Medical Image Classification via SVM using LBP Features from Saliency-Based Folded Data

Good results on image classification and retrieval using support vector machines (SVM) with local binary patterns (LBPs) as features have been extensively reported in the literature where an entire image is retrieved or classified. In contrast, in medical imaging, not all parts of the image may be equally significant or relevant to the image retrieval application at hand. For instance, in lung x-ray image, the lung region may contain a tumour, hence being highly significant whereas the surrounding area does not contain significant information from medical diagnosis perspective. In this paper, we propose to detect salient regions of images during training and fold the data to reduce the effect of irrelevant regions. As a result, smaller image areas will be used for LBP features calculation and consequently classification by SVM. We use IRMA 2009 dataset with 14,410 x-ray images to verify the performance of the proposed approach. The results demonstrate the benefits of saliency-based folding approach that delivers comparable classification accuracies with state-of-the-art but exhibits lower computational cost and storage requirements, factors highly important for big data analytics.Comment: To appear in proceedings of The 14th International Conference on Machine Learning and Applications (IEEE ICMLA 2015), Miami, Florida, USA, 201

Self-Configuring and Evolving Fuzzy Image Thresholding

Every segmentation algorithm has parameters that need to be adjusted in order to achieve good results. Evolving fuzzy systems for adjustment of segmentation parameters have been proposed recently (Evolving fuzzy image segmentation -- EFIS [1]. However, similar to any other algorithm, EFIS too suffers from a few limitations when used in practice. As a major drawback, EFIS depends on detection of the object of interest for feature calculation, a task that is highly application-dependent. In this paper, a new version of EFIS is proposed to overcome these limitations. The new EFIS, called self-configuring EFIS (SC-EFIS), uses available training data to auto-configure the parameters that are fixed in EFIS. As well, the proposed SC-EFIS relies on a feature selection process that does not require the detection of a region of interest (ROI).Comment: To appear in proceedings of The 14th International Conference on Machine Learning and Applications (IEEE ICMLA 2015), Miami, Florida, USA, 201

Autoencoding the Retrieval Relevance of Medical Images

Content-based image retrieval (CBIR) of medical images is a crucial task that can contribute to a more reliable diagnosis if applied to big data. Recent advances in feature extraction and classification have enormously improved CBIR results for digital images. However, considering the increasing accessibility of big data in medical imaging, we are still in need of reducing both memory requirements and computational expenses of image retrieval systems. This work proposes to exclude the features of image blocks that exhibit a low encoding error when learned by a $n/p/n$ autoencoder ($p\!<\!n$). We examine the histogram of autoendcoding errors of image blocks for each image class to facilitate the decision which image regions, or roughly what percentage of an image perhaps, shall be declared relevant for the retrieval task. This leads to reduction of feature dimensionality and speeds up the retrieval process. To validate the proposed scheme, we employ local binary patterns (LBP) and support vector machines (SVM) which are both well-established approaches in CBIR research community. As well, we use IRMA dataset with 14,410 x-ray images as test data. The results show that the dimensionality of annotated feature vectors can be reduced by up to 50% resulting in speedups greater than 27% at expense of less than 1% decrease in the accuracy of retrieval when validating the precision and recall of the top 20 hits.Comment: To appear in proceedings of The 5th International Conference on Image Processing Theory, Tools and Applications (IPTA'15), Nov 10-13, 2015, Orleans, Franc

Understanding social decision-making mechanisms using Markov Decision Processes

International audienc

Evaluation of vancomycin use in university-affiliated hospitals in Southern Khorasan Province (East Iran) based on HICPAC guidelines

Motahare Mahi-Birjand,1 Masood Ziaee,1 Bita Bijari,1 Reza Khalvati,2 Mohammad Reza Abedini,3 Hasan Golboei Mousavi,4 Arash Ziaee5 1Infectious Disease Research Center, Birjand University of Medical Sciences, Birjand, Iran; 2Food and Drug Administration, Mazandaran University of Medical Sciences, Mazandaran, Iran; 3Department of Pharmacology, Cellular and Molecular Research Center, School of Medicine, Birjand University of Medical Sciences, Birjand, Iran; 4Department of Otolaryngology, Birjand University of Medical Sciences, Birjand, Iran; 5Mashhad University of Medical Sciences, Mashhad, Iran Background: Vancomycin resistance has raised concerns about its effectiveness prospect in the treatment of patients with Gram-positive infections. The Healthcare Infection Control Practices Advisory Committee (HICPAC) has recently established guidelines to delineate improper use of vancomycin. In this light, we sought out to determine the appropriateness of vancomycin prescription using the HICPAC guidelines.Setting: The study was carried out in two university-affiliated hospitals, Valiasr and Imam Reza, with 297 and 234 beds, respectively, from May 2012 to May 2013. Methods: This retrospective study evaluated the vancomycin prescription and usage in the hospitals. Total vancomycin use was determined and expressed as vancomycin courses per 298 admitted patients. The patient information was collected on a data collection sheet as follows: demographic variables, etiology and localization of infection, microbiological data, duration of vancomycin treatment, reasons for vancomycin prescription, prescribed antibiotic dosing, and patient regimen. Results: The average age of the patients and vancomycin treatment duration were 55.965 years and 10.5 days, respectively. Septicemia (15.7%) was the most common cause of vancomycin administration. Vancomycin use was documented to be appropriate and inappropriate in 236 (89.4%) and 28 (10.6%) patients, respectively. No statistically significant differences were found among the wards and hospitals (P values =0.66 and 0.54, respectively) in terms of appropriateness of vancomycin use based on the HICPAC criteria. In addition, 29.21% and 62% of all patients exhibited complete and partial recovery, respectively. We found that 90% of the cases showed compliance with the HICPAC recommendations. Conclusion: Comprehensive programs are required to improve the vancomycin use in the hospitals. Vancomycin use should be monitored due to its large-scale empiric use. The rate of improper use of vancomycin in the infection and intensive care unit services may be high, and pharmacists must take appropriate action to optimize the use of the drug. Keywords: vancomycin, drug utilization, anti-bacterial agents, university hospital

Energy Comparison of Room Temperature and Superconducting Synchrotrons for Hadron Therapy

The yearly energy requirements of normal conducting (NC) and superconducting (SC) magnet options of a new hadron therapy (HT) facility are compared. Special reference is made to the layouts considered for the proposed SEEIIST facility. Benchmarking with the NC CNAO HT centre in Pavia (Italy) was carried out. The energy comparison is centred on the different synchrotron solutions, assuming the same injector and lines in the designs. The beam current is more than a factor 10 higher with respect to present generation facilities. This allows efficient ’multi-energy extraction’ (MEE), which shortens the therapy treatment and is needed especially in the SC option, because of the slow magnet ramping time. Hence, power values of the facility in the traditional mode were converted into MEE ones, for the sake of a fair stepwise comparison between NC and SC magnets. The use of cryocoolers and a liquefier are also compared, for synchrotron refrigeration. This study shows that a NC facility operated in MEE mode requires the least average energy, followed by the SC synchrotron solution with a liquefier, while the most energy intensive solution is the SC one with cryocoolers