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

Semiautomatic Detection of Scoliotic Rib Borders From Posteroanterior Chest Radiographs

3-D assessment of scoliotic deformities relies on an accurate 3-D reconstruction of bone structures from biplanar X-rays, which requires a precise detection and matching of anatomical structures in both views. In this paper, we propose a novel semiautomated technique for detecting complete scoliotic rib borders from PA-0° and PA-20° chest radiographs, by using an edge-following approach with multiple-path branching and oriented filtering. Edge-following processes are initiated from user starting points along upper and lower rib edges and the final rib border is obtained by finding the most parallel pair among detected edges. The method is based on a perceptual analysis leading to the assumption that no matter how bent a scoliotic rib is, it will always present relatively parallel upper and lower edges. The proposed method was tested on 44 chest radiographs of scoliotic patients and was validated by comparing pixels from all detected rib borders against their reference locations taken from the associated manually delineated rib borders. The overall 2-D detection accuracy was 2.64 ± 1.21 pixels. Comparing this accuracy level to reported results in the literature shows that the proposed method is very well suited for precisely detecting borders of scoliotic ribs from PA-0° and PA-20° chest radiographs.CIHR / IRS

Automatic Chest X-rays Analysis using Statistical Machine Learning Strategies

Tuberculosis (TB) is a disease responsible for the deaths of more than one million people worldwide every year. Even though it is preventable and curable, it remains a major threat to humanity that needs to be taken care of. It is often diagnosed in developed countries using approaches such as sputum smear microscopy and culture methods. However, since these approaches are rather expensive, they are not commonly used in poor regions of the globe such as India, Africa, and Bangladesh. Instead, the well known and affordable chest x-ray (CXR) interpretation by radiologists is the technique employed in those places. Nevertheless, if this method is obsolete in other parts of the world nowadays it is because of its many flaws including: i) it is a tedious task that requires experienced medical personnel --which is scarce given the high demand for it--, ii) it is manual and difficult when executed for a large population, and iii) it is prone to human error depending on the proficiency and aptitude of the interpreter. Researchers have thus been trying to overcome these challenges over the years by proposing software solutions that mainly involve computer vision, artificial intelligence, and machine learning. The problems with these existing solutions are that they are either complex or not reliable enough. The need for better solutions in this specific domain as well as my desire to bring my contribution to something meaningful are what led us to investigate in this direction. In this manuscript, I propose a simple fully automatic software solution that uses only machine learning and image processing to analyze and detect anomalies related to TB in CXR scans. My system starts by extracting the region of interest from the incoming images, then performs a computationally inexpensive yet efficient feature extraction that involves edge detection using Laplacian of Gaussian and positional information retention. The extracted features are then fed to a regular random forest classifier for discrimination. I tested the system on two benchmark data collections --Montgomery and Shenzhen-- and obtained state-of-the-art results that reach up to 97% classification accuracy

Classification of lung diseases using deep learning models

Although deep learning-based models show high performance in the medical field, they required large volumes of data which is problematic due to the protection of patient privacy and lack of publically available medical databases. In this thesis, we address the problem of medical data scarcity by considering the task of pulmonary disease detection in chest X-Ray images using small volume datasets (<1000 samples). We implement three deep convolution neural networks pre-trained on the ImageNet dataset (VGG16, ResNet-50, and InveptionV3) and asses them in the lung disease classification tasks transfer learning approach. We created a pipeline that applied segmentation on Chest X-Ray images before classifying them and we compared the performance of our framework with the existing one. We demonstrated that pre-trained models and simple classifiers such as shallow neural networks can compete with the complex systems. We also implemented activation maps for our system. The analysis of class activation maps shows that not only does the segmentation improve results in terms of accuracy but also focuses models on medically relevant areas of lungs. We validated our techniques on the publicly available Shenzhen and Montgomery datasets and compared them to the currently available solutions. Our method was able to reach the same level of accuracy as the best performing models trained on the Montgomery dataset however, the advantage of our approach is a smaller number of trainable parameters. What is more, our InceptionV3 based model almost tied with the best performing solution on the Shenzhen dataset but as previously, it is computationally less expensive

Modified Chrispin-Norman chest radiography score for cystic fibrosis: observer agreement and correlation with lung function

Contains fulltext : 96114.pdf ( ) (Closed access)OBJECTIVE: To test observer agreement and two strategies for possible improvement (consensus meeting and reference images) for the modified Chrispin-Norman score for children with cystic fibrosis (CF). METHODS: Before and after a consensus meeting and after developing reference images three observers scored sets of 25 chest radiographs from children with CF. Observer agreement was tested for line, ring, mottled and large soft shadows, for overinflation and for the composite modified Chrispin-Norman score. Correlation with lung function was assessed. RESULTS: Before the consensus meeting agreement between observers 1 and 2 was moderate-good, but with observer 3 agreement was poor-fair. Scores correlated significantly with spirometry for observers 1 and 2 (-0.72<R<-0.42, P < 0.05), but not for observer 3. Agreement with observer 3 improved after the consensus meeting. Reference images improved agreement for overinflation and mottled and large shadows and correlation with lung function, but agreement for the modified Chrispin-Norman score did not improve further. CONCLUSION: Consensus meetings and reference images improve among-observer agreement for the modified Chrispin-Norman score, but good agreement was not achieved among all observers for the modified Chrispin-Norman score and for bronchial line and ring shadows

Model-Based Edge Detector for Spectral Imagery Using Sparse Spatiospectral Masks

Two model-based algorithms for edge detection in spectral imagery are developed that specifically target capturing intrinsic features such as isoluminant edges that are characterized by a jump in color but not in intensity. Given prior knowledge of the classes of reflectance or emittance spectra associated with candidate objects in a scene, a small set of spectral-band ratios, which most profoundly identify the edge between each pair of materials, are selected to define a edge signature. The bands that form the edge signature are fed into a spatial mask, producing a sparse joint spatiospectral nonlinear operator. The first algorithm achieves edge detection for every material pair by matching the response of the operator at every pixel with the edge signature for the pair of materials. The second algorithm is a classifier-enhanced extension of the first algorithm that adaptively accentuates distinctive features before applying the spatiospectral operator. Both algorithms are extensively verified using spectral imagery from the airborne hyperspectral imager and from a dots-in-a-well midinfrared imager. In both cases, the multicolor gradient (MCG) and the hyperspectral/spatial detection of edges (HySPADE) edge detectors are used as a benchmark for comparison. The results demonstrate that the proposed algorithms outperform the MCG and HySPADE edge detectors in accuracy, especially when isoluminant edges are present. By requiring only a few bands as input to the spatiospectral operator, the algorithms enable significant levels of data compression in band selection. In the presented examples, the required operations per pixel are reduced by a factor of 71 with respect to those required by the MCG edge detector

Minimally Interactive Segmentation with Application to Human Placenta in Fetal MR Images

Placenta segmentation from fetal Magnetic Resonance (MR) images is important for fetal surgical planning. However, accurate segmentation results are difficult to achieve for automatic methods, due to sparse acquisition, inter-slice motion, and the widely varying position and shape of the placenta among pregnant women. Interactive methods have been widely used to get more accurate and robust results. A good interactive segmentation method should achieve high accuracy, minimize user interactions with low variability among users, and be computationally fast. Exploiting recent advances in machine learning, I explore a family of new interactive methods for placenta segmentation from fetal MR images. I investigate the combination of user interactions with learning from a single image or a large set of images. For learning from a single image, I propose novel Online Random Forests to efficiently leverage user interactions for the segmentation of 2D and 3D fetal MR images. I also investigate co-segmentation of multiple volumes of the same patient with 4D Graph Cuts. For learning from a large set of images, I first propose a deep learning-based framework that combines user interactions with Convolutional Neural Networks (CNN) based on geodesic distance transforms to achieve accurate segmentation and good interactivity. I then propose image-specific fine-tuning to make CNNs adaptive to different individual images and able to segment previously unseen objects. Experimental results show that the proposed algorithms outperform traditional interactive segmentation methods in terms of accuracy and interactivity. Therefore, they might be suitable for segmentation of the placenta in planning systems for fetal and maternal surgery, and for rapid characterization of the placenta by MR images. I also demonstrate that they can be applied to the segmentation of other organs from 2D and 3D images