively. The above detection results compare favourably to those of other seizure onset detection methodologies applied to scalp EEG in the literature. The methodology described, based on the k-NN algorithm, appears to be promising for the detection of the onset of epileptic seizures based on scalp EEG.

Automatic correspondence on medical images: a comparative study of four methods for allocating corresponding points.

The accurate estimation of point correspondences is often required in a wide variety of medical image-processing applications. Numerous point correspondence methods have been proposed in this field, each exhibiting its own characteristics, strengths, and weaknesses. This paper presents a comprehensive comparison of four automatic methods for allocating corresponding points, namely the template-matching technique, the iterative closest points approach, the correspondence by sensitivity to movement scheme, and the self-organizing maps algorithm. Initially, the four correspondence methods are described focusing on their distinct characteristics and their parameter selection for common comparisons. The performance of the four methods is then qualitatively and quantitatively compared over a total of 132 two-dimensional image pairs divided into eight sets. The sets comprise of pairs of images obtained using controlled geometry protocols (affine and sinusoidal transforms) and pairs of images subject to unknown transformations. The four methods are statistically evaluated pairwise on all image pairs and individually in terms of specific features of merit based on the correspondence accuracy as well as the registration accuracy. After assessing these evaluation criteria for each method, it was deduced that the self-organizing maps approach outperformed in most cases the other three methods in comparison.

Automatic correspondence using the enhanced hexagonal centre-based inner search algorithm for point-based dental image registration.

OBJECTIVES: In this paper, the enhanced hexagonal centre-based inner search (EHCBIS) algorithm, for automatic point correspondence, is proposed for dental image registration. METHODS: The presented algorithm is incorporated within a general registration scheme, which is based on extracting a set of candidate points on the reference image, finding their corresponding points in the image to be transformed (float image) using the proposed algorithm and applying a suitable geometrical transformation towards automatic registration. The performance of the proposed algorithm is evaluated against three well-known methods for automatic correspondence, the self-organizing maps, the automatic extraction of corresponding points and the trimmed iterative closest point method, in terms of registration accuracy. RESULTS: Qualitative and quantitative results on registering 123 dental pairs show that the proposed algorithm outperforms the other methods for automatic correspondence with or without the presence of noise. CONCLUSIONS: The EHCBIS method is capable of defining automatically corresponding points in dental image pairs. It can be incorporated within a general scheme for point-based registration of dental radiographs acquired with or without rigorous a priori standardization. The applied projective transformation provides a reliable model for registering intraoral radiographs. The methodology does not require any segmentation prior to alignment providing subtraction radiographs and fused images for clinical evaluation regarding the evolution of a disease or the response to a therapeutic scheme.

Application of the Kohonen network for automatic point correspondence in retinal images.

In this paper, an algorithm for automatic point correspondence is proposed towards retinal image registration. Given a pair of corresponding retinal images and a set of bifurcations or other salient points in one of the images, the algorithm detects effectively the set of corresponding points in the second image, by exploiting the properties of Kohonen's Self Organizing Maps and embedding them in a stochastic optimization procedure. The proposed algorithm was tested on 20 unimodal retinal pairs and the obtained results show an enhanced performance in terms of accuracy and robustness compared to the existing algorithm, on which it is based.

Preliminary study on the association of vessel diameter variation and glaucoma.

In this paper, the relation between retinal vessel diameters and glaucoma by means of advanced image processing techniques is examined. Eighteen (18) patients with ocular hypertension who converted to early glaucoma (converters) and nineteen (19) patients with ocular hypertension who remain stable (non-converters) were imaged with a fundus camera at regular intervals. The first acquired image serves as the reference image, which is compared to each follow-up image. The proposed methodology involves the extraction of vessel centerlines by means of differential calculus, the geometrical alignment (registration) of the images using the chamfer matching algorithm, the estimation of vessel diameters by fitting a Gaussian function to intensity profiles. The statistical analysis of the variations of the vessels diameters revealed a significant difference between the two populations. This difference indicates a probable association between glaucoma and variations of the vessel diameter.

Automatic correspondence using the enhanced hexagonal center-based inner search algorithm for point-based dental image registration.

In this paper a modified version of the center-based inner search algorithm, the enhanced hexagonal center-based inner search algorithm, for automatic point correspondence is proposed towards dental registration. The modified algorithm is incorporated within a general registration scheme which is based on extracting a set of candidate points on the reference image, finding their corresponding points in the other image (float image) using the proposed algorithm and applying an affine geometrical transformation towards automatic registration. The performance of the proposed algorithm is evaluated against a well-known method for automatic correspondence, in terms of the registration accuracy. Qualitative and quantitative results on registering 24 dental pairs showed that the proposed algorithm outperforms the other method for automatic correspondence.

Registration of retinal angiograms using self organizing maps.

In this paper, an automatic method for registering multimodal retinal images is presented. The method consists of three steps: the vessel centerline detection and extraction of bifurcation points only in the reference image, the automatic correspondence of bifurcation points in the two images using a novel implementation of the Self Organized Maps (SOMs) and the extraction of the parameters of the affine transform using the previously obtained correspondences. The proposed registration algorithm was tested on 24 multimodal retinal pairs and the obtained results show an advantageous performance in terms of accuracy with respect to the manual registration.

A digital subtraction radiography scheme based on automatic multiresolution registration.

OBJECTIVES: To establish a digital subtraction radiography scheme for aligning clinical in vivo radiographs based on the implementations of an automatic geometric registration method and a contrast correction technique. METHODS: Thirty-five pairs of in vivo dental radiographs from four clinical studies were used in this work. First, each image pair was automatically aligned by applying a multiresolution registration strategy using the affine transformation followed by the implementation of the projective transformation at full resolution. Then, a contrast correction technique was applied in order to produce subtraction radiographs and fused images for further clinical evaluation. The performance of the proposed registration method was assessed against a manual method based on the projective transformation. RESULTS: The qualitative assessment of the experiments based on visual inspection has shown advantageous performance of the proposed automatic registration method against the manual method. Furthermore, the quantitative analysis showed statistical difference in terms of the root mean square (RMS) error estimated over the whole images and specific regions of interest. CONCLUSIONS: The proposed automatic geometric registration method is capable of aligning radiographs acquired with or without rigorous a priori standardization. The methodology is pixel-based and does not require the application of any segmentation process prior to alignment. The employed projective transformation provides a reliable model for registering intraoral radiographs. The implemented contrast correction technique sequentially applied provides subtraction radiographs and fused images for clinical evaluation regarding the evolution of a disease or the response to a therapeutic scheme.

Impaired P600 in neuroleptic naive patients with first-episode schizophrenia.

Deficits of working memory (WM) are recognized as an important pathological feature in schizophrenia. Since the P600 component of event related potentials has been hypothesized that represents aspects of second-pass parsing processes of information processing, and is related to WM, the present study focuses on P600 elicited during a WM test in drug-naive first-episode schizophrenics (FES) compared to healthy controls. We examined 16 drug-naive first-episode schizophrenic patients and 23 healthy controls matched for age and sex. Compared with controls schizophrenic patients showed reduced P600 amplitude on left temporoparietal region and increased P600 amplitude on left occipital region. With regard to the latency, the patients exhibited significantly prolongation on right temporoparietal region. The obtained pattern of differences classified correctly 89.20% of patients. Memory performance of patients was also significantly impaired relative to controls. Our results suggest that second-pass parsing process of information processing, as indexed by P600, elicited during a WM test, is impaired in FES. Moreover, these findings lend support to the view that the auditory WM in schizophrenia involves or affects a circuitry including temporoparietal and occipital brain areas.

Abnormal P600 in heroin addicts with prolonged abstinence elicited during a working memory test.

The P600 component of event-related potentials, believed to be generated by anterior cingulate gyrus and basal ganglia, is considered as an index of aspects of second-pass parsing processes of information processing, having much in common with working memory (WM) systems. Moreover, dysfunction of these brain structures as well as WM deficits have been implicated in the pathophysiology of opioid addicts. The present study is focused on P600 elicited during a WM test in twenty heroin addicts with prolonged abstinence compared with an equal number of healthy controls. The results showed significantly prolonged latencies at right hemisphere, specifically at Fp2 abduction. Moreover, memory performance of patients did not differ from that of normal controls. These findings may indicate that abstinent heroin addicts manifest abnormal aspects of second-pass parsing processes as are reflected by the P600 latencies, elicited during a WM test. Additionally, the P600 might serve as a valuable investigative tool for a more comprehensive understanding of the neurobiological substrate of drug abuse.