Classification of Graphomotor Impressions Using Convolutional Neural Networks: An Application to Automated Neuro-Psychological Screening Tests

Graphomotor impressions are a product of complex cognitive, perceptual and motor skills and are widely used as psychometric tools for the diagnosis of a variety of neuro-psychological disorders. Apparent deformations in these responses are quantified as errors and are used are indicators of various conditions. Contrary to conventional assessment methods where manual analysis of impressions is carried out by trained clinicians, an automated scoring system is marked by several challenges. Prior to analysis, such computerized systems need to extract and recognize individual shapes drawn by subjects on a sheet of paper as an important pre-processing step. The aim of this study is to apply deep learning methods to recognize visual structures of interest produced by subjects. Experiments on figures of Bender Gestalt Test (BGT), a screening test for visuo-spatial and visuo-constructive disorders, produced by 120 subjects, demonstrate that deep feature representation brings significant improvements over classical approaches. The study is intended to be extended to discriminate coherent visual structures between produced figures and expected prototypes

Transthoracic ultrasound guided fine needle aspiration cytology of peripheral lung lesions: an experience of a pulmonologist

Background: Ultrasound (USG) guided fine needle aspiration cytology (FNAC) is a simple and cost-effective method for the diagnosis of various peripheral lung lesions. Being radiation free and easily available in most of centres, it has become an important diagnostic modality for early diagnosis of peripheral lung lesions. Besides procedure is simple and complications if occur, can be managed by a pulmonologist effectively. This study was aimed to evaluate the role of Transthoracic ultrasound guided FNAC in diagnosis of peripheral lung lesion.Methods: This prospective observational study was conducted at Government Chest Diseases Hospital Srinagar over a period of one year from January 2018-December 2018. 61 patients who fulfilled inclusion criteria were included in this study. After properly explaining the procedure and taking informed consent, USG guided FNAC was done in patients with peripheral lung lesions under local anaesthesia. Radiological and cytological data of enrolled patients was collected prospectively and analysed.Result: About 61 patients were included in this study comprising of 39 males and 22 females in age range of 17- 90 years. Malignancy was the most common cytological diagnosis (78.57%). while as benign diagnosis was reached in 21.43%. In 8.19% of patients, FNAC was inconclusive. Among the malignant group, adenocarcinoma (47.72%) was most common cytological diagnosis. The overall diagnostic yield of USG guided FNAC in this study was 91.8%.Conclusion: USG guided FNAC of peripheral lung lesions is a simple procedure with high accuracy and less complication rate which can be performed by a pulmonologist for diagnosis

iVision HHID: Handwritten Hyperspectral Images Dataset for Benchmarking Hyperspectral Imaging-based Document Forensic Analysis

We present a dataset of hyperspectral images of handwriting samples collected from 54 individuals. The purpose of the presented dataset is to further explore the use of hyperspectral imaging in document image analysis and to benchmark the performance of forensic analysis methods for hyperspectral document images. Each hyperspectral cube in the dataset has a spatial resolution of 512 × 650 pixels and contains 149 spectral channels in the spectral range of 478∼901nm. All the individuals have different personalities and have their writing patterns. The information of age and gender of each individual is collected. Each subject has written twenty-eight sentences using 12 different varieties of pens from different brands in blue color, each approximately 9 words or 33 characters long, all English alphabets in capital and small cases, digits from 0-9. The previous methods use synthetic mixed samples created by joining different parts of the images from the UWA WIHSI dataset.Each document consists of real mixed samples written with different pens and by different writers with a variety of mixing ratios of inks and writers for forensic analysis.The standard A4 pages, each weighing 70 grams and manufactured by “AA” company, are used for data collection. The handwritten notes written by each subject with different pens are annotated in rectangular boxes. This dataset can be used for several tasks related to hyperspectral document image analysis and document forensic analysis including, handwritten optical character recognition, ink mismatch detection, writer identification at sentence, word, and character-level, handwriting-based gender classification, handwriting-based age prediction, handwritten word segmentation, and word generation. This dataset was designed and collected by the research team at the Artificial intelligence and Computer Vision Lab (iVision), Institute of Space Technology, Pakistan, and the hyperspectral images were acquired through imaging spectroscopy in the visible wavelength range at Wageningen University Research, the Netherland

Developing Trust through Social Media Influencers and Halal Tourism to Impact the Travel Decision of Travelers

Marketers are trying their best to find tactics and means of communication to gain the trust of consumers by attracting them and keeping their needs and interests in view, like halal tourism in the domain of the hospitability industry. The main purpose of this study is to analyze and explain whether the trust is developed by Social Media influencers and Halal Tourism and hence impacts the travel decision of travelers or not, in the context of Pakistan. In this regard, the data is collected from university students of the University of Punjab Gujranwala Campus and GIFT University. After collection different tests of normality, correlation, regression, and mediations were run on the data for analysis by using statistical software SPSS 20. After analysis, it was found that all hypotheses are accepted and interpreted that Social Media Influencers and Halal Tourism significantly impact the decisions of travelers. The study also revealed that trust mediates the relationship between Social Media influencers and Travel decisions; as well as the relation between Halal Tourism and Travel decisions. Organizations and managers must use these findings and knowledge to attract consumers and enhance their profits. These findings contribute to theory as it is the first study of its type and paves way for future research

Optimized Class-Separability in Hyperspectral Images

International audienceImage visualization techniques are mostly based on three bands as RGB color composite channels for human eye to characterize the scene. This, however, is not effective in case of hyper-spectral images (HSI) because they contain dozens of informative spectral bands. To eliminate redundancy of spectral information among these bands, dimensionality reduction (DR) is applied while at the same trying to retain maximum information. In this paper, we propose a new method of information-preserved hyper-spectral satellite image visualization that is based on fusion of unsupervised band selection techniques and color matching function (CMF) stretching. The results show consistent, edge-preserved and pre-attentive feature less images with high class separability. Different visualization techniques are compared to demonstrate the effectiveness of our scheme that can prompt an important advancement in the field

Automation of Optimized Gabor Filter Parameter Selection for Road Cracks Detection

International audienceAutomated systems for road crack detection are extremely important in road maintenance for vehicle safety and traveler's comfort. Emerging cracks in roads need to be detected and accordingly repaired as early as possible to avoid further damage thus reducing rehabilitation cost. In this paper, a robust method for Gabor filter parameters optimization for automatic road crack detection is discussed. Gabor filter has been used in previous literature for similar applications. However, there is a need for automatic selection of optimized Gabor filter parameters due to variation in texture of roads and cracks. The problem of change of background, which in fact is road texture, is addressed through a learning process by using synthetic road crack generation for Gabor filter parameter tuning. Tuned parameters are then tested on real cracks and a thorough quantitative analysis is performed for performance evaluation

Approach to exaggerated startle reflex: A case of hyperekplexia minor

A broad set of conditions may present with an exaggerated startle reflex in clinics. This, combined with the overall rarity of these disorders, may pose diagnostic uncertainty in the mind of the treating physician. Herein, we report a case of a patient who presented to us with the complaint of exaggerated startle reflex and outline a simple approach towards characterisation of these disorders

Saliency based visualization of hyper-spectral images

International audienceThe problem with visualization of hyper-spectral images on tri-stimulus displays arises from the fact that they contain hundreds of spectral bands while generally used display devices support only three bands/channels namely blue, green and red. Therefore, for visualization a hyper-spectral (HS) image has to be reduced to three bands. The main challenge while performing this band reduction is to retain and display the maximum information available in a hyper-spectral image. Human visual system focuses attention on certain regions in images called " salient regions ". Therefore to provide a comprehensive representation of hyper-spectral data on tri-stimulus displays we propose to use a weighted fusion method of saliency maps and hyper-spectral bands. The efficacy of the proposed algorithm has been demonstrated by tests on both urban and countryside images of AVIRIS and ROSIS sensors.

Keyword based Information Retrieval System for Urdu Document Images

International audienceVarious dynasties ruled the Indian sub-continent and left behind enormous and rich cultural heritage that also included intellectually enriched research in the shape of various documents scripted in Urdu. In order to provide efficient access to this knowledge, analysis though digitizing the existing work is the need of hour. In addition to digitization, efficient search mechanisms also need to be implemented to provide users a rapid access to the queried information. In most cases, the digitized documents are complemented by manually assigned tags which not only is a time consuming task but also provides a very limited search facility. Automating the transcription of these documents using Optical Character Recognition (OCR) systems is also challenging due to the very complex cursive nature of Urdu text. To overcome these limitations, a keyword spotting based information retrieval system for document images is introduced in this study. The proposed technique relies on two major modules, document indexing and retrieval. Images of documents are segmented into partial words (ligatures) and identical partial words (PWs) are grouped into clusters. We introduce the concept of considering each (partial) word as a unique shape and a set of shape descriptors is extracted to characterize the PWs. The clusters of PWs are used to index a given set of documents. During retrieval, the query word presented to the system is matched with the clusters in the database and all documents containing instances of the query word are retrieved and presented to the user. The system evaluated on a set of printed Urdu documents in Nastaliq font realized promising precision and recall rates

iVision HHID: Handwritten hyperspectral images dataset for benchmarking hyperspectral imaging-based document forensic analysis

This article presents a dataset of hyperspectral images of handwriting samples collected from 54 individuals. The purpose of the presented dataset is to further explore the use of hyperspectral imaging in document image analysis and to benchmark the performance of forensic analysis methods for hyperspectral document images. Each hyperspectral cube in the dataset has a spatial resolution of 512 × 650 pixels and contains 149 spectral channels in the spectral range of 478–901 nm. All the individuals have different personalities and have their writing patterns. The information of age and gender of each individual is collected. Each subject has written twenty-eight sentences using 12 different varieties of pens from different brands in blue color, each approximately 9 words or 33 characters long, all English alphabets in capital and small cases, digits from 0 to 9. The previous methods use synthetic mixed samples created by joining different parts of the images from the UWA WIHSI dataset.Each document consists of real mixed samples written withdifferent pens and by different writers with a variety of mixing ratios of inks and writers for forensic analysis.The standard A4 pages, each weighing 70 gs and manufactured by “AA” company, are used for data collection. The handwritten notes written by each subject with different pens are annotated in rectangular boxes. This dataset can be used for several tasks related to hyperspectral document image analysis and document forensic analysis including, handwritten optical character recognition, ink mismatch detection, writer identification at sentence, word, and character-level, handwriting-based gender classification, handwriting-based age prediction, handwritten word segmentation, and word generation. This dataset was designed and collected by the research team at the Artificial intelligence and Computer Vision Lab (iVision), Institute of Space Technology, Pakistan, and the hyperspectral images were acquired through imaging spectroscopy in the visible wavelength range at Wageningen University & Research, the Netherlands