43 research outputs found
Novel Computer-Aided Diagnosis Schemes for Radiological Image Analysis
The computer-aided diagnosis (CAD) scheme is a powerful tool in assisting clinicians (e.g., radiologists) to interpret medical images more accurately and efficiently. In developing high-performing CAD schemes, classic machine learning (ML) and deep learning (DL) algorithms play an essential role because of their advantages in capturing meaningful patterns that are important for disease (e.g., cancer) diagnosis and prognosis from complex datasets. This dissertation, organized into four studies, investigates the feasibility of developing several novel ML-based and DL-based CAD schemes for different cancer research purposes. The first study aims to develop and test a unique radiomics-based CT image marker that can be used to detect lymph node (LN) metastasis for cervical cancer patients. A total of 1,763 radiomics features were first computed from the segmented primary cervical tumor depicted on one CT image with the maximal tumor region. Next, a principal component analysis algorithm was applied on the initial feature pool to determine an optimal feature cluster. Then, based on this optimal cluster, machine learning models (e.g., support vector machine (SVM)) were trained and optimized to generate an image marker to detect LN metastasis. The SVM based imaging marker achieved an AUC (area under the ROC curve) value of 0.841 ± 0.035. This study initially verifies the feasibility of combining CT images and the radiomics technology to develop a low-cost image marker for LN metastasis detection among cervical cancer patients. In the second study, the purpose is to develop and evaluate a unique global mammographic image feature analysis scheme to identify case malignancy for breast cancer. From the entire breast area depicted on the mammograms, 59 features were initially computed to characterize the breast tissue properties in both the spatial and frequency domain. Given that each case consists of two cranio-caudal and two medio-lateral oblique view images of left and right breasts, two feature pools were built, which contain the computed features from either two positive images of one breast or all the four images of two breasts. For each feature pool, a particle swarm optimization (PSO) method was applied to determine the optimal feature cluster followed by training an SVM classifier to generate a final score for predicting likelihood of the case being malignant. The classification performances measured by AUC were 0.79±0.07 and 0.75±0.08 when applying the SVM classifiers trained using image features computed from two-view and four-view images, respectively. This study demonstrates the potential of developing a global mammographic image feature analysis-based scheme to predict case malignancy without including an arduous segmentation of breast lesions. In the third study, given that the performance of DL-based models in the medical imaging field is generally bottlenecked by a lack of sufficient labeled images, we specifically investigate the effectiveness of applying the latest transferring generative adversarial networks (GAN) technology to augment limited data for performance boost in the task of breast mass classification. This transferring GAN model was first pre-trained on a dataset of 25,000 mammogram patches (without labels). Then its generator and the discriminator were fine-tuned on a much smaller dataset containing 1024 labeled breast mass images. A supervised loss was integrated with the discriminator, such that it can be used to directly classify the benign/malignant masses. Our proposed approach improved the classification accuracy by 6.002%, when compared with the classifiers trained without traditional data augmentation. This investigation may provide a new perspective for researchers to effectively train the GAN models on a medical imaging task with only limited datasets. Like the third study, our last study also aims to alleviate DL models’ reliance on large amounts of annotations but uses a totally different approach. We propose employing a semi-supervised method, i.e., virtual adversarial training (VAT), to learn and leverage useful information underlying in unlabeled data for better classification of breast masses. Accordingly, our VAT-based models have two types of losses, namely supervised and virtual adversarial losses. The former loss acts as in supervised classification, while the latter loss works towards enhancing the model’s robustness against virtual adversarial perturbation, thus improving model generalizability. A large CNN and a small CNN were used in this investigation, and both were trained with and without the adversarial loss. When the labeled ratios were 40% and 80%, VAT-based CNNs delivered the highest classification accuracy of 0.740±0.015 and 0.760±0.015, respectively. The experimental results suggest that the VAT-based CAD scheme can effectively utilize meaningful knowledge from unlabeled data to better classify mammographic breast mass images. In summary, several innovative approaches have been investigated and evaluated in this dissertation to develop ML-based and DL-based CAD schemes for the diagnosis of cervical cancer and breast cancer. The promising results demonstrate the potential of these CAD schemes in assisting radiologists to achieve a more accurate interpretation of radiological images
Digital Image Processing
This book presents several recent advances that are related or fall under the umbrella of 'digital image processing', with the purpose of providing an insight into the possibilities offered by digital image processing algorithms in various fields. The presented mathematical algorithms are accompanied by graphical representations and illustrative examples for an enhanced readability. The chapters are written in a manner that allows even a reader with basic experience and knowledge in the digital image processing field to properly understand the presented algorithms. Concurrently, the structure of the information in this book is such that fellow scientists will be able to use it to push the development of the presented subjects even further
Echocardiography
The book "Echocardiography - New Techniques" brings worldwide contributions from highly acclaimed clinical and imaging science investigators, and representatives from academic medical centers. Each chapter is designed and written to be accessible to those with a basic knowledge of echocardiography. Additionally, the chapters are meant to be stimulating and educational to the experts and investigators in the field of echocardiography. This book is aimed primarily at cardiology fellows on their basic echocardiography rotation, fellows in general internal medicine, radiology and emergency medicine, and experts in the arena of echocardiography. Over the last few decades, the rate of technological advancements has developed dramatically, resulting in new techniques and improved echocardiographic imaging. The authors of this book focused on presenting the most advanced techniques useful in today's research and in daily clinical practice. These advanced techniques are utilized in the detection of different cardiac pathologies in patients, in contributing to their clinical decision, as well as follow-up and outcome predictions. In addition to the advanced techniques covered, this book expounds upon several special pathologies with respect to the functions of echocardiography
Multi-fractal dimension features by enhancing and segmenting mammogram images of breast cancer
The common malignancy which causes deaths in women is breast cancer. Early detection of breast cancer using mammographic image can help in reducing the mortality rate and the probability of recurrence. Through mammographic examination, breast lesions can be detected and classified. Breast lesions can be detected using many popular tools such as Magnetic Resonance Imaging (MRI), ultrasonography, and mammography. Although mammography is very useful in the diagnosis of breast cancer, the pattern similarities between normal and pathologic cases makes the process of diagnosis difficult. Therefore, in this thesis Computer Aided Diagnosing (CAD) systems have been developed to help doctors and technicians in detecting lesions. The thesis aims to increase the accuracy of diagnosing breast cancer for optimal classification of cancer. It is achieved using Machine Learning (ML) and image processing techniques on mammogram images. This thesis also proposes an improvement of an automated extraction of powerful texture sign for classification by enhancing and segmenting the breast cancer mammogram images. The proposed CAD system consists of five stages namely pre-processing, segmentation, feature extraction, feature selection, and classification. First stage is pre-processing that is used for noise reduction due to noises in mammogram image. Therefore, based on the frequency domain this thesis employed wavelet transform to enhance mammogram images in pre-processing stage for two purposes which is to highlight the border of mammogram images for segmentation stage, and to enhance the region of interest (ROI) using adaptive threshold in the mammogram images for feature extraction purpose. Second stage is segmentation process to identify ROI in mammogram images. It is a difficult task because of several landmarks such as breast boundary and artifacts as well as pectoral muscle in Medio-Lateral Oblique (MLO). Thus, this thesis presents an automatic segmentation algorithm based on new thresholding combined with image processing techniques. Experimental results demonstrate that the proposed model increases segmentation accuracy of the ROI from breast background, landmarks, and pectoral muscle. Third stage is feature extraction where enhancement model based on fractal dimension is proposed to derive significant mammogram image texture features. Based on the proposed, model a powerful texture sign for classification are extracted. Fourth stage is feature selection where Genetic Algorithm (GA) technique has been used as a feature selection technique to select the important features. In last classification stage, Artificial Neural Network (ANN) technique has been used to differentiate between Benign and Malignant classes of cancer using the most relevant texture feature. As a conclusion, classification accuracy, sensitivity, and specificity obtained by the proposed CAD system are improved in comparison to previous studies. This thesis has practical contribution in identification of breast cancer using mammogram images and better classification accuracy of benign and malign lesions using ML and image processing techniques
Methods and instrumentation for raman characterization of bladder cancer tumor
High incidence and recurrence rates make bladder cancer the most common malignant tumor in the urinary system. Cystoscopy is the gold standard test used for diagnosis, nevertheless small flat tumors might be missed, and the procedure still represents discomfort to patients and high recurrence can result from of urethral injuries. During cystoscopy, suspicious tumors are detected through white light endoscopy and resected tissue is further examined by histopathology. after resection, the pathologist provides information on the differentiation of the cells and the penetration depth of the tumor in the tissue, known as grading and staging of tumor, respectively. During cystoscopy, information on tumor grading and morphological depth characterization can assist onsite diagnosis and significantly reduce the amount of unnecessarily resected tissue. Recently, new developments in optical imaging and spectroscopic approaches have been demonstrated to improve the results of standard techniques by providing real-time detection of macroscopic and microscopic biomedical information. Different applications to detect anomalies in tissues and cells based on the chemical composition and structure at the microscopic level have been successfully tested. There is, nevertheless, the need to cope with the demands for clinical translation. This doctoral thesis presents the investigations, clinical studies and approaches applied to filling the main open research questions when applying Raman spectroscopy as a diagnostic tool for bladder cancer tumor grading and general Raman spectroscopy-based oncological clinical studies
Exploration of the Relationship Between the Fractal Dimension of Microcalcification Clusters and the Hurst Exponent of Background Tissue Disruption in Mammograms
Breast cancer is one of the most frequent cancers among women worldwide and holds the second place in cancer-related death. Mammography is the most commonly used screening technique, however, the dense nature of some breasts makes the analysis of mammograms challenging for radiologists. The 2D Wavelet Transform Modulus Maxima (WTMM) is one mathematical approach that is used to for the analysis of mammograms. In 2014, a team from the CompuMAINE Lab characterized differences between benign microcalcification clusters (MC) from malignant MC by calculating their fractal dimension, D, with the aid of the 2D WTMM method. In a different implementation of the 2D WTMM method, this same team did research in 2017 where they quantified tissue disruption in breast tissue microenvironment using the Hurst exponent, H. The goal of this study was to further explore the potential relationship between the fractality of MC clusters and tissue disruption in the microenvironment surrounding these clusters. Statistical relationships are explored between the fractal dimension, D, of MC clusters and the Hurst exponent, H measuring tissue disruption. A “2D fractal dimension vs. Hurst exponent plot” was graphed to show this relationship used to distinguish between benign and malignant cases. In the graph, a quadrilateral region extending horizontally from Hurst value of (0.2,0.8) centered at 0.5 and stretching vertically from fractal dimension value of (1.2,1.8) centered 1.5 was identified. Analysis of this region has showed that the 60% of the malignant cases and 21% benign cases are found inside the quadrilateral for CC view and 68% of the malignant cases and 12% of benign cases are found inside the region for MLO view. As a conclusion, based on the outcomes of this study one can hypothesize that with further analyses, loss of tissue homeostasis describing the state of the microenvironment of a breast tissue and the fractal nature of MC clusters have a quantifiable relationship to distinguish benign cases from malignant cases in mammogram analysis
Actas de SABI2020
Los temas salientes incluyen un marcapasos pulmonar que promete complementar y eventualmente sustituir la conocida ventilación mecánica por presión positiva (intubación), el análisis de la marchaespontánea sin costosos equipamientos, las imágenes infrarrojas y la predicción de la salud cardiovascular en temprana edad por medio de la biomecánica arterial
An Hybrid Approach for Identification of Breast Cancer using Mammogram Images
Breast Cancer (BC) is the first among the cancer deaths in women all over the world. Mammography is broadly perceived as the best imaging methodology for the early location of BC. Mammography examination reduced the BC death in spite of increasing number of noticed malignancies during the last decade. Although it is the best reliable method for early location, it has several limitations. One essential viewpoint is that the exactness rate tends to diminishing when doctors' examined high volume of mammograms. This work mainly concentrates on identifying regions containing small clusters of micro calcifications to categorize the tissue as being regular or irregular. Potentially cancerous tissue is distinguished from normal tissue by analyzing features of a given region within a mammogram. Therefore, feature extraction and saliency play an important role in cancer detection
Analyse structurelle de l'hydrogène neutre dans la voie lactée
Les étoiles vivent et meurent en rejetant de la matière dans le milieu interstellaire (MIS) et elles naissent à l’intérieur de celui-ci. Nous avons analysé la composante d’hydrogène neutre du MIS. Nos données proviennent de la partie canadienne de l’International Galactic Plane Survey qui vise l’imagerie spectroscopique de l’hydrogène neutre du plan de notre galaxie. Nous avons utilisé deux outils mathématiques d’analyse d’images: la technique d’Espaces Métriques (TEM) et la méthode des Maxima du Module de la Transformée en Ondelettes (MMTO). La TEM est un formalisme mathématique d’analyse d’images qui permet de comparer quantitativement la complexité des objets étudiés. Nous avons amélioreré l’outil aux niveaux mathématique et technique avant de l’utiliser pour caractériser la complexité de 28 régions d’hydrogéne neutre. Aprés avoir classé les 28 objets, nous avons trouvé des corrélations entre ce classement et les propriétés physiques des objets sous-jacents, dont: (1) Plus le flux des photons UV est élevé, plus la région de H i photodissociée est complexe; et (2) la complexité des régions H i augmente avec l’ˆage des restes de supernovae auxquels elles sont associées. La méthode MMTO est un formalisme multifractal basé sur la transformée en ondelettes. Nos résultats obtenus à partir de cette méthode concernent les propriétés multifractales et anisotropes de l’hydrogène neutre dans notre galaxie. Les nuages terrestres exhibent des propriétés multifractales. Nous avons démontré que l’hydrogène neutre du disque de notre galaxie est monofractal. En analysant séparément les bras spiraux et les milieux inter-bras, nous avons découvert une signature anisotrope et que les structures horizontales sont plus complexes que les structures verticales. Cette anisotropie est indépendante de l’échelle pour les inter-bras tandis qu’elle est dépendante de l’échelle pour les bras spiraux. Les hypothèses investiguées pour obtenir une explication physique sont: le gradient de distribution en z (“scale-height gradient”), l’onde de densité, l’activité de formation d’étoiles, la photo-lévitation de nuages poussiéreux, les mouvements aléatoires de nuages H i, la corrugation et la turbulence.Stars live and die by rejecting matter in the interstellar medium (ISM), where they were born. We have analyzed the neutral hydrogen component of the ISM. The data come from the Canadian portion of the International Galactic Plane Survey which aims the spectroscopic imaging of the neutral hydrogen from our Galaxy. We have used two mathematical image analysis tools: Metric Space Technique (MST) and the Wavelet Transform Modulus Maxima (WTMM) method. The MST is an image analysis mathematical formalism that allows one to quantitatively compare the complexity of the studied objects. We have improved the tool mathematically and technically before using it to characterize the complexity of 28 neutral hydrogen regions. After classifying the 28 objects, we have found some correlations between this ranking and the physical properties of the underlying objects, for example: (1) The complexity of the photodissociated neutral hydrogen regions increases with the flux of UV photons; and (2) the complexity of neutral hydrogen regions increases with the age of the supernovae remnants to which they are associated. The WTMM method is a multifractal formalism based on the wavelet transform. The results we obtain from this method concern the multifractal and anisotropic properties of neutral hydrogen in our Galaxy. Earth clouds exhibit multifractal properties. We have shown that the neutral hydrogen from our galactic disk is monofractal. By analyzing separately spiral arms and the inter-arm regions, we have discovered an anisotropic signature and that the horizontal structures and more complex than the vertical structures. This anisotropy is independent of scale for the inter-arms while it is depedent of scale for the spiral arms. The investigated hypotheses to obtain some physical explanations are: the scale-height gradient, the density wave, star formation activity, photo-levitation of dusty clouds, random motions of neutral hydrogen clouds, corrugation and turbulence
Proceedings of the International Workshop on Medical Ultrasound Tomography: 1.- 3. Nov. 2017, Speyer, Germany
Ultrasound Tomography is an emerging technology for medical imaging that is quickly approaching its clinical utility. Research groups around the globe are engaged in research spanning from theory to practical applications. The International Workshop on Medical Ultrasound Tomography (1.-3. November 2017, Speyer, Germany) brought together scientists to exchange their knowledge and discuss new ideas and results in order to boost the research in Ultrasound Tomography