Digital chest radiography: an update on modern technology, dose containment and control of image quality

The introduction of digital radiography not only has revolutionized communication between radiologists and clinicians, but also has improved image quality and allowed for further reduction of patient exposure. However, digital radiography also poses risks, such as unnoticed increases in patient dose and suboptimum image processing that may lead to suppression of diagnostic information. Advanced processing techniques, such as temporal subtraction, dual-energy subtraction and computer-aided detection (CAD) will play an increasing role in the future and are all targeted to decrease the influence of distracting anatomic background structures and to ease the detection of focal and subtle lesions. This review summarizes the most recent technical developments with regard to new detector techniques, options for dose reduction and optimized image processing. It explains the meaning of the exposure indicator or the dose reference level as tools for the radiologist to control the dose. It also provides an overview over the multitude of studies conducted in recent years to evaluate the options of these new developments to realize the principle of ALARA. The focus of the review is hereby on adult applications, the relationship between dose and image quality and the differences between the various detector systems

Diseases of the Chest, Breast, Heart and Vessels 2019-2022

This open access book focuses on diagnostic and interventional imaging of the chest, breast, heart, and vessels. It consists of a remarkable collection of contributions authored by internationally respected experts, featuring the most recent diagnostic developments and technological advances with a highly didactical approach. The chapters are disease-oriented and cover all the relevant imaging modalities, including standard radiography, CT, nuclear medicine with PET, ultrasound and magnetic resonance imaging, as well as imaging-guided interventions. As such, it presents a comprehensive review of current knowledge on imaging of the heart and chest, as well as thoracic interventions and a selection of "hot topics". The book is intended for radiologists, however, it is also of interest to clinicians in oncology, cardiology, and pulmonology

A computer aided diagnosis system for lung nodules detection in postero anterior chest radiographs

This thesis describes a Computer Aided System aimed at lung nodules detection. The fully automatized method developed to search for nodules is composed by four steps. They are the segmentation of the lung field, the enhancement of the image, the extraction of the candidate regions, and the selection between them of the regions with the highest chance to be True Positives. The steps of segmentation, enhancement and candidates extraction are based on multi-scale analysis. The common assumption underlying their development is that the signal representing the details to be detected by each of them (lung borders or nodule regions) is composed by a mixture of more simple signals belonging to different scales and level of details. The last step of candidate region classification is the most complicate; its 8 task is to discern among a high number of candidate regions, the few True Positives. To this aim several features and different classifiers have been investigated. In Chapter 1 the segmentation algorithm is described; the algorithm has been tested on the images of two different databases, the JSRT and the Niguarda database, both described in the next section, for a total of 409 images. We compared the results obtained with another method presented in the literature and described by Ginneken, in [85], as the one obtaining the best performance at the state of the art; it has been tested on the same images of the JSRT database. No errors have been detected in the results obtained by our method, meanwhile the one previously mentioned produced an overall number of error equal to 50. Also the results obtained on the images of the Niguarda database confirmed the efficacy of the system realized, allowing us to say that this is the best method presented so far in the literature. This sentence is based also on the fact that this is the only system tested on such an amount of images, and they are belonging to two different databases. Chapter 2 is aimed at the description of the multi-scale enhancement and the extraction methods. The enhancement allows to produce an image where the \u201cconspicuity\u201d of nodules is increased, so that nodules of different sizes and located in parts of the lungs characterized by completely different anatomic noise are more visible. Based on the same assumption the candidates extraction procedure, described in the same chapter, employs a multi-scale method to detect all the nodules of different sizes. Also this step has been compared with two methods ([8] and [1]) described in the literature and tested on the same images. Our implementation of the first one of them ([8]) produced really poor results; the second one obtained a sensitivity ratio (See Appendix C for its definition) equal to 86%. The considerably better performance of our method is proved by the fact that the sensitivity ratio we obtained is much higher (it is equal to 97%) and also the number of False positives detected is much less. The experiments aimed at the classification of the candidates are described in chapter 3; both a rule based technique and 2 learning systems, the Multi Layer Perceptron (MLP) and the Support Vector Machine (SVM), have been investigated. Their input is a set of 16 features. The rule based system obtained the best performance: the cardinality of the set of candidates left is highly reduced without lowering the sensitivity of the system, since no True Positive region is lost. It can be added that this performance is much better than the one of the system used by Ginneken and Schilam in [1], since its sensitivity is lower (equal to 77%) and the number of False Positive left is comparable. The drawback of a rule based system is the need of setting the 9 thresholds used by the rules; since they are experimentally set the system is dependent on the images used to develop it. Therefore it may happen that, on different databases, the performance could not be so good. The result of the MLPs and of the SVMs are described in detail and the ROC analysis is also reported, regarding the experiments performed with the SVMs. Furthermore, the attempt to improve the performance of the classification leaded to other experiments employing SVMs trained with more complicate feature sets. The results obtained, since not better than the previous, showed the need of a proper selection of the features. Future works will then be focused at testing other sets of features, and their combination obtained by means of proper feature selection techniques

Implementation and evaluation of a bony structure suppression software tool for chest X-ray imaging

Includes abstract.Includes bibliographical references.This project proposed to implement a bony structure suppression tool and analyse its effects on a texture-based classification algorithm in order to assist in the analysis of chest X-ray images. The diagnosis of pulmonary tuberculosis (TB) often includes the evaluation of chest X-ray images, and the reliability of image interpretation depends upon the experience of the radiologist. Computer-aided diagnosis (CAD) may be used to increase the accuracy of diagnosis. Overlapping structures in chest X-ray images hinder the ability of lung texture analysis for CAD to detect abnormalities. This dissertation examines whether the performance of texturebased CAD tools may be improved by the suppression of bony structures, particularly of the ribs, in the chest region

Diseases of the Chest, Breast, Heart and Vessels 2019-2022

This open access book focuses on diagnostic and interventional imaging of the chest, breast, heart, and vessels. It consists of a remarkable collection of contributions authored by internationally respected experts, featuring the most recent diagnostic developments and technological advances with a highly didactical approach. The chapters are disease-oriented and cover all the relevant imaging modalities, including standard radiography, CT, nuclear medicine with PET, ultrasound and magnetic resonance imaging, as well as imaging-guided interventions. As such, it presents a comprehensive review of current knowledge on imaging of the heart and chest, as well as thoracic interventions and a selection of "hot topics". The book is intended for radiologists, however, it is also of interest to clinicians in oncology, cardiology, and pulmonology

Full-resolution Lung Nodule Segmentation from Chest X-ray Images using Residual Encoder-Decoder Networks

Lung cancer is the leading cause of cancer death and early diagnosis is associated with a positive prognosis. Chest X-ray (CXR) provides an inexpensive imaging mode for lung cancer diagnosis. Suspicious nodules are difficult to distinguish from vascular and bone structures using CXR. Computer vision has previously been proposed to assist human radiologists in this task, however, leading studies use down-sampled images and computationally expensive methods with unproven generalization. Instead, this study localizes lung nodules using efficient encoder-decoder neural networks that process full resolution images to avoid any signal loss resulting from down-sampling. Encoder-decoder networks are trained and tested using the JSRT lung nodule dataset. The networks are used to localize lung nodules from an independent external CXR dataset. Sensitivity and false positive rates are measured using an automated framework to eliminate any observer subjectivity. These experiments allow for the determination of the optimal network depth, image resolution and pre-processing pipeline for generalized lung nodule localization. We find that nodule localization is influenced by subtlety, with more subtle nodules being detected in earlier training epochs. Therefore, we propose a novel self-ensemble model from three consecutive epochs centered on the validation optimum. This ensemble achieved a sensitivity of 85% in 10-fold internal testing with false positives of 8 per image. A sensitivity of 81% is achieved at a false positive rate of 6 following morphological false positive reduction. This result is comparable to more computationally complex systems based on linear and spatial filtering, but with a sub-second inference time that is faster than other methods. The proposed algorithm achieved excellent generalization results against an external dataset with sensitivity of 77% at a false positive rate of 7.6

Eigenimage Processing of Frontal Chest Radiographs

The goal of this research was to improve the speed and accuracy of reporting by clinical radiologists. By applying a technique known as eigenimage processing to chest radiographs, abnormal findings were enhanced and a classification scheme developed. Results confirm that the method is feasible for clinical use. Eigenimage processing is a popular face recognition routine that has only recently been applied to medical images, but it has not previously been applied to full size radiographs. Chest radiographs were chosen for this research because they are clinically important and are challenging to process due to their large data content. It is hoped that the success with these images will enable future work on other medical images such as those from CT and MRI. Eigenimage processing is based on a multivariate statistical method which identifies patterns of variance within a training set of images. Specifically it involves the application of a statistical technique called principal components analysis to a training set. For this research, the training set was a collection of 77 normal radiographs. This processing produced a set of basis images, known as eigenimages, that best describe the variance within the training set of normal images. For chest radiographs the basis images may also be referred to as 'eigenchests'. Images to be tested were described in terms of eigenimages. This identified patterns of variance likely to be normal. A new image, referred to as the remainder image, was derived by removing patterns of normal variance, thus making abnormal patterns of variance more conspicuous. The remainder image could either be presented to clinicians or used as part of a computer aided diagnosis system. For the image sets used, the discriminatory power of a classification scheme approached 90%. While the processing of the training set required significant computation time, each test image to be classified or enhanced required only a few seconds to process. Thus the system could be integrated into a clinical radiology department

Bridging the gap between Natural and Medical Images through Deep Colorization

Deep learning has thrived by training on large-scale datasets. However, in many applications, as for medical image diagnosis, getting massive amount of data is still prohibitive due to privacy, lack of acquisition homogeneity and annotation cost. In this scenario, transfer learning from natural image collections is a standard practice that attempts to tackle shape, texture and color discrepancies all at once through pretrained model fine-tuning. In this work, we propose to disentangle those challenges and design a dedicated network module that focuses on color adaptation. We combine learning from scratch of the color module with transfer learning of different classification backbones, obtaining an end-to-end, easy-to-train architecture for diagnostic image recognition on X-ray images. Extensive experiments showed how our approach is particularly efficient in case of data scarcity and provides a new path for further transferring the learned color information across multiple medical datasets