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Objectives: Idiopathic intracranial hypertension (IIH) is characterized by increased intracranial pressure. We investigated the optic nerve, Meckel's cavity, internal carotid artery (ICA) and pituitary findings of IIH by Cranial Magnetic Resonance Imaging (MRI). Methods: Cranial MRI images of 35 adult patients with IIH and 35 adult subjects with normal cranial MRI results (control) were evaluated. Optic nerve diameter (OND), optic nerve sheat diameter (ONSD), OND/ONSD ratio, ON tortuosity, ON protrusion, posterior scleral flattening, ICA transverse diameter, CSF distance in Meckel's cavity and ICA transverse diameter/CSF distance in Meckel's cavity ratio, and pituitary gland measurements (height and transverse dimension; and Optic chiasm- pituitary gland distance) were measured. Results: OND and ONSD of the IIH group were significantly higher than those of the control groups at anterior and posterior measurements (p < 0.05). OND/ONSD ratio of the IIH group was lower at anterior measurement; and higher at the posterior measurement than the control group (p < 0.05). Right ICA transverse diameter and bilateral CSF distance in Meckel's cavity of the IIH group were higher than those of the control. Optic chiasm- pituitary gland distance of the IIH group was significantly higher than that of the control group (p < 0.05). Conclusion: OND/ONSD ratio is different in anterior and posterior measurements. So we recommend measuring OND and ONSD separately in IIH patients to use in the clinical practice. Similarly, optic chiasm-pituitary gland distance is also another point to note for IIH patients on MRI. Our paper adds new approach to IIH in terms of OND/ONSD ratio. [ABSTRACT FROM AUTHOR]

Glaucoma is one of the leading cause of blindness for over 60 million people around the world. Since a cure for glaucoma doesn't yet exist, early screening and diagnosis become critical for the prevention of the disease. Optic disc and optic cup evaluation are one of the preeminent steps for glaucoma diagnosis. A novel approach is developed in this paper for the identification of glaucoma using a segmentation based approach on the optic disc and optic cup. The Dhristi dataset was used to help improve performance on a small dataset. A custom UNET++ model is built for the segmentation task by tuning the hyper-parameters in addition to a custom loss function. The developed loss function helps tackle the class imbalance occurring due to small size of the optic nerve head. The proposed approach achieves 96% accuracy in classifying glaucomatous and non-glaucomatous images based on clinical feature identification. The improvised model is able to achieve state-of-art results for Intersection over Union (IOU) scores, 0.9477 for optic disc and 0.9321 for optic cup, along with providing an enhancement in reducing the training time. The model was tested on publicly available datasets RIM-ONE, DRIONS-DB and ORIGA and is able to achieve an accuracy of 91%, 92% and 90% respectively. The developed approach is validated by training it over RIM-ONE dataset independently, without changing any model parameters. The model provides significant improvement in segmentation of the optic disc and optic cup along with improvement in training time. [ABSTRACT FROM AUTHOR]

In this paper, present a wearable detecting glove with inserted heterocentral element optic nerve sensors that identify finger flexion to realize unconstrained hand movement checking. The hetero-main element sensor is fit to the wearable detecting glove since it's fit for optical force based estimations with great security and repeatability utilizing single-mode transmission filaments and is unaffected by temperature variances. The hetero-center sensor components are situated on the back of the hand so as that they are not influenced by arbitrary wrinkles inside the glove at the joints. Subsequently, the hetero-center flexion sensor after adjustment is equipped for recognizing the joint edges of the fingers regardless of contrasts close by size, and in this way the hetero-center detecting strategy empowers the distinguishing glove to be worked with a base number of sensor centers. The optical loss performance of the hetero-core sensors reveals monotonic characteristics with regard to the flexion angle of joints. The optical loss is 1.35dB for a flexion angle of roughly 97.2° with accuracy of 0.89° within the detected flexion angle. Ongoing hand movement catch was exhibited by methods for the proposed detecting glove without limiting normal human conduct. [ABSTRACT FROM AUTHOR]

Glaucoma is one of the most complicated disorder in human eye that causes permanent vision loss gradually if not detect in early stage. It can damage the optic nerve without any symptoms and warnings. Different automated glaucoma detection systems were developed for analyzing glaucoma at early stage but lacked good accuracy of detection. This paper proposes a novel automated glaucoma detection system which effectively process with digital colour fundus images using hybrid classifiers. The proposed system concentrates on both Cup-to Disk Ratio (CDR) and different features to improve the accuracy of glaucoma. Morphological Hough Transform Algorithm (MHTA) is designed for optic disc segmentation. Intensity based elliptic curve method is used for separation of optic cup effectively. Further feature extraction and CDR value can be estimated. Finally, classification is performed with combination of Naive Bayes Classifier and K Nearest Neighbour (KNN). The proposed system is evaluated by using High Resolution Fundus (HRF) database which outperforms the earlier methods in literature in various performance metrics. [ABSTRACT FROM AUTHOR]

Abstract Glaucoma is a condition of the eye in which optic nerve fibres are damaged that leads to loss of vision and may even lead to blindness if not diagnosed on time. Many manual scanning methods are available but they are time consuming expensive and require experts in these fields to use them. A number of automated glaucoma diagnosis methods using fundus images are also available. This paper compares the feature selection using Student's t-test and Principal Component Analysis (PCA). It also proposes a modified decision making approach using bit-wise OR operation at the output of the classifier for different colour components. The R, G, B and grey scale values are extracted from the input image. This is then subject to 2D EWT to form the sub-band images. Then correntropy is extracted from these decomposed components. Features are selected using two methods: Student's t-test and PCA. Features are classified using Least Squares Support Vector Machine (LS-SVM) to classify between normal and glaucoma images. For performance analysis, the impact of testing and training on the detection and performance analysis of the classifier was done. It is found that the accuracy of detection using Student's t-test to the blue component is 78.8% and combined response of red, blue, green and grey component gives 97% whereas the accuracy of detection using PCA to the red component is 87.3% and the combined response of red, blue, green and grey component is 99%. With better detection accuracy, less processing time and computational complexity PCA was found to be better of the two methods. [ABSTRACT FROM AUTHOR]

• This paper proposes 2D-FBSE-EWT method for image analysis. • The 2D-FBSE-EWT method has been studied in the multi-frequency scale framework. • The method has been studied for automated glaucoma detection from fundus images. • Zero order and first order 2D-FBSE-EWT methods have been compared. • The classification performance of 2D-FBSE-EWT method is compared with other methods. Glaucoma is an eye disease in which fluid within the eye rises and puts pressure on optic nerves. This fluid pressure slowly damages the optic nerves, and if it is left untreated, it may lead to permanent vision loss. So the detection of glaucoma is necessary for on-time treatment. This paper presents a method, namely two dimensional Fourier-Bessel series expansion based empirical wavelet transform (2D-FBSE-EWT), which uses the Fourier-Bessel series expansion (FBSE) spectrum of order zero and order one for boundaries detection. 2D-FBSE-EWT method is also studied on multi-frequency scale during boundaries detection in FBSE spectrum. In multi-frequency scale based 2D-FBSE-EWT analysis, three frequency scales full, half, and quarter are used. These methods are used for the decomposition of fundus images into sub-images. For glaucoma detection from sub-images, two methods are used: (1) proposed method-1, which is a conventional machine learning (ML) based method and (2) proposed method-2, which is an ensemble ResNet-50 based method. The ensemble is done using operations like maxima, minima, averages, and fusion. Proposed method-1 has provided best result with order one 2D-FBSE-EWT at full scale. In Proposed method-2, order one 2D-FBSE-EWT at full scale with fusion ensemble method provides better accuracy as compared to other ensemble methods. Our proposed methods have outperformed all the compared methods used for glaucoma detection. [ABSTRACT FROM AUTHOR]

Copyright of Colombian Journal of Anesthesiology is the property of Lippincott Williams & Wilkins and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Canadian Journal of Ophthalmology is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Neurophysiologie-Labor is the property of Elsevier GmbH, Urban & Fischer Verlag and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Collegium Antropologicum is the property of Croatian Anthropological Society and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Collegium Antropologicum is the property of Croatian Anthropological Society and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

• A novel feature extraction method (ECNet) is proposed for glaucoma detection. • An evolutionary algorithm is employed to optimize the weights of ECNet. • ECNet demands a relatively few number of learnable parameters. • Learning of ECNet is free from error gradient. • Results show the superiority of the proposed method over existing methods. Glaucoma is an ocular disorder that affects the optic nerve and ultimately leads to partial or complete vision loss. Hence, there is a strong need for early screening of glaucoma. Earlier diagnosis schemes mostly rely on handcrafted feature engineering. On the other hand, the non-handcrafted feature extraction methods are generally designed with the support of gradient-based algorithms that suffer from critical problems like overfitting and demand for a larger set of samples for effective training. To mitigate these issues, in this paper, we propose a novel non-handcrafted feature extraction method termed as evolutionary convolutional network (ECNet) for automated detection of glaucoma from fundus images. The proposed method includes various layers such as convolutional, compression, rectified linear unit (ReLU), and summation layer which facilitate the extraction of discriminative features. An evolutionary algorithm called real-coded genetic algorithm (RCGA) is employed to optimize the weights at different layers. The ECNet is trained using a criteria that maximizes the inter-class distance and minimizes the intra-class variance of different classes. The final feature vectors are then subjected to a set of classifiers such as K-nearest neighbor (KNN), backpropagation neural network (BPNN), support vector machine (SVM), extreme learning machine (ELM), and kernel ELM (K-ELM) to select optimum performing model. The experimental results on a dataset of 1426 fundus images (589 normal and 837 glaucoma) demonstrate that the ECNet model with SVM yielded the highest accuracy of 97.20% compared to state-of-the-art techniques. The proposed model can aid ophthalmologists to validate their screening. [ABSTRACT FROM AUTHOR]

• Biotinidase deficiency is a rare autosomal metabolic disorder. • It is easily tested by measuring biotinidase activity in serum or through whole blood spotted on filter paper in newborns. • Newborn screening tests are available since 1984, but not globally implemented. • Symptoms usually present in childhood, but onset in early adulthood as an opticospinal syndrome is possible. • Biotinidase deficiency is a treatable disorder. Diagnosis of biotinidase deficiency is rare and usually made in infancy, through newborn screening or after presenting symptoms. We present the case of 19-year old male with progressive optic atrophy and in a second phase spinal cord syndrome unresponsive to immunosuppressive therapies. After diagnosis of profound biotinidase deficiency, oral biotin substitution was started with partial visual improvement and normalization of gait. This case highlights the possibility of late-onset biotinidase deficiency and its treatable character. [ABSTRACT FROM AUTHOR]

Abstract: Glaucoma is one of the leading causes of irreversible blindness. Primary open-angle glaucoma (POAG), the most common type, is a progressive optic neuropathy with characteristic structural changes in the optic nerve head and functional changes in the visual field. Mechanical and vascular theories for the pathogenesis of glaucomatous optic neuropathy have been proposed. Elevated intraocular pressure (IOP) is a strong risk factor, although a subset of POAG patients has normal IOP and is designated normal tension glaucoma (NTG). Clearly, factors other than IOP are likely to be involved in retinal ganglion cell death in glaucoma. An intriguing finding of recent studies is that intracranial pressure (ICP) is lower in patients with POAG and NTG when compared with nonglaucomatous control subjects. It has been suggested that the relationship between IOP and ICP may play a fundamental role in the development of glaucoma. A decreased ICP could result in an increased trans-lamina cribrosa pressure difference (IOP minus ICP) and lead to glaucomatous damage. In the present paper, we raise the question of whether ICP fluctuations also may be important in glaucoma. The effect of ICP fluctuation might be comparable to that of IOP fluctuation, which has been recognized as an independent risk factor for glaucoma progression. [Copyright &y& Elsevier]

• Glaucoma screening based on fundus image by using cup to disc ratioapproach. • Cup and disc is clustered by using Simple Linear Iterative Clustering superpixel. • Statistical pixel level information is extracted for classification purpose. • Support vector machine is then used to group the clustered superpixel. Glaucoma is an ocular disease caused by damaged optic nerve head (ONH) due to high intraocular pressure (IOP) within the eyeball. Usually, glaucoma patients will not realize the presence of this disease due to lack of visible early symptoms such as pain and redness mark. The disease can cause permanent blindness if it is not treated immediately. Hence, glaucoma screening is very crucial in detecting the disease during the early stages. There are various types of glaucoma screening tests such as tonometry test which is based on IOP measurement, ophthalmology test which is based on shape and color of the eyes, and pachymetry test which is based on complete field vision measurement. All these three screening tests involve manual assessment which is time-consuming and costly. Therefore, an efficient glaucoma screening system that can automatically analyze the severity level of the disease is very much needed. Thus, the main objective of this paper is to develop an automatic glaucoma screening system based on superpixel classification by providing a high-quality input image. Firstly, input images are undergone preprocessing methods to cater for noise removal and illumination correction. This is emphasized in the implementation of the anisotropic diffusion filter and illumination correction method. The pixels of the input images are then aggregate into superpixels using Simple Linear Iterative Clustering (SLIC) approach. Then, image features based on histogram data and textural information are extracted on each superpixel using statistical pixel-level (SPL) method. The prominent features are then fed into Support Vector Machine (SVM) classifier to classify each superpixel into optic disc, optic cup, blood vessel, and background regions. The classifier is also used to determine the boundaries of both optic disc and optic cup. Lastly, the segmented optic disc and optic cup are used to determine the presence of glaucoma using cup-to-disc ratio (CDR) measurement. The proposed method has been tested on RIM-One database. The experimental results have successfully distinguished optic disc and optic cup from the background with an average accuracy and sensitivity of 98.6% and 92.3%, respectively tested on linear kernel. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study was to observe the relationship of the sphenoid sinus and posterior ethmoid cells with the optic nerve. Material and Methods: 34 CT scans (68 sides) of paranasal sinuses of patients older than 18 years were retrospectively reviewed. Images were assessed in two planes - axial and coronal. We observed the position and relationship of the optic nerve to the posterior ethmoid and sphenoid sinuses, bony dehiscence and protrusion of the optic nerve into sinuses, and pneumatization of the anterior clinoid process. Results: The most frequent position of optic nerve (ON) was a location close to the posterior ethmoid and sphenoid sinuses without contacting or indentation of the wall - 55.9 % (38 nerves). The bulging of ON to sphenoid sinus was found in 14.7 % (10 nerves) and the course of the nerve through sinus in 16 nerves (23.5 %). The position of ON intimately to both sinuses was observed in 5.9 % (4 nerves). Protrusion of ON, dehiscence of the bony wall and pneumatization of anterior clinoid process (ACP) was seen in 12 (35.3 %), 4 (11.8 %) and 9 (26.5 %) patients, respectively. Both the protrusion of ON and ACP pneumatization were present in 8 (23.5 %) patients on the right side and in 5 (14.7 %) patients on the left side. Conclusion: Variations of posterior ethmoid and sphenoid sinuses are numerous and may entail potential risk of injury of the ON during sinus surgery. Knowledge of individual differences and configurations in the operation area may help the surgeon to prevent complications. Computed tomography is the preferred radiographic modality for evaluation of bony variations and the pathology of this region. [ABSTRACT FROM AUTHOR]