Improving medical image perception by hierarchical clustering based segmentation

It has been well documented that radiologists' performance is not perfect: they make both false positive and false negative decisions. For example, approximately thirty percent of early lung cancer is missed on chest radiographs when the evidence is clearly visible in retrospect. Currently computer-aided detection (CAD) uses software, designed to reduce errors by drawing radiologists' attention to possible abnormalities by placing prompts on images. Alberdi et al examined the effects of CAD prompts on performance, comparing the negative effect of no prompt on a cancer case with prompts on a normal case. They showed that no prompt on a cancer case can have a detrimental effect on reader sensitivity and that the reader performs worse than if the reader was not using CAD. This became particularly apparent when difficult cases were being read. They suggested that the readers were using CAD as a decision making tool instead of a prompting aid. They conclude that "incorrect CAD can have a detrimental effect on human decisions". The goal of this paper is to explore the possibility of using hierarchical clustering based segmentation (HSC), as a perceptual aid, to improve the performance of the reader

Improving medical image perception by hierarchical clustering based segmentation

It has been well documented that radiologists' performance is not perfect: they make both false positive and false negative decisions. For example, approximately thirty percent of early lung cancer is missed on chest radiographs when the evidence is clearly visible in retrospect [1]. Currently Computer-Aided Detection (CAD) uses software, designed to reduce errors by drawing radiologists' attention to possible abnormalities by placing prompts on images. Alberdi et al examined the effects of CAD prompts on performance, comparing the negative effect of no prompt on a cancer case with prompts on a normal case. They showed that no prompt on a cancer case can have a detrimental effect on reader sensitivity and that the reader performs worse than if the reader was not using CAD. This became particularly apparent when difficult cases were being read. They suggested that the readers were using CAD as a decision making tool instead of a prompting aid. They conclude that "incorrect CAD can have a detrimental effect on human decisions" [2]. The goal of this paper is to explore the possibility of using Hierarchical Clustering based Segmentation (HCS) [3], as a perceptual aid, to improve the performance of the reader

Vision Based Environment Mapping By Network Connected Multi-Robotic System.

The conventional environment mapping solutions are computationally very expensive and cannot effectively be used in multi-robotic environment, where small size robots with limited memory and processing resources are used. This study provides an environment mapping solution in which a group of small size robots extract simple distance vector features from the on-board camera images. The robots share these features between them using a wireless communication network setup in infrastructure mode. For mapping the distance vector features on a global map and to show a collective map building operation, the robots needed their accurate location and heading information. The robots location and heading information is computed using two ceiling mounted cameras, which collective localises the robots. Experimental results show that the proposed method provides the required environmental map which can facilitate the robot navigation operation in the environ- ment. It was observed that, using the proposed approach, the near by object boundaries can be mapped with higher accuracy comparatively the far lying objects

Vision Based Object Recognition and Localisation by a Wireless Connected Distributed Robotic Systems

Object recognition and localisation are important processes in computer vision and robotics. Advances in computer vision have resulted in many object recognition techniques, but most of them are computationally very intensive and require robots with powerful processing systems. For small robots, these techniques are not applicable because of the constraints of execution time. In this study, an optimised implementation of SURF based recognition technique is presented. Suitable image pre-processing techniques were developed which reduced the recognition time on small robots with limited processing resources. The recognition time was reduced from 39 seconds to 780 milliseconds. This recognition technique was adopted by a team of small robots which were given prior training to search for objects of interest in the environment. For the localisation of the robots and objects a new template, designed for passive markers based tracking, was introduced. These markers were placed on the top of each robot and they were tracked by the two ceiling mounted cameras. The information from both sources, that is ceiling mounted cameras and team of robots, was used collectively to localise the objects in the environment. The objects were localised with an error ranging from 2.8cm to 5.2cm from their actual positions in the test arena which has the dimensions of 150x163cm

An Efficient Approach To Object Recognition For Mobile Robots.

In robotics, the object recognition approaches developed so far have proved very valuable, but their high memory and processing requirements make them suitable only for robots with high processing capability or for offline processing. When it comes to small size robots, these approaches are not effective and light- weight vision processing is adopted which causes a big drop in recognition performance. In this research, a computationally expensive, but efficient appearance-based object recognition approach is considered and tested on a small robotic platform which has limited memory and processing resources. Rather than processing the high resolution images, all the times, to perform recognition, a novel idea of switching between high and low resolutions, based on the “distance to object” is adopted. It is also shown that much of the computation time can be saved by identifying the irrelevant information in the images and avoid processing them with computationally expensive approaches. This helps to bridge the gap between the computationally expensive approaches and embedded platform with limited processing resources

Implementation Of Distributed Mosaic Formation And Object Detection In Modular Robotic Systems.

In reconfigurable modular robotics, when robot modules joins to form a robotic organism, they create a dis- tributed processing environment in a unified system. This research builds on the efficient use of these dis- tributed processing resources and presents the manner these resources can be utilised to implement distributed mosaic formation and object detection within the organism. The generation of mosaics provides surrounding awareness to the organism and helps it to localise itself with reference to the objects in the mosaics. Whereas, the detection of objects in the mosaic helps in identifying parts of the mosaic which needed processing

Diffusion of knowledge and behaviours among trainee doctors in an acute medical unit and implications for quality improvement work: a mixed methods social network analysis

Abstract: Objectives: To describe the social networks that diffuse knowledge, attitudes and behaviours relating to different domains of practice within teams of trainee doctors in an acute hospital medical setting. The domains examined were “clinical-technical”, “patient centeredness” and “organisation of work”. Design: Sequential mixed methods: i) sociocentric survey of trainee consisting of questions about which colleagues are emulated or looked to for advice, with construction of social network maps, followed by ii) semi structured interviews regarding per to peer influence, analysed using a grounded theory approach. The study took place over 24 months. Setting: An acute medical admissions unit, which receives admissions from the emergency department and primary care, in a NHS England teaching hospital. Participants: Trainee medical doctors working in five consecutive rotational teams. Surveys were done by 39 trainee doctors; then 20 different participants from a maximal diversity sample were interviewed. Results: Clinical-technical behaviours spread in a dense network with rich horizontal peer to peer connections. Patient centred behaviours spread in a sparse network. Approaches to non-patient facing work are seldom copied from colleagues. Highly influential individuals for clinical technical memes were identified; high influencers were not identified for the other domains. Conclusion: Information and influence relating to different aspects of practice have different patterns of spread within teams of trainee doctors; highly influential individuals were important only for spread of clinical-technical practice. Influencers have particular characteristics, and this knowledge could guide leaders and teachers

The impact of information quantity and strength of relationship between training set and validation set on accuracy of genomic estimated breeding values

Recent advances in genomic selection are a revolution in animal breeding. A genome consisting 10 chromosomes each with 100 cM in length with 100 equally spaced markers (1 cM) were simulated. After 50 generations of random mating in a finite population (Ne = 100) in order to create sufficient linkage disequilibrium, population was expanded to two different population sizes of 500 and 1000. This structure was conserved until generation 59. Only females of generations 51 to 58 had phenotypicrecords and were included in the training set. The generation 59 was assumed as juveniles without any phenotypic records (validation set). Two measures of heritability (h2 = 0.1 and h2 = 0.5) were considered.Each simulation was replicated 10 times and results were averaged across replications. The results showed that using individuals of more recent generations in training set led to higher accuracy of genomic estimated breeding values (GEBVs) than individuals from more distant generations. However, increase in the amount of phenotypic records in training set even from individuals of older generations will increase accuracy of GEBVs. Number of phenotypic records in training set was shown to haveimportant role in accuracy of GEBVs especially for low heritability traits

Epidemiological aspects of cutaneous leishmaniasis in Iran

Background: The prevalence of Cutaneous Leishmaniasis (CL) has been reported as 1.8 to 37.9 in different provinces of Iran. However, enough knowledge about epidemiological aspects of CL disease is needed to launch a proper program to plan control and preventive strategies about the disease. Objectives: The present study aimed to determine the epidemiological aspect of CL in Iran during the first 6 months of 2014. Patients and Methods: In this cross-sectional study, all cases of CL reported to centers for disease control and prevention (CDC) by state health departments from March 2014 to September 2014 were included. Descriptive statistics including frequency tables, measures of central value, and measures of dispersion to describe the study variables were used to analyze data. Area maps were created using ArcView GIS v. 3.3. Results: Most of the CL cases were observed in eastern, central, and southern provinces. Two thousand thirty-one cases (55.13) were male and 2,306 (62.6) were living in urban areas. The mean age of the patients was 27 ± 18 years old. More than 31 of them were under 14 years of age. Also, 3570 individuals (96.91) were new cases. more wounds were seen so that 62.75 of the wounds were on the hands, 24.8 in the head and neck, and 2.7 in the body. Conclusions: According to the epidemiological features of CL in Iran, Providing a uniform mechanism for control and prevention of this disease is not possible. Thus, initial actions such as staff training, screening in endemic areas, and treatment of patients with urban leishmaniasis as a reservoir for the disease can be useful, according to the geographical position and carrier. © 2015, Infectious Diseases and Tropical Medicine Research Center

Improved pitting corrosion resistance of S.S 316L by Pulsed Current Gas Tungsten Arc Welding

In this study, S.S 316L was welded using Direct Current Gas Tungsten Arc Welding (DGTAW) and Pulsed Current Gas Tungsten Arc Welding (PGTAW) methods. Optical observations, scanning electron microscopy (SEM) and X-ray diffraction analysis (XRD) were employed to study the effect of continuous and pulse currents on microstructure and phase transformation in weld metal (WM). In addition pits morphology were evaluated by SEM. The corrosion behaviour was analyzed using cyclic polarizaton tests and Mott-schottky measurements. The pulse current resulted in finer grain and more ferrite in WM. This can be due to the decrease in heat input and higher cooling rate encouraged by pulse current. Cyclic potentiodynamic polarization tests showed that the WM of sample produced by pulse current show higher corrosion and pitting resistances than that in sample produced by continuous current. The reason is attributed to lower segregation of solute elements such as chromium and molybdenum into the delta-ferrite and also finer grain size produced in WM due to lower heat input and higher cooling rate. Both of these factors increase the stability of passive layer formed. The results showed that the corrosion behaviour of WM in both conditions (pulse and continuous current) is higher than the base metal (BM). This fact is attributed to the presence of ferrite bands formed in BM due to the segregation of alloy elements. The Mott-schottky plots confirmed that the passive layer formed on welded samples was an n-type semiconductor. The results showed that the samples showed less pitting resistance contained more oxygen vacancies in their passive film structure. It is also concluded that the breakdown of passive layer and pitting formation obey point defect model (PDM). Keywords: S.S 316L, Pulsed Current Gas Tungsten Arc Welding (PGTAW), lacy ferrite, vermicular ferrite, Pitting corrosion, Mott- Schottky