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

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

Image Area Reduction for Efficient Medical Image Retrieval

Content-based image retrieval (CBIR) has been one of the most active areas in medical image analysis in the last two decades because of the steadily increase in the number of digital images used. Efficient diagnosis and treatment planning can be supported by developing retrieval systems to provide high-quality healthcare. Extensive research has attempted to improve the image retrieval efficiency. The critical factors when searching in large databases are time and storage requirements. In general, although many methods have been suggested to increase accuracy, fast retrieval has been rather sporadically investigated. In this thesis, two different approaches are proposed to reduce both time and space requirements for medical image retrieval. The IRMA data set is used to validate the proposed methods. Both methods utilized Local Binary Pattern (LBP) histogram features which are extracted from 14,410 X-ray images of IRMA dataset. The first method is image folding that operates based on salient regions in an image. Saliency is determined by a context-aware saliency algorithm which includes folding the image. After the folding process, the reduced image area is used to extract multi-block and multi-scale LBP features and to classify these features by multi-class Support vector machine (SVM). The other method consists of classification and distance-based feature similarity. Images are firstly classified into general classes by utilizing LBP features. Subsequently, the retrieval is performed within the class to locate the most similar images. Between the retrieval and classification processes, LBP features are eliminated by employing the error histogram of a shallow (n/p/n) autoencoder to quantify the retrieval relevance of image blocks. If the region is relevant, the autoencoder gives large error for its decoding. Hence, via examining the autoencoder error of image blocks, irrelevant regions can be detected and eliminated. In order to calculate similarity within general classes, the distance between the LBP features of relevant regions is calculated. The results show that the retrieval time can be reduced, and the storage requirements can be lowered without significant decrease in accuracy

Deep Learning-Based Driver Behavior Detection on Simulated and Real Data

Driver behaviour has a significant influence on vehicle accidents. Measuring and providing feedback on driver behaviour can provide significant benefits for understanding and improving road safety. Mobile phones can be leveraged for the detection of driver actions and characteristics from the broadest population of drivers. Mobile phones also offer easy accessibility for cost-effective and reliable information with the built-in sensors available on them, such as the Global Positional System (GPS) and Inertial Measurement Unit (IMU). However, when it comes to a larger scale, obtaining labelled data from these mobile devices is still far from optimal for low-cost and reliable applications due to noise and missing data. In this study, data obtained from mobile phone sensors is simulated as a time series dataset using a traffic simulator and a robotic simulator. Then, the dataset is used with deep learning methods to classify both manoeuvres and driver behaviours, focusing specifically on aggressive driver behaviour. We propose a novel method using two Convolutional Neural Networks Convolutional Neural Networks (CNN) working in parallel to classify driver behaviours while classifying manoeuvres (i.e., aggressive right lane-change). We claim that the Parallel Convolutional Neural Network (PCNN) not only speeds up training time but also increases performance since having information about the manoeuvre helps improve behaviour classification performance. To validate this, first, a single task CNN for manoeuvre classification and a single task CNN aggressive/non-aggressive behaviour classifiers were built separately. The utility of the classifiers was demonstrated on a large simulated dataset created using the Sumo and Webots simulators. Subsequently, the PCNN classifier has been trained and validated on the big simulated dataset and a small driven dataset. We have also collected a dataset driven on real road by using GPS and IMU sensors and the PCNN model has been tested on this real dataset to investigate whether a classifier trained on simulated data can generalize to real data. In addition to this method, we propose a method of Spatial CNN with Attention (SCNN-A) layer to apply to our time series data with extracting more high-level features from spatial data for classification purposes

Plasma total oxidant and antioxidant status after oral glucose tolerance and mixed meal tests in patients with polycystic ovary syndrome

WOS: 000383571900007PubMed ID: 27300033Purpose Insulin resistance (IR) and increased oxidative stress (OS) are the characteristics of polycystic ovary syndrome (PCOS). In this study, we aimed to evaluate the effects of oral glucose tolerance (OGTT) and mixed meal tests (MMT) on plasma total oxidant (TOS) and total antioxidant status (TAS) in patients with PCOS and the relationship between these parameters and IR, calculated via homeostasis of model assessment-IR (HOMA-IR) and Matsuda's insulin sensitivity index (ISI) derived from OGTT and MMT. Methods Twenty-two patients with PCOS, and age- and body mass index (BMI)-matched 20 women as controls were enrolled into the study. Five-hour OGTT and MMT were performed on different days, and before and after these tests, plasma TOS and TAS levels were investigated. IR was calculated with HOMA-IR and Matsuda's ISI. Results HOMA-IR levels were higher in patients with PCOS, compared to controls, while Matsuda's ISI derived from OGTT and MMT was higher in controls. Plasma TOS levels before OGTT and MMT were higher in patients with PCOS than controls, while TAS levels were similar. After OGTT, plasma TOS levels became decreased at 5th hour, when compared to baseline values in PCOS group. Likewise, the same decrement was found in controls, but the decrement was not significant. After OGTT and MMT at 5th hour, no changes were observed in TAS levels, compared to baseline. Conclusion Matsuda's ISIs derived from OGTT and MMT can be used instead of each other, and interestingly, we found a decrease in TOS levels after OGTT in patients with PCOS.Konya Training and Research Hospital's Research FundThis study was supported by Konya Training and Research Hospital's Research Fund