MR Image Based Approach for Metal Artifact Reduction in X-Ray CT

For decades, computed tomography (CT) images have been widely used to discover valuable anatomical information. Metallic implants such as dental fillings cause severe streaking artifacts which significantly degrade the quality of CT images. In this paper, we propose a new method for metal-artifact reduction using complementary magnetic resonance (MR) images. The method exploits the possibilities which arise from the use of emergent trimodality systems. The proposed algorithm corrects reconstructed CT images. The projected data which is affected by dental fillings is detected and the missing projections are replaced with data obtained from a corresponding MR image. A simulation study was conducted in order to compare the reconstructed images with images reconstructed through linear interpolation, which is a common metal-artifact reduction technique. The results show that the proposed method is successful in reducing severe metal artifacts without introducing significant amount of secondary artifacts

Evaluating the Role of Content in Subjective Video Quality Assessment

Video quality as perceived by human observers is the ground truth when Video Quality Assessment (VQA) is in question. It is dependent on many variables, one of them being the content of the video that is being evaluated. Despite the evidence that content has an impact on the quality score the sequence receives from human evaluators, currently available VQA databases mostly comprise of sequences which fail to take this into account. In this paper, we aim to identify and analyze differences between human cognitive, affective, and conative responses to a set of videos commonly used for VQA and a set of videos specifically chosen to include video content which might affect the judgment of evaluators when perceived video quality is in question. Our findings indicate that considerable differences exist between the two sets on selected factors, which leads us to conclude that videos starring a different type of content than the currently employed ones might be more appropriate for VQA

Feature selection and extraction in sequence labeling for arrhythmia detection

Automated Electrocardiogram (ECG)-based arrhythmia detection methods replace traditional, manual arrhythmia detection reducing the requirement for trained medical staff. Traditionally, ECG-based arrhythmia detection is performed via QRS complex detection followed by feature extraction, based on hand-crafted features, such as RR-intervals, Fast Fourier Transform-based features, wavelet analysis, higher order statistics and Hermite features. After the features are extracted, the ECG segments are classified into pre-defined categories. This study investigates the value of the feature extraction and selection methods for ECG-based arrhythmia detection. That is, with the emerging trend of deep learning methods which are capable of automatic feature extraction and selection, the research question addressed in this paper is if good classification performance can be obtained by feeding the raw ECG sequence directly into robust classifiers or handcrafted feature extraction/selection is necessary. Classification performance across a range of state-of-the-art classification methods indicates that feeding raw signals into the convolution neural network-based classifiers usually leads to the best performance but at the expense of high inference time

Customer Churn Prediction in B2B Non-Contractual Business Settings Using Invoice Data

Customer churn is a problem virtually all companies face, and the ability to predict it reliably can be a cornerstone for successful retention campaigns. In this study, we propose an approach to customer churn prediction in non-contractual B2B settings that relies exclusively on invoice-level data for feature engineering and uses multi-slicing to maximally utilize available data. We cast churn as a binary classification problem and assess the ability of three established classifiers to predict it when using different churn definitions. We also compare classifier performance when different amounts of historical data are used for feature engineering. The results indicate that robust models for different churn definitions can be derived by using invoice-level data alone and that using more historical data for creating some of the features tends to lead to better performing models for some classifiers. We also confirm that the multi-slicing approach to dataset creation yields better performing models compared to the traditionally used single-slicing approach

Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification

The latest generation of convolutional neural networks (CNNs) has achieved impressive results in the field of image classification. This paper is concerned with a new approach to the development of plant disease recognition model, based on leaf image classification, by the use of deep convolutional networks. Novel way of training and the methodology used facilitate a quick and easy system implementation in practice. The developed model is able to recognize 13 different types of plant diseases out of healthy leaves, with the ability to distinguish plant leaves from their surroundings. According to our knowledge, this method for plant disease recognition has been proposed for the first time. All essential steps required for implementing this disease recognition model are fully described throughout the paper, starting from gathering images in order to create a database, assessed by agricultural experts. Caffe, a deep learning framework developed by Berkley Vision and Learning Centre, was used to perform the deep CNN training. The experimental results on the developed model achieved precision between 91% and 98%, for separate class tests, on average 96.3%

Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification

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