Evaluation of color representation for texture analysis

Since more than 50 years texture in image material is a topic of research. Hereby, color was ignored mostly. This study compares 70 different configurations for texture analysis, using four features. For the configurations we used: (i) a gray value texture descriptor: the co-occurrence matrix and a color texture descriptor: the color correlogram, (ii) six color spaces, and (iii) several quantization schemes. A three classifier combination was used to classify the output of the configurations on the VisTex texture database. The results indicate that the use of a coarse HSV color space quantization can substantially improve texture recognition compared to various other gray and color quantization schemes

A Multi-Modal Public Transport Solution For Male, Maldives

Male, the island capital of the Maldives, an archipelago of over 1000 islands in the Indian Ocean faces chronic traffic congestion. This 2 sq km island is home to over 100,000 people. There is a taxi service comprising of around 450 vehicles and a dhoni (ferry) service amounting to over 100 vessels to neighbouring islands. Male, which is fast becoming a small urban centre faces typical peak period traffic issues. The vehicle fleet is dominated by motor cycles which still contribute to traffic congestion in narrow streets. The taxi system which comprises of individually owned taxis registered with a ‘call centre’, provide limited services but fails during peak demand periods especially on rainy days. There is very little coordination between the ferry and taxi services. The paper is based on the results of a detailed urban transport planning study carried out in Male Urban Area which included passenger interviews, vehicle counts and travel time surveys covering all modes of motorized and non-motorized travel. This paper investigates the introduction of a mini-bus transport system that would provide easy transfers between ferries and major traffic generators and attractors. The contribution of a mini-bus service in the long-term is also discussed with respect to implementation of traffic demand management measures. This paper discuses the most appropriate type of vehicle that could be used and the potential framework for ownership and management of such a system taking in to consideration the multi-modal connectivity and also the service parameters for the operation of a successful minibus service. The paper also analyses the present operation of the ferry services and investigates its ownership and operation parameters for efficiency and cost effectiveness. The paper reports reasons for the varied efficiencies seen on the different routes and the impact the informal and loosely regulated service providers have on the key performance indicators of these services. It also compares cost between different ferry services and studies the relationship between the ownership structure, technology levels, productivity and fare.Institute of Transport and Logistics Studies. Faculty of Economics and Business. The University of Sydne

Automated assessment of COVID-19 reporting and data system and chest CT severity scores in patients suspected of having COVID-19 using artificial intelligence

Background: The coronavirus disease 2019 (COVID-19) pandemic has spread across the globe with alarming speed, morbidity, and mortality. Immediate triage of patients with chest infections suspected to be caused by COVID-19 using chest CT may be of assistance when results from definitive viral testing are delayed.Purpose: To develop and validate an artificial intelligence (AI) system to score the likelihood and extent of pulmonary COVID-19 on chest CT scans using the COVID-19 Reporting and Data System (CO-RADS) and CT severity scoring systems.Materials and Methods: The CO-RADS AI system consists of three deep-learning algorithms that automatically segment the five pulmonary lobes, assign a CO-RADS score for the suspicion of COVID-19, and assign a CT severity score for the degree of parenchymal involvement per lobe. This study retrospectively included patients who underwent a nonenhanced chest CT examination because of clinical suspicion of COVID-19 at two medical centers. The system was trained, validated, and tested with data from one of the centers. Data from the second center served as an external test set. Diagnostic performance and agreement with scores assigned by eight independent observers were measured using receiver operating characteristic analysis, linearly weighted kappa values, and classification accuracy.Results: A total of 105 patients (mean age, 62 years +/- 16 [standard deviation]; 61 men) and 262 patients (mean age, 64 years +/- 16; 154 men) were evaluated in the internal and external test sets, respectively. The system discriminated between patients with COVID-19 and those without COVID-19, with areas under the receiver operating characteristic curve of 0.95 (95% CI: 0.91, 0.98) and 0.88 (95% CI: 0.84, 0.93), for the internal and external test sets, respectively. Agreement with the eight human observers was moderate to substantial, with mean linearly weighted k values of 0.60 +/- 0.01 for CO-RADS scores and 0.54 +/- 0.01 for CT severity scores.Conclusion: With high diagnostic performance, the CO-RADS AI system correctly identified patients with COVID-19 using chest CT scans and assigned standardized CO-RADS and CT severity scores that demonstrated good agreement with findings from eight independent observers and generalized well to external data. (C) RSNA, 2020Cardiovascular Aspects of Radiolog

Mimicking human texture classification

Contains fulltext : __32741.pdf (preprint version ) (Open Access

Automated segmentation of pulmonary structures in thoracic computed tomography scans: a review

Item does not contain fulltextComputed tomography ({CT}) is the modality of choice for imaging the lungs in vivo. Sub-millimeter isotropic images of the lungs can be obtained within seconds, allowing detection of small lesions and detailed analysis of disease processes. The high resolution of thoracic {CT} and the high prevalence of lung diseases require a high degree of automation in the analysis pipeline. Automated segmentation of pulmonary structures in thoracic {CT} has been an important research topic for over a decade now. This systematic review provides an overview of current literature. We discuss segmentation methods for the lungs, the pulmonary vasculature, the airways, including airway tree construction and airway wall segmentation, the fissures, the lobes, and the pulmonary segments. For each topic the current state of the art is summarized, and topics for future research are identified

Modeling human color categorization: color discrimination and color memory

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Modeling cognitive texture classification, (Part I)

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Object based image retrieval: Utilizing color and texture

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Air Trapping in Emphysema

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Modeling cognitive texture classification, (Part II)

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