A NEW METHOD FOR PREDICTING EARLY-STAGE LUNG NODULES BASED ON PSO-SVM HYBRID ALGORITHM

The aim of this article was to use the Support Vector Machine (SVM) to predict the benign and malignant solitary pulmonary nodules (SPNs) in early-stage lung cancer in order to lessen the patient’s pain and save the money. Fifty and one patient records were collected .Each record consisted of four clinical characteristics and nine morphological characteristics. The SVM classifier was built by radial basis kernel function. The penalty factor C and kernel parameter σ were optimized by comparing particle swarm optimization (PSO), grid search algorithm (GSA) and genetic algorithm (GA)and then employed to diagnose the SPNs. By comparison with a Logistic regression (LR) model, the overall results of our calculation demonstrated that the area under the receiver operator characteristic (ROC) curve for the model (0.913 ± 0.051, p\u3c0.05) was higher than the LR model. The accuracy, sensitivity and specificity in the model were 90.7%, 89.3% and 93.3% respectively. It is represented that the PSO-SVM model can be used in predicting the early-stage lung nodules

Multifractal techniques for analysis and classification of emphysema images

This thesis proposes, develops and evaluates different multifractal methods for detection, segmentation and classification of medical images. This is achieved by studying the structures of the image and extracting the statistical self-similarity measures characterized by the Holder exponent, and using them to develop texture features for segmentation and classification. The theoretical framework for fulfilling these goals is based on the efficient computation of fractal dimension, which has been explored and extended in this work. This thesis investigates different ways of computing the fractal dimension of digital images and validates the accuracy of each method with fractal images with predefined fractal dimension. The box counting and the Higuchi methods are used for the estimation of fractal dimensions. A prototype system of the Higuchi fractal dimension of the computed tomography (CT) image is used to identify and detect some of the regions of the image with the presence of emphysema. The box counting method is also used for the development of the multifractal spectrum and applied to detect and identify the emphysema patterns. We propose a multifractal based approach for the classification of emphysema patterns by calculating the local singularity coefficients of an image using four multifractal intensity measures. One of the primary statistical measures of self-similarity used in the processing of tissue images is the Holder exponent (α-value) that represents the power law, which the intensity distribution satisfies in the local pixel neighbourhoods. The fractal dimension corresponding to each α-value gives a multifractal spectrum f(α) that was used as a feature descriptor for classification. A feature selection technique is introduced and implemented to extract some of the important features that could increase the discriminating capability of the descriptors and generate the maximum classification accuracy of the emphysema patterns. We propose to further improve the classification accuracy of emphysema CT patterns by combining the features extracted from the alpha-histograms and the multifractal descriptors to generate a new descriptor. The performances of the classifiers are measured by using the error matrix and the area under the receiver operating characteristic curve (AUC). The results at this stage demonstrated the proposed cascaded approach significantly improves the classification accuracy. Another multifractal based approach using a direct determination approach is investigated to demonstrate how multifractal characteristic parameters could be used for the identification of emphysema patterns in HRCT images. This further analysis reveals the multi-scale structures and characteristic properties of the emphysema images through the generalized dimensions. The results obtained confirm that this approach can also be effectively used for detecting and identifying emphysema patterns in CT images. Two new descriptors are proposed for accurate classification of emphysema patterns by hybrid concatenation of the local features extracted from the local binary patterns (LBP) and the global features obtained from the multifractal images. The proposed combined feature descriptors of the LBP and f(α) produced a very good performance with an overall classification accuracy of 98%. These performances outperform other state-of-the-art methods for emphysema pattern classification and demonstrate the discriminating power and robustness of the combined features for accurate classification of emphysema CT images. Overall, experimental results have shown that the multifractal could be effectively used for the classifications and detections of emphysema patterns in HRCT images

Computational methods for the analysis of functional 4D-CT chest images.

Medical imaging is an important emerging technology that has been intensively used in the last few decades for disease diagnosis and monitoring as well as for the assessment of treatment effectiveness. Medical images provide a very large amount of valuable information that is too huge to be exploited by radiologists and physicians. Therefore, the design of computer-aided diagnostic (CAD) system, which can be used as an assistive tool for the medical community, is of a great importance. This dissertation deals with the development of a complete CAD system for lung cancer patients, which remains the leading cause of cancer-related death in the USA. In 2014, there were approximately 224,210 new cases of lung cancer and 159,260 related deaths. The process begins with the detection of lung cancer which is detected through the diagnosis of lung nodules (a manifestation of lung cancer). These nodules are approximately spherical regions of primarily high density tissue that are visible in computed tomography (CT) images of the lung. The treatment of these lung cancer nodules is complex, nearly 70% of lung cancer patients require radiation therapy as part of their treatment. Radiation-induced lung injury is a limiting toxicity that may decrease cure rates and increase morbidity and mortality treatment. By finding ways to accurately detect, at early stage, and hence prevent lung injury, it will have significant positive consequences for lung cancer patients. The ultimate goal of this dissertation is to develop a clinically usable CAD system that can improve the sensitivity and specificity of early detection of radiation-induced lung injury based on the hypotheses that radiated lung tissues may get affected and suffer decrease of their functionality as a side effect of radiation therapy treatment. These hypotheses have been validated by demonstrating that automatic segmentation of the lung regions and registration of consecutive respiratory phases to estimate their elasticity, ventilation, and texture features to provide discriminatory descriptors that can be used for early detection of radiation-induced lung injury. The proposed methodologies will lead to novel indexes for distinguishing normal/healthy and injured lung tissues in clinical decision-making. To achieve this goal, a CAD system for accurate detection of radiation-induced lung injury that requires three basic components has been developed. These components are the lung fields segmentation, lung registration, and features extraction and tissue classification. This dissertation starts with an exploration of the available medical imaging modalities to present the importance of medical imaging in today’s clinical applications. Secondly, the methodologies, challenges, and limitations of recent CAD systems for lung cancer detection are covered. This is followed by introducing an accurate segmentation methodology of the lung parenchyma with the focus of pathological lungs to extract the volume of interest (VOI) to be analyzed for potential existence of lung injuries stemmed from the radiation therapy. After the segmentation of the VOI, a lung registration framework is introduced to perform a crucial and important step that ensures the co-alignment of the intra-patient scans. This step eliminates the effects of orientation differences, motion, breathing, heart beats, and differences in scanning parameters to be able to accurately extract the functionality features for the lung fields. The developed registration framework also helps in the evaluation and gated control of the radiotherapy through the motion estimation analysis before and after the therapy dose. Finally, the radiation-induced lung injury is introduced, which combines the previous two medical image processing and analysis steps with the features estimation and classification step. This framework estimates and combines both texture and functional features. The texture features are modeled using the novel 7th-order Markov Gibbs random field (MGRF) model that has the ability to accurately models the texture of healthy and injured lung tissues through simultaneously accounting for both vertical and horizontal relative dependencies between voxel-wise signals. While the functionality features calculations are based on the calculated deformation fields, obtained from the 4D-CT lung registration, that maps lung voxels between successive CT scans in the respiratory cycle. These functionality features describe the ventilation, the air flow rate, of the lung tissues using the Jacobian of the deformation field and the tissues’ elasticity using the strain components calculated from the gradient of the deformation field. Finally, these features are combined in the classification model to detect the injured parts of the lung at an early stage and enables an earlier intervention

Quantitative Evaluation of Pulmonary Emphysema Using Magnetic Resonance Imaging and x-ray Computed Tomography

Chronic obstructive pulmonary disease (COPD) is a leading cause of morbidity and mortality affecting at least 600 million people worldwide. The most widely used clinical measurements of lung function such as spirometry and plethysmography are generally accepted for diagnosis and monitoring of the disease. However, these tests provide only global measures of lung function and they are insensitive to early disease changes. Imaging tools that are currently available have the potential to provide regional information about lung structure and function but at present are mainly used for qualitative assessment of disease and disease progression. In this thesis, we focused on the application of quantitative measurements of lung structure derived from 1H magnetic resonance imaging (MRI) and high resolution computed tomography (CT) in subjects diagnosed with COPD by a physician. Our results showed that significant and moderately strong relationship exists between 1H signal intensity (SI) and 3He apparent diffusion coefficient (ADC), as well as between 1H SI and CT measurements of emphysema. This suggests that these imaging methods may be quantifying the same tissue changes in COPD, and that pulmonary 1H SI may be used effectively to monitor emphysema as a complement to CT and noble gas MRI. Additionally, our results showed that objective multi-threshold analysis of CT images for emphysema scoring that takes into account the frequency distribution of each Hounsfield unit (HU) threshold was effective in correctly classifying the patient into COPD and healthy subgroups. Finally, we found a significant correlation between whole lung average subjective and objective emphysema scores with high inter-observer agreement. It is concluded that 1H MRI and high resolution CT can be used to quantitatively evaluate lung tissue alterations in COPD subjects

Image processing in medicine advances for phenotype characterization, computer-assisted diagnosis and surgical planning

En esta Tesis presentamos nuestras contribuciones al estado del arte en procesamiento digital de imágenes médicas, articulando nuestra exposición en torno a los tres principales objetivos de la adquisición de imágenes en medicina: la prevención, el diagnóstico y el tratamiento de las enfermedades. La prevención de la enfermedad se puede conseguir a veces mediante una caracterización cuidadosa de los fenotipos propios de la misma. Tal caracterización a menudo se alcanza a partir de imágenes. Presentamos nuestro trabajo en caracterización del enfisema pulmonar a partir de imágenes TAC (Tomografía Axial Computerizada) de tórax en alta resolución, a través del análisis de las texturas locales de la imagen. Nos proponemos llenar el vacío existente entre la práctica clínica actual, y las sofisticadas pero costosas técnicas de caracterización de regiones texturadas, disponibles en la literatura. Lo hacemos utilizando la distribución local de intensidades como un descriptor adecuado para determinar el grado de destrucción de tejido en pulmones enfisematosos. Se presentan interesantes resultados derivados del análisis de varios cientos de imágenes para niveles variables de severidad de la enfermedad, sugiriendo tanto la validez de nuestras hipótesis, como la pertinencia de este tipo de análisis para la comprensión de la enfermedad pulmonar obstructiva crónica. El procesado de imágenes médicas también puede asistir en el diagnóstico y detección de enfermedades. Presentamos nuestras contribuciones a este campo, que consisten en técnicas de segmentación y cuantificación de imágenes dermatoscópicas de lesiones de la piel. La segmentación se obtiene mediante un novedoso algoritmo basado en contornos activos que explota al máximo el contenido cromático de las imágenes, gracias a la maximización de la discrepancia mediante comparaciones cross-bin. La cuantificación de texturas en lesiones melanocíticas se lleva a cabo utilizando un modelado de los patrones de pigmentación basado en campos aleatorios de Markov, en un esfuerzo por adoptar la tendencia emergente en dermatología: la detección de la malignidad mediante el análisis de la irregularidad de la textura. Los resultados para ambas técnicas son validados con un conjunto significativo de imágenes dermatológicas, sugiriendo líneas interesantes para la detección automática del melanoma maligno. Cuando la enfermedad ya está presente, el tratamiento digital de imágenes puede asistir en la planificación quirúrgica y la intervención guiada por imagen. La planificación terapeútica, ejemplicada por la planificación de cirugía plástica usando realidad virtual, se aborda en nuestro trabajo en segmentación de hueso/grasa/músculo en imágenes TAC. Usando un abordaje interactivo e incremental, nuestro sistema permite obtener segmentaciones precisas a partir de unos cuantos clics de ratón para una gran variedad de condiciones de adquisición y frente a anatomícas anormales. Presentamos nuestra metodología, y nuestra validación experimental profusa basada tanto en segmentaciones manuales como en valoraciones subjetivas de los usuarios, e indicamos referencias al lector que detallan los beneficios obtenidos con el uso de la plataforma de planifificación que utiliza nuestro algoritmo. Como conclusión presentamos una disertación final sobre la importancia de nuestros resultados y las líneas probables de trabajo futuro hacía el objetivo último de mejorar el cuidado de la salud mediante técnicas de tratamiento digital de imágenes médicas.In this Thesis we present our contributions to the state-of-the-art in medical image processing, articulating our exposition around the three main roles of medical imaging: disease prevention, diagnosis and treatment. Disease prevention can sometimes be achieved by proper characterization of disease phenotypes. Such characterization is often attained from the standpoint of imaging. We present our work in characterization of emphysema from highresolution computed-tomography images via quanti_cation of local texture. We propose to _ll the gap between current clinical practice and sophisticated texture approaches by the use of local intensity distributions as an adequate descriptor for the degree of tissue destruction in the emphysematous lung. Interesting results are presented from the analysis of several hundred datasets of lung CT for varying disease severity, suggesting both the correctness of our hypotheses and the pertinence of _ne emphysema quanti_cation for understanding of chronic obstructive pulmonary disease. Medical image processing can also assist in the diagnosis and detection of disease. We introduce our contributions to this_eld, consisting of segmentation and quanti_cation techniques in application to dermatoscopy images of skin lesions. Segmentation is achieved via a novel active contour algorithm that fully exploits the color content of the images, via cross-bin histogram dissimilarity maximization. Texture quanti_cation in the context of melanocytic lesions is performed using modelization of the pigmentation patterns via Markov random elds, in an e_ort to embrace the emerging trend in dermatology: malignancy assessment based on texture irregularity analysis. Experimental results for both, the segmentation and quanti_cation proposed techniques, will be validated on a signi_cant set of dermatoscopy images, suggesting interesting pathways towards automatic detection and diagnosis of malignant melanoma. Once disease has occurred, image processing can assist in therapeutical planning and image-guided intervention. Therapeutical planning, exempli_ed by virtual reality surgical planning, is tackled by our work in segmentation of bone/fat/muscle in CT images for plastic surgery planning. Using an interactive, incremental approach, our system is able to provide accurate segmentations based on a couple of mouse-clicks for a wide variety of imaging conditions and abnormal anatomies. We present our methodology, and provide profuse experimental validation based on manual segmentations and subjective assessment, and refer the reader to related work reporting on the clinical bene_ts obtained using the virtual reality platform hosting our algorithm. As a conclusion we present a _nal dissertation on the signi_cance of our results and the probable lines of future work towards fully bene_tting healthcare using medical image processing

Positron emision [i.e. emission] tomography (PET) in non-malignant chest diseases

Molecular imaging is a functional imaging that identify disease in its earliest stages and determine the exact location of metabolically active tissue such as tumours. Often before symptoms occur or abnormalities can be detected with other diagnostic tests. Two simultaneous studies to explore the potentials of Positron Emission tomography (PET) have been conducted. In the first study, the role of PET in pulmonary drug deposition has been evaluated whereas in the second study, it’s potential in monitoring disease progression and treatment response monitoring in IPF has been discovered.Gamma imaging such as planer and Single Photon Emission computed tomography (SPECT) have been used for decades in the imaging of pulmonary drug deposition, despite numerous advantage of PET very few studies were found in the literature. Two studies were conducted using in-house developed lung surrogate phantom and Andersen cascade impactor to demonstrate PET role in pulmonary drug deposition. The lung surrogate phantom study is a ‘’proof of concept’’ in which series of experiments was conducted leading to the development of a usable model. Each experimental procedure was conducted repeatedly over time to reduce the level of experimental errors. To my knowledge, this is the first phantom experiment quantifying the deposition pattern of aerosolized [18F]-FDG while mimicking human tidal breathing. In a separate experiment the Andersen cascade impactor (ACI) have been used to measure distribution of beclometasone dipropionate (BDP), formoterol fumarate dihydrate (FFA) as well as [18F]-2-fluorp-2-deoxy-D-glucose ([18F]-FDG) along the stages of ACI.The overall activity deposition within the phantom; cylinder and the extra-pulmonary section of the tube were 8.07 ± 3.51MBq. The deposition within the cylinder (lung surrogate) was 6.27 ± 2.55MBq. The average total internal dose (phantom cylinders and the extra-pulmonary section of the tube was calculated to be 0.2mSv/PET scan. These results are expected in human clinical trial under similar experimental conditions.The Aerodynamic particle size distribution (APSD) along the fractionating part of the AIM comprises of large particle mass (LPM) and small particle mass (SPM). The LPM is APSD >5μg deposited on stage 1 (representing to upper respiratory tract), whereas, the SPM comprised of the particle size 1-5 μg and < 1 μg deposited on stage 2 (representing the small airways and lung parenchyma) and an exhalation filter. In general, the deposition of the drugs and [18F]-FDG within the fractionating part of the impactor was predominantly within 1-5μg, which is a desirable fine particle fraction (FPF) of the active pharmaceutical ingredients (API) leading to pulmonary deposition.The potentials of PET imaging in pulmonary drug deposition has been demonstrated in these experiments using lung surrogate phantom and cascade impactor. [18F]-FDG PET imaging has the potentials in providing better understanding of regional distribution of pulmonary drug deposition. Standardization of these methods will enable PET imaging to be used in pulmonary drug development.In the second study, A retrospective studies using PET data was carried out to measure uptake of [18F]-FDG in the region of apparently normal lung in IPF. This was compared to normal control lung images to ascertain differences in their uptake value.HRCT is the current gold standard imaging the diagnosis of IPF. Recently there is growing interest in exploring the potentials of PET imaging in the disease progression and treatment response monitoring in IPF.Patients with IPF that had undergone PET-CT imaging for investigation of concomitant cancer diagnosis were identified retrospectively in a single interstitial lung disease (ILD) tertiary referral centre. Non IPF patients that had a PET-CT scan in the same centre for cancer diagnosis without non-malignant lung disease were identified to form two control groups: a lung cancer control group and a control group with no evidence of intra-thoracic disease (extra-thoracic malignancy controls). These two control groups were identified to allow assessment of whether the presence of thoracic malignancy effected [18F]-FDG uptake. In the event of no effect being identified, a pooled analysis comparing IPF patients and all controls was planned.No difference in standard uptake value (SUV) Maximum (Max) and SUV mean uptake was observed in the mean of 4 (Region of Interest) ROIs between lung cancer controls and extra-thoracic malignancy controls in all 3 normalizations (SUV Max body weight (BW), SUV body surface area (BSA) and SUV activity concentration (AC)) and therefore data from these groups were pooled for comparison with IPF patients. The SUV Max and SUV mean of radiologically normal lung in IPF patients was significantly higher than the normal lung in controls. However, the CT number/Hounsfield unit of the IPF patients and the control group are comparable. In addition, 20 textural features were identified in each ROI both in CT and PET data sets. Five out of the twenty CT textural features shows significant differences between the 2 controls as such, they were excluded. Fifteen were pooled together for comparison with IPF patients. Five out of the fifteen CT textural features shows significant differences when compared with IPF and all are consistent with five features that shows significant difference in PET dataset.Increase [18F]-FDG PET signal within areas of areas of apparently normal lung parenchyma has been demonstrated using SUV with 3 different normalization methods as well as using textural feature analysis. These findings have shown the heterogeneous nature of the disease process indicating the possibility of the disease activity within the apparently normal lung CT lung images. These finding may provide insight into the pathogenesis of the disease and may be helpful in monitoring the disease progression and treatment response