A Computationally Efficient U-Net Architecture for Lung Segmentation in Chest Radiographs

Lung segmentation plays a crucial role in computer-aided diagnosis using Chest Radiographs (CRs). We implement a U-Net architecture for lung segmentation in CRs across multiple publicly available datasets. We utilize a private dataset with 160 CRs provided by the Riverain Medical Group for training purposes. A publicly available dataset provided by the Japanese Radiological Scientific Technology (JRST) is used for testing. The active shape model-based results would serve as the ground truth for both these datasets. In addition, we also study the performance of our algorithm on a publicly available Shenzhen dataset which contains 566 CRs with manually segmented lungs (ground truth). Our overall performance in terms of pixel-based classification is about 98.3% and 95.6% for a set of 100 CRs in Shenzhen dataset and 140 CRs in JRST dataset. We also achieve an intersection over union value of 0.95 at a computation time of 8 seconds for the entire suite of Shenzhen testing cases

A total variation-undecimated wavelet approach to chest radiograph image enhancement

Most often medical images such as X-Rays have a low dynamic range and many of their targeted features are difficult to identify. Intensity transformations that improve image quality usually rely onwavelet denoising and enhancement typically use the technique of thresholding to obtain better quality medical images. A disadvantage of wavelet thresholding is that even though it adequately removes noise in an image, it introduces unwanted artifacts into the image near discontinuities. We utilize a total variation method and an undecimated wavelet image enhancing algorithm for improving the image quality of chest radiographs. Our approach achieves a high level chest radiograph image deniosing in lung nodules detection while preserving the important features. Moreover, our method results in a high image sensitivity that reduces the average number of false positives on a test set of medical data

Medical imaging analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

Chest radiograph image enhancement with wavelet decomposition and morphological operations

Medical image processing algorithms significantly affect the precision ofdisease diagnostic process. This makes it crucial to improve the quality of a medical image with the goal to enhance perceivability of the points of interest in order to obtain accurate diagnosis of a patient.  Despite the reliance of various medical diagnostics on utilize X-rays, they are usually plagued by dark and low contrast properties. Sought-after  details in X-rays can only be accessed by means of digital image processing techniques, despite the fact that these techniques are far from being  perfect. In this paper, we implement a wavelet decomposition and reconstruction technique to enhance radiograph properties, some of which include contrast and noise, by using a series of morphological erosion and dilation to improve the visual quality of the chest radiographs for the detection of cancer nodules

Expert System with an Embedded Imaging Module for Diagnosing Lung Diseases

Lung diseases are one of the major causes of suffering and death in the world. Improved survival rate could be obtained if the diseases can be detected at its early stage. Specialist doctors with the expertise and experience to interpret medical images and diagnose complex lung diseases are scarce. In this work, a rule-based expert system with an embedded imaging module is developed to assist the general physicians in hospitals and clinics to diagnose lung diseases whenever the services of specialist doctors are not available. The rule-based expert system contains a large knowledge base of data from various categories such as patient's personal and medical history, clinical symptoms, clinical test results and radiological information. An imaging module is integrated into the expert system for the enhancement of chest X-Ray images. The goal of this module is to enhance the chest X-Ray images so that it can provide details similar to more expensive methods such as MRl and CT scan. A new algorithm which is a modified morphological grayscale top hat transform is introduced to increase the visibility of lung nodules in chest X-Rays. Fuzzy inference technique is used to predict the probability of malignancy of the nodules. The output generated by the expert system was compared with the diagnosis made by the specialist doctors. The system is able to produce results\ud which are similar to the diagnosis made by the doctors and is acceptable by clinical standards

Computer-aided diagnosis in chest radiography

Chest radiographs account for more than half of all radiological examinations; the chest is the mirror of health and disease. This thesis is about techniques for computer analysis of chest radiographs. It describes methods for texture analysis and segmenting the lung fields and rib cage in a chest film. It includes a description of an automatic system for detecting regions with abnormal texture, that is applied to a database of images from a tuberculosis screening program

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

Expert System with an Embedded Imaging Module for Diagnosing Lung Diseases

Lung diseases are one of the major causes of suffering and death in the world. Improved survival rate could be obtained if the diseases can be detected at its early stage. Specialist doctors with the expertise and experience to interpret medical images and diagnose complex lung diseases are scarce. In this work, a rule-based expert system with an embedded imaging module is developed to assist the general physicians in hospitals and clinics to diagnose lung diseases whenever the services of specialist doctors are not available. The rule-based expert system contains a large knowledge base of data from various categories such as patient's personal and medical history, clinical symptoms, clinical test results and radiological information. An imaging module is integrated into the expert system for the enhancement of chest X-Ray images. The goal of this module is to enhance the chest X-Ray images so that it can provide details similar to more expensive methods such as MRl and CT scan. A new algorithm which is a modified morphological grayscale top hat transform is introduced to increase the visibility of lung nodules in chest X-Rays. Fuzzy inference technique is used to predict the probability of malignancy of the nodules. The output generated by the expert system was compared with the diagnosis made by the specialist doctors. The system is able to produce results which are similar to the diagnosis made by the doctors and is acceptable by clinical standards