A novel shape feature to classify microcalcifications

Clinical evident shows that the shape of mammographic calcification is an indicator of the pathology. Microcalcifications (MC) with rough shape are early signs of malignant breast cancer. This thesis proposed a shape metric to help radiologist in classifying regions of interest. Region growing and gradient vector flow algorithm are used to obtain the contour of MC to calculate the normalized distance signature. A three level wavelet decomposition with a Daubechies eight tap wavelet is used to provide a bandpass function and extract the desired shape feature of the MC. A comparison with previously used shape features such as compactness, moment, Fourier descriptors is provided. 58 malignant and 125 benign cases, totaling 368 individual MC, are tested by the proposed method and previously used shape features

Performance of a CAD scheme applied to images obtained from mammographic film digitization and full-field digital mammography (FFDM).

This work has as purpose to compare the effects of a CAD scheme applied to digitized and \ud direct digital mamograms sets. A routine designed to be applied to mammogram in \ud DICOM standard was developed and a schema based on the Watershed Transform to \ud masses detection was applied to 252 ROIs from 130 digitized mammograms, resulting in \ud 92% of true positive and 10% of false positives. For clustered microcalcifications \ud detection, another procedure was applied to 165 ROIs from 120 mammograms, resulting in \ud 93% of true positive and 16% of false positive. By using the same procedures to 154 \ud digital mammograms obtained from FFDM, the rates have shown a little decrease in the \ud scheme performance: 89% of true positive and 16% of false positive for masses detection; \ud 90% of true positive and 27% of false positive for clusters detection. Although the tests \ud with digital mammograms have been carried with a smaller number of images and \ud different cases compared to the digitized ones, including several dense breasts images, the \ud results can be considered comparable, mainly forclustered microcalcifications detection \ud with a difference of only 3% between the sensibility rates for the both images sets. Another \ud important feature affecting these results is the contrast difference between the two images \ud set. This implies the need of extensive investigations not only with a larger number of \ud cases from FFDM but also on the parameters related to its image acquisition as well as to \ud its corresponding processing.Este trabalho tem como objetivo comparar os resultados de um esquema CAD aplicado em \ud conjunto de mamografias digitalizadas e em um conjunto de mamografias obtidas de um \ud mamógrafo digital. Para extrair as imagens do padrão DICOM, padrão utilizado pelos \ud mamógrafos digitais, uma rotina computacional foi desenvolvida. Para a detecção de \ud nódulos, um esquema baseado em Transforma Watershed foi aplicado a 252 regiões de \ud interesse (ROIs) de 130 mamografias digitalizadas, resultando em 92% de verdadeiro \ud positivo e 10%de falsos positivos. Para a detecção de microcalcificações agrupadas, outro \ud procedimento foi aplicado a165 ROIs extraídas de 120 mamografias digitalizadas, \ud resultando em 93% de verdadeiro positivo e 16% de falso positivo. Ao utilizar os mesmos \ud procedimentos para154 mamografias digitais obtidas a partir de um FFDM, as taxas \ud mostraram uma diminuição pequena no desempenho: 89% do verdadeiro positivo e 16% \ud de falso positivo para a detecção de nódulos, e 90% de verdadeiro positivo e 27% de falsos \ud positivo para a detecção de clusters de microcalcificações. Embora os testes com \ud mamografias digitais tenham sido realizados com um menor número de imagens e casos \ud diferentes em comparação com os digitalizados, incluindo várias imagens de mamas \ud densas, os resultados podem ser considerados comparáveis, principalmente para a detecção \ud de clusters de microcalcificações com uma diferença de apenas 3% entre as taxas de \ud sensibilidade para as imagens dos dois conjuntos. Outra característica importante que afeta \ud esses resultados é a diferença de contraste dos dois grupos de imagens analisados. Isto \ud implica na necessidade de extensas investigações não só com um maior número de casos \ud de mamografias digitais, mas também um estudo sobre os parâmetros relacionados a \ud aquisição da imagem, bem como para o seu processamentoCNPqFAPESPHospital of Clinics in Botucatu/S

Advancing the Clinical Potential of Carbon Nanotube-enabled stationary 3D Mammography

Scope and purpose. 3D imaging has revolutionized medicine. Digital breast tomosynthesis (DBT), also recognized as 3D mammography, is a relatively recent example. stationary DBT (sDBT) is an experimental technology in which the single moving x-ray source of conventional DBT has been replaced by a fixed array of carbon nanotube (CNT)-enabled sources. Given the potential for a higher spatial and temporal resolution compared to commercially-available, moving-source DBT devices, it was hypothesized that sDBT would provide a valuable tool for breast imaging. As such, the purpose of this work was to explore the clinical potential of sDBT. To accomplish this purpose, three broad Aims were set forth: (1) study the challenges of scatter and artifact with sDBT, (2) assess the performance of sDBT relative to standard mammographic screening approaches, and (3) develop a synthetic mammography capability for sDBT. Throughout the work, developing image processing approaches to maximize the diagnostic value of the information presented to readers remained a specific goal. Data sources and methodology. Sitting at the intersection of development and clinical application, this work involved both basic experimentation and human study. Quantitative measures of image quality as well as reader preference and accuracy were used to assess the performance of sDBT. These studies imaged breast-mimicking phantoms, lumpectomy specimens, and human subjects on IRB-approved study protocols, often using standard 2D and conventional 3D mammography for reference. Key findings. Characterizing scatter and artifact allowed the development of new processing approaches to improve image quality. Additionally, comparing the performance of sDBT to standard breast imaging technologies helped identify opportunities for improvement through processing. This line of research culminated in the incorporation of a synthetic mammography capability into sDBT, yielding images that have the potential to improve the diagnostic value of sDBT. Implications. This work advanced the evolution of CNT-enabled sDBT toward a viable clinical tool by incorporating key image processing functionality and characterizing the performance of sDBT relative to standard breast imaging techniques. The findings confirmed the clinical utility of sDBT while also suggesting promising paths for future research and development with this unique approach to breast imaging.Doctor of Philosoph

Mammography

In this volume, the topics are constructed from a variety of contents: the bases of mammography systems, optimization of screening mammography with reference to evidence-based research, new technologies of image acquisition and its surrounding systems, and case reports with reference to up-to-date multimodality images of breast cancer. Mammography has been lagged in the transition to digital imaging systems because of the necessity of high resolution for diagnosis. However, in the past ten years, technical improvement has resolved the difficulties and boosted new diagnostic systems. We hope that the reader will learn the essentials of mammography and will be forward-looking for the new technologies. We want to express our sincere gratitude and appreciation?to all the co-authors who have contributed their work to this volume

Segmentation, Super-resolution and Fusion for Digital Mammogram Classification

Mammography is one of the most common and effective techniques used by radiologists for the early detection of breast cancer. Recently, computer-aided detection/diagnosis (CAD) has become a major research topic in medical imaging and has been widely applied in clinical situations. According to statics, early detection of cancer can reduce the mortality rates by 30% to 70%, therefore detection and diagnosis in the early stage are very important. CAD systems are designed primarily to assist radiologists in detecting and classifying abnormalities in medical scan images, but the main challenges hindering their wider deployment is the difficulty in achieving accuracy rates that help improve radiologists’ performance. The detection and diagnosis of breast cancer face two main issues: the accuracy of the CAD system, and the radiologists’ performance in reading and diagnosing mammograms. This thesis focused on the accuracy of CAD systems. In particular, we investigated two main steps of CAD systems; pre-processing (enhancement and segmentation), feature extraction and classification. Through this investigation, we make five main contributions to the field of automatic mammogram analysis. In automated mammogram analysis, image segmentation techniques are employed in breast boundary or region-of-interest (ROI) extraction. In most Medio-Lateral Oblique (MLO) views of mammograms, the pectoral muscle represents a predominant density region and it is important to detect and segment out this muscle region during pre-processing because it could be bias to the detection of breast cancer. An important reason for the breast border extraction is that it will limit the search-zone for abnormalities in the region of the breast without undue influence from the background of the mammogram. Therefore, we propose a new scheme for breast border extraction, artifact removal and removal of annotations, which are found in the background of mammograms. This was achieved using an local adaptive threshold that creates a binary mask for the images, followed by the use of morphological operations. Furthermore, an adaptive algorithm is proposed to detect and remove the pectoral muscle automatically. Feature extraction is another important step of any image-based pattern classification system. The performance of the corresponding classification depends very much on how well the extracted features represent the object of interest. We investigated a range of different texture feature sets such as Local Binary Pattern Histogram (LBPH), Histogram of Oriented Gradients (HOG) descriptor, and Gray Level Co-occurrence Matrix (GLCM). We propose the use of multi-scale features based on wavelet and local binary patterns for mammogram classification. We extract histograms of LBP codes from the original image as well as the wavelet sub-bands. Extracted features are combined into a single feature set. Experimental results show that our proposed method of combining LBPH features obtained from the original image and with LBPH features obtained from the wavelet domain increase the classification accuracy (sensitivity and specificity) when compared with LBPH extracted from the original image. The feature vector size could be large for some types of feature extraction schemes and they may contain redundant features that could have a negative effect on the performance of classification accuracy. Therefore, feature vector size reduction is needed to achieve higher accuracy as well as efficiency (processing and storage). We reduced the size of the features by applying principle component analysis (PCA) on the feature set and only chose a small number of eigen components to represent the features. Experimental results showed enhancement in the mammogram classification accuracy with a small set of features when compared with using original feature vector. Then we investigated and propose the use of the feature and decision fusion in mammogram classification. In feature-level fusion, two or more extracted feature sets of the same mammogram are concatenated into a single larger fused feature vector to represent the mammogram. Whereas in decision-level fusion, the results of individual classifiers based on distinct features extracted from the same mammogram are combined into a single decision. In this case the final decision is made by majority voting among the results of individual classifiers. Finally, we investigated the use of super resolution as a pre-processing step to enhance the mammograms prior to extracting features. From the preliminary experimental results we conclude that using enhanced mammograms have a positive effect on the performance of the system. Overall, our combination of proposals outperforms several existing schemes published in the literature

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

Implementing decision tree-based algorithms in medical diagnostic decision support systems

As a branch of healthcare, medical diagnosis can be defined as finding the disease based on the signs and symptoms of the patient. To this end, the required information is gathered from different sources like physical examination, medical history and general information of the patient. Development of smart classification models for medical diagnosis is of great interest amongst the researchers. This is mainly owing to the fact that the machine learning and data mining algorithms are capable of detecting the hidden trends between features of a database. Hence, classifying the medical datasets using smart techniques paves the way to design more efficient medical diagnostic decision support systems. Several databases have been provided in the literature to investigate different aspects of diseases. As an alternative to the available diagnosis tools/methods, this research involves machine learning algorithms called Classification and Regression Tree (CART), Random Forest (RF) and Extremely Randomized Trees or Extra Trees (ET) for the development of classification models that can be implemented in computer-aided diagnosis systems. As a decision tree (DT), CART is fast to create, and it applies to both the quantitative and qualitative data. For classification problems, RF and ET employ a number of weak learners like CART to develop models for classification tasks. We employed Wisconsin Breast Cancer Database (WBCD), Z-Alizadeh Sani dataset for coronary artery disease (CAD) and the databanks gathered in Ghaem Hospital’s dermatology clinic for the response of patients having common and/or plantar warts to the cryotherapy and/or immunotherapy methods. To classify the breast cancer type based on the WBCD, the RF and ET methods were employed. It was found that the developed RF and ET models forecast the WBCD type with 100% accuracy in all cases. To choose the proper treatment approach for warts as well as the CAD diagnosis, the CART methodology was employed. The findings of the error analysis revealed that the proposed CART models for the applications of interest attain the highest precision and no literature model can rival it. The outcome of this study supports the idea that methods like CART, RF and ET not only improve the diagnosis precision, but also reduce the time and expense needed to reach a diagnosis. However, since these strategies are highly sensitive to the quality and quantity of the introduced data, more extensive databases with a greater number of independent parameters might be required for further practical implications of the developed models