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

Exploiting Spatio-Temporal Coherence for Video Object Detection in Robotics

This paper proposes a method to enhance video object detection for indoor environments in robotics. Concretely, it exploits knowledge about the camera motion between frames to propagate previously detected objects to successive frames. The proposal is rooted in the concepts of planar homography to propose regions of interest where to find objects, and recursive Bayesian filtering to integrate observations over time. The proposal is evaluated on six virtual, indoor environments, accounting for the detection of nine object classes over a total of ∼ 7k frames. Results show that our proposal improves the recall and the F1-score by a factor of 1.41 and 1.27, respectively, as well as it achieves a significant reduction of the object categorization entropy (58.8%) when compared to a two-stage video object detection method used as baseline, at the cost of small time overheads (120 ms) and precision loss (0.92).</p

Developing and Applying CAD-generated Image Markers to Assist Disease Diagnosis and Prognosis Prediction

Developing computer-aided detection and/or diagnosis (CAD) schemes has been an active research topic in medical imaging informatics (MII) with promising results in assisting clinicians in making better diagnostic and/or clinical decisions in the last two decades. To build robust CAD schemes, we need to develop state-of-the-art image processing and machine learning (ML) algorithms to optimize each step in the CAD pipeline, including detection and segmentation of the region of interest, optimal feature generation, followed by integration to ML classifiers. In my dissertation, I conducted multiple studies investigating the feasibility of developing several novel CAD schemes in the field of medicine concerning different purposes. The first study aims to investigate how to optimally develop a CAD scheme of contrast-enhanced digital mammography (CEDM) images to classify breast masses. CEDM includes both low energy (LE) and dual-energy subtracted (DES) images. A CAD scheme was applied to segment mass regions depicting LE and DES images separately. Optimal segmentation results generated from DES images were also mapped to LE images or vice versa. After computing image features, multilayer perceptron-based ML classifiers integrated with a correlation-based feature subset evaluator and leave-one-case-out cross-validation method were built to classify mass regions. The study demonstrated that DES images eliminated the overlapping effect of dense breast tissue, which helps improve mass segmentation accuracy. By mapping mass regions segmented from DES images to LE images, CAD yields significantly improved performance. The second study aims to develop a new quantitative image marker computed from the pre-intervention computed tomography perfusion (CTP) images and evaluate its feasibility to predict clinical outcome among acute ischemic stroke (AIS) patients undergoing endovascular mechanical thrombectomy after diagnosis of large vessel occlusion. A CAD scheme is first developed to pre-process CTP images of different scanning series for each study case, perform image segmentation, quantify contrast-enhanced blood volumes in bilateral cerebral hemispheres, and compute image features related to asymmetrical cerebral blood flow patterns based on the cumulative cerebral blood flow curves of two hemispheres. Next, image markers based on a single optimal feature and ML models fused with multi-features are developed and tested to classify AIS cases into two classes of good and poor prognosis based on the Modified Rankin Scale. The study results show that ML model trained using multiple features yields significantly higher classification performance than the image marker using the best single feature (p<0.01). This study demonstrates the feasibility of developing a new CAD scheme to predict the prognosis of AIS patients in the hyperacute stage, which has the potential to assist clinicians in optimally treating and managing AIS patients. The third study aims to develop and test a new CAD scheme to predict prognosis in aneurysmal subarachnoid hemorrhage (aSAH) patients using brain CT images. Each patient had two sets of CT images acquired at admission and prior to discharge. CAD scheme was applied to segment intracranial brain regions into four subregions, namely, cerebrospinal fluid (CSF), white matter (WM), gray matter (GM), and extraparenchymal blood (EPB), respectively. CAD then computed nine image features related to 5 volumes of the segmented sulci, EPB, CSF, WM, GM, and four volumetrical ratios to sulci. Subsequently, 16 ML models were built using multiple features computed either from CT images acquired at admission or prior to discharge to predict eight prognosis related parameters. The results show that ML models trained using CT images acquired at admission yielded higher accuracy to predict short-term clinical outcomes, while ML models trained using CT images acquired prior to discharge had higher accuracy in predicting long-term clinical outcomes. Thus, this study demonstrated the feasibility of predicting the prognosis of aSAH patients using new ML model-generated quantitative image markers. The fourth study aims to develop and test a new interactive computer-aided detection (ICAD) tool to quantitatively assess hemorrhage volumes. After loading each case, the ICAD tool first segments intracranial brain volume, performs CT labeling of each voxel. Next, contour-guided image-thresholding techniques based on CT Hounsfield Unit are used to estimate and segment hemorrhage-associated voxels (ICH). Next, two experienced neurology residents examine and correct the markings of ICH categorized into either intraparenchymal hemorrhage (IPH) or intraventricular hemorrhage (IVH) to obtain the true markings. Additionally, volumes and maximum two-dimensional diameter of each sub-type of hemorrhage are also computed for understanding ICH prognosis. The performance to segment hemorrhage regions between semi-automated ICAD and the verified neurology residents’ true markings is evaluated using dice similarity coefficient (DSC). The data analysis results in the study demonstrate that the new ICAD tool enables to segment and quantify ICH and other hemorrhage volumes with higher DSC. Finally, the fifth study aims to bridge the gap between traditional radiomics and deep learning systems by comparing and assessing these two technologies in classifying breast lesions. First, one CAD scheme is applied to segment lesions and compute radiomics features. In contrast, another scheme applies a pre-trained residual net architecture (ResNet50) as a transfer learning model to extract automated features. Next, the principal component algorithm processes both initially computed radiomics and automated features to create optimal feature vectors. Then, several support vector machine (SVM) classifiers are built using the optimized radiomics or automated features. This study indicates that (1) CAD built using only deep transfer learning yields higher classification performance than the traditional radiomic-based model, (2) SVM trained using the fused radiomics and automated features does not yield significantly higher AUC, and (3) radiomics and automated features contain highly correlated information in lesion classification. In summary, in all these studies, I developed and investigated several key concepts of CAD pipeline, including (i) pre-processing algorithms, (ii) automatic detection and segmentation schemes, (iii) feature extraction and optimization methods, and (iv) ML and data analysis models. All developed CAD models are embedded with interactive and visually aided graphical user interfaces (GUIs) to provide user functionality. These techniques present innovative approaches for building quantitative image markers to build optimal ML models. The study results indicate the underlying CAD scheme's potential application to assist radiologists in clinical settings for their assessments in diagnosing disease and improving their overall performance

Gaze-Based Human-Robot Interaction by the Brunswick Model

We present a new paradigm for human-robot interaction based on social signal processing, and in particular on the Brunswick model. Originally, the Brunswick model copes with face-to-face dyadic interaction, assuming that the interactants are communicating through a continuous exchange of non verbal social signals, in addition to the spoken messages. Social signals have to be interpreted, thanks to a proper recognition phase that considers visual and audio information. The Brunswick model allows to quantitatively evaluate the quality of the interaction using statistical tools which measure how effective is the recognition phase. In this paper we cast this theory when one of the interactants is a robot; in this case, the recognition phase performed by the robot and the human have to be revised w.r.t. the original model. The model is applied to Berrick, a recent open-source low-cost robotic head platform, where the gazing is the social signal to be considered

Artificial Intelligence and Cognitive Computing

Artificial intelligence (AI) is a subject garnering increasing attention in both academia and the industry today. The understanding is that AI-enhanced methods and techniques create a variety of opportunities related to improving basic and advanced business functions, including production processes, logistics, financial management and others. As this collection demonstrates, AI-enhanced tools and methods tend to offer more precise results in the fields of engineering, financial accounting, tourism, air-pollution management and many more. The objective of this collection is to bring these topics together to offer the reader a useful primer on how AI-enhanced tools and applications can be of use in today’s world. In the context of the frequently fearful, skeptical and emotion-laden debates on AI and its value added, this volume promotes a positive perspective on AI and its impact on society. AI is a part of a broader ecosystem of sophisticated tools, techniques and technologies, and therefore, it is not immune to developments in that ecosystem. It is thus imperative that inter- and multidisciplinary research on AI and its ecosystem is encouraged. This collection contributes to that

NOTIFICATION !!!

All the content of this special edition is retrieved from the conference proceedings published by the European Scientific Institute, ESI. http://eujournal.org/index.php/esj/pages/view/books The European Scientific Journal, ESJ, after approval from the publisher re publishes the papers in a Special edition