Prediction of near-term risk of developing breast cancer using computerized features from bilateral mammograms

abstract: Asymmetry of bilateral mammographic tissue density and patterns is a potentially strong indicator of having or developing breast abnormalities or early cancers. The purpose of this study is to design and test the global asymmetry features from bilateral mammograms to predict the near-term risk of women developing detectable high risk breast lesions or cancer in the next sequential screening mammography examination. The image dataset includes mammograms acquired from 90 women who underwent routine screening examinations, all interpreted as negative and not recalled by the radiologists during the original screening procedures. A computerized breast cancer risk analysis scheme using four image processing modules, including image preprocessing, suspicious region segmentation, image feature extraction, and classification was designed to detect and compute image feature asymmetry between the left and right breasts imaged on the mammograms. The highest computed area under curve (AUC) is 0.754 ± 0.024 when applying the new computerized aided diagnosis (CAD) scheme to our testing dataset. The positive predictive value and the negative predictive value were 0.58 and 0.80, respectively.NOTICE: this is the author's version of a work that was accepted for publication in . Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in , 38, 348-357. DOI: 10.1016/j.compmedimag.2014.03.00

Image analysis for classification of dysplasia in Barrett’s esophagus using endoscopic optical coherence tomography

Barrett’s esophagus (BE) and associated adenocarcinoma have emerged as a major health care problem. Endoscopic optical coherence tomography is a microscopic sub-surface imaging technology that has been shown to differentiate tissue layers of the gastrointestinal wall and identify dysplasia in the mucosa, and is proposed as a surveillance tool to aid in management of BE. In this work a computer-aided diagnosis (CAD) system has been demonstrated for classification of dysplasia in Barrett’s esophagus using EOCT. The system is composed of four modules: region of interest segmentation, dysplasia-related image feature extraction, feature selection, and site classification and validation. Multiple feature extraction and classification methods were evaluated and the process of developing the CAD system is described in detail. Use of multiple EOCT images to classify a single site was also investigated. A total of 96 EOCT image-biopsy pairs (63 non-dysplastic, 26 low-grade and 7 high-grade dysplastic biopsy sites) from a previously described clinical study were analyzed using the CAD system, yielding an accuracy of 84% for classification of non-dysplastic vs. dysplastic BE tissue. The results motivate continued development of CAD to potentially enable EOCT surveillance of large surface areas of Barrett’s mucosa to identify dysplasia

Computer aided diagnosis system for breast cancer using deep learning.

The recent rise of big data technology surrounding the electronic systems and developed toolkits gave birth to new promises for Artificial Intelligence (AI). With the continuous use of data-centric systems and machines in our lives, such as social media, surveys, emails, reports, etc., there is no doubt that data has gained the center of attention by scientists and motivated them to provide more decision-making and operational support systems across multiple domains. With the recent breakthroughs in artificial intelligence, the use of machine learning and deep learning models have achieved remarkable advances in computer vision, ecommerce, cybersecurity, and healthcare. Particularly, numerous applications provided efficient solutions to assist radiologists and doctors for medical imaging analysis, which has remained the essence of the visual representation that is used to construct the final observation and diagnosis. Medical research in cancerology and oncology has been recently blended with the knowledge gained from computer engineering and data science experts. In this context, an automatic assistance or commonly known as Computer-aided Diagnosis (CAD) system has become a popular area of research and development in the last decades. As a result, the CAD systems have been developed using multidisciplinary knowledge and expertise and they have been used to analyze the patient information to assist clinicians and practitioners in their decision-making process. Treating and preventing cancer remains a crucial task that radiologists and oncologists face every day to detect and investigate abnormal tumors. Therefore, a CAD system could be developed to provide decision support for many applications in the cancer patient care processes, such as lesion detection, characterization, cancer staging, tumors assessment, recurrence, and prognosis prediction. Breast cancer has been considered one of the common types of cancers in females across the world. It was also considered the leading cause of mortality among women, and it has been increased drastically every year. Early detection and diagnosis of abnormalities in screened breasts has been acknowledged as the optimal solution to examine the risk of developing breast cancer and thus reduce the increasing mortality rate. Accordingly, this dissertation proposes a new state-of-the-art CAD system for breast cancer diagnosis that is based on deep learning technology and cutting-edge computer vision techniques. Mammography screening has been recognized as the most effective tool to early detect breast lesions for reducing the mortality rate. It helps reveal abnormalities in the breast such as Mass lesion, Architectural Distortion, Microcalcification. With the number of daily patients that were screened is continuously increasing, having a second reading tool or assistance system could leverage the process of breast cancer diagnosis. Mammograms could be obtained using different modalities such as X-ray scanner and Full-Field Digital mammography (FFDM) system. The quality of the mammograms, the characteristics of the breast (i.e., density, size) or/and the tumors (i.e., location, size, shape) could affect the final diagnosis. Therefore, radiologists could miss the lesions and consequently they could generate false detection and diagnosis. Therefore, this work was motivated to improve the reading of mammograms in order to increase the accuracy of the challenging tasks. The efforts presented in this work consists of new design and implementation of neural network models for a fully integrated CAD system dedicated to breast cancer diagnosis. The approach is designed to automatically detect and identify breast lesions from the entire mammograms at a first step using fusion models’ methodology. Then, the second step only focuses on the Mass lesions and thus the proposed system should segment the detected bounding boxes of the Mass lesions to mask their background. A new neural network architecture for mass segmentation was suggested that was integrated with a new data enhancement and augmentation technique. Finally, a third stage was conducted using a stacked ensemble of neural networks for classifying and diagnosing the pathology (i.e., malignant, or benign), the Breast Imaging Reporting and Data System (BI-RADS) assessment score (i.e., from 2 to 6), or/and the shape (i.e., round, oval, lobulated, irregular) of the segmented breast lesions. Another contribution was achieved by applying the first stage of the CAD system for a retrospective analysis and comparison of the model on Prior mammograms of a private dataset. The work was conducted by joining the learning of the detection and classification model with the image-to-image mapping between Prior and Current screening views. Each step presented in the CAD system was evaluated and tested on public and private datasets and consequently the results have been fairly compared with benchmark mammography datasets. The integrated framework for the CAD system was also tested for deployment and showcase. The performance of the CAD system for the detection and identification of breast masses reached an overall accuracy of 97%. The segmentation of breast masses was evaluated together with the previous stage and the approach achieved an overall performance of 92%. Finally, the classification and diagnosis step that defines the outcome of the CAD system reached an overall pathology classification accuracy of 96%, a BIRADS categorization accuracy of 93%, and a shape classification accuracy of 90%. Results given in this dissertation indicate that our suggested integrated framework might surpass the current deep learning approaches by using all the proposed automated steps. Limitations of the proposed work could occur on the long training time of the different methods which is due to the high computation of the developed neural networks that have a huge number of the trainable parameters. Future works can include new orientations of the methodologies by combining different mammography datasets and improving the long training of deep learning models. Moreover, motivations could upgrade the CAD system by using annotated datasets to integrate more breast cancer lesions such as Calcification and Architectural distortion. The proposed framework was first developed to help detect and identify suspicious breast lesions in X-ray mammograms. Next, the work focused only on Mass lesions and segment the detected ROIs to remove the tumor’s background and highlight the contours, the texture, and the shape of the lesions. Finally, the diagnostic decision was predicted to classify the pathology of the lesions and investigate other characteristics such as the tumors’ grading assessment and type of the shape. The dissertation presented a CAD system to assist doctors and experts to identify the risk of breast cancer presence. Overall, the proposed CAD method incorporates the advances of image processing, deep learning, and image-to-image translation for a biomedical application

COMPUTER AIDED SYSTEM FOR BREAST CANCER DIAGNOSIS USING CURVELET TRANSFORM

Breast cancer is a leading cause of death among women worldwide. Early detection is the key for improving breast cancer prognosis. Digital mammography remains one of the most suitable tools for early detection of breast cancer. Hence, there are strong needs for the development of computer aided diagnosis (CAD) systems which have the capability to help radiologists in decision making. The main goal is to increase the diagnostic accuracy rate. In this thesis we developed a computer aided system for the diagnosis and detection of breast cancer using curvelet transform. Curvelet is a multiscale transform which possess directionality and anisotropy, and it breaks some inherent limitations of wavelet in representing edges in images. We started this study by developing a diagnosis system. Five feature extraction methods were developed with curvelet and wavelet coefficients to differentiate between different breast cancer classes. The results with curvelet and wavelet were compared. The experimental results show a high performance of the proposed methods and classification accuracy rate achieved 97.30%. The thesis then provides an automatic system for breast cancer detection. An automatic thresholding algorithm was used to separate the area composed of the breast and the pectoral muscle from the background of the image. Subsequently, a region growing algorithm was used to locate the pectoral muscle and suppress it from the breast. Then, the work concentrates on the segmentation of region of interest (ROI). Two methods are suggested to accomplish the segmentation stage: an adaptive thresholding method and a pattern matching method. Once the ROI has been identified, an automatic cropping is performed to extract it from the original mammogram. Subsequently, the suggested feature extraction methods were applied to the segmented ROIs. Finally, the K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) classifiers were used to determine whether the region is abnormal or normal. At this level, the study focuses on two abnormality types (mammographic masses and architectural distortion). Experimental results show that the introduced methods have very high detection accuracies. The effectiveness of the proposed methods has been tested with Mammographic Image Analysis Society (MIAS) dataset. Throughout the thesis all proposed methods and algorithms have been applied with both curvelet and wavelet for comparison and statistical tests were also performed. The overall results show that curvelet transform performs better than wavelet and the difference is statistically significant

Mamogram görüntülerinden makine öğrenmesi yöntemleri ile meme kanseri teşhisi

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Meme kanseri son yıllarda kanser türleri arasında en çok yaygınlık gösteren kanser türüdür. Meme kanserinin teşhisi ve tedavisinde mamografi olarak bilinen X-Ray görüntüleme yöntemi yaygın bir şekilde kullanılmaktadır. Mamografi cihazları ile elde edilen mamogram görüntüleri radyoloji uzmanları tarafından incelenir, yorumlanır ve hasta ile ilgili rapor yazılır. Mamogram görüntülerinde uzmanlar öncelikle kitle tespit etmeye ve mikrokireçlenme(MC, Microcalcification) tespit etmeye çalışırlar. MC tespiti kitle tespitine göre gözden kaçırılma riski daha fazla olan bir durumdur. Yapılan araştırmalarda radyologların MC vakalarını tespit etmekte zorlandıklarını ve yüzde yetmişlik bir doğrulukla çalıştıkları ortaya koyulmuştur. Son yıllarda meme kanseri teşhisi alanında bilgisayar destekli tespit sistemleri geliştirilmeye başlanmıştır. Araştırmacılar mamogram görüntüleri üzerinde kitle tespiti yapan veya MC tespiti yapan yöntemler yaklaşımlar ve algoritmalar geliştirmektedir. Bu çalışmada MC bölgelerinin tespitini yapmak için makine öğrenmesi yöntemi kullanılarak bir çalışma yapılmıştır. Yapılan çalışmada gri seviye eş oluşum matrisi temelli doku analizi (GLCM, Gray Level Cooccurrance Matrix), dalgacık dönüşümü temelli ayrıştırma, iki boyutlu eşit genişlikli ayrıştırma (EWD2) ve çoklu pencere temelli istatistiki analiz (MWBSA) kullanılarak farklı özellik çıkartım yöntemleri ile MC desenlerinin karakteristik özellikleri sayısal yöntemlerle analiz edilmiş olup çok katmanlı ileri beslemeli yapay sinir ağı (MLPNN, Multiple Layer Percepteron Neural Network) olarak bilinen sınıflandırıcı ve destek vektör makinesi (SVM, Support Vector Machine) kullanılarak bir makine öğrenmesi yaklaşımı geliştirilmiştir. Çalışma sonuçlarının geçerliliği, tıbbi karar verme sürecinde bir testin ayırt ediciliğini belirlemek amacıyla kullanılan yöntemlerden biri olan Alıcı İşlem Karakteristikleri Eğrisi (ROC, Receiver Operating Characteristic) yöntemi kullanılarak yapılmıştır. Duyarlılık ve özgüllük testi olarak da bilinen bu test neticesinde aday mikrokireçlenme tespit aşamasında MLPNN sınıflandırıcı kullanılarak en iyi sonuç MWBSA yöntemi ile elde edilmiştir. SVM sınıflandırıcı kullanılarak en iyi sonuç ise EWD2 ve GLCM yöntemleri kullanılarak elde edilmiştir. Aday mikrokireçlenme bölgelerinin sınıflandırılması olan ikinci aşamada ise MLPNN sınıflandırıcı kullanılarak en iyi sonuç EWD2 yöntemi ve GLCM yöntemi kullanılarak elde edilirken SVM sınıflandırıcı kullanılarak yapılan deneylerde en iyi sonuç dalgacık dönüşümü yöntemi kullanılarak elde ediliştir. Çalışmanın sonunda MATLAB yazılım geliştirme ortamı kullanılarak grafik arayüze sahip BCDS ismi verilen MC temelli meme kanseri teşhis yazılımı geliştirilmiştir. Geliştirilen bu yazılım gelecekte üzerine yeni özellik çıkartım yöntemleri ve yeni sınıflandırıcı modelleri eklenebilecek şekilde dinamik bir yapıya sahiptir.Breast cancer is the most common cancer type among other cancer types in recent years. X-ray imaging method, known as mammography for diagnosis and treatment of breast cancer, is widely used. The mammogram images, produced by mammography devices, are examined, interpreted, and a report about the patient is written by radiologists. Radiologists first try to catch masses and microcalcifications in mammogram images. Detection of microcalcification (MC) is a more difficult process than mass detection. Research has shown that radiologists have difficulty detecting microcalcification and they work with seventy percent accuracy. In recent years several computer aided detection systems have been developed on breast cancer diagnosis. Researchers have been developing methods, approaches and algorithms catching masses and MC in mammogram images. In this study machine learning method was used for detection of microcalcification problem. In the current study, the characteristic features of MC patterns were analyzed by using quantitative methods such as gray level co-occurrence matrix based texture analysis (GLCM), wavelet-based parsing, two-dimensional equal width separation (EWD2), and multi-window based statistical analysis (MWBSA), and a machine learning approach was developed by employing a classifier and support vector machine (CSM) known as multi-layer percepteron neural network (MLPNN). The validity of the study findings was performed using the Receiver Operating Characteristic (ROC) method, which is used for determining the distinctiveness of a test during a medical decision making process. As a result of this test, also known as sensitivity and specificity test, the best result was obtained with MWBSA method using MLFFNN classifier during microcalcification diagnosis process. The best result for CSM classifier was obtained using EWD2 and GLCM methods. At the second stage, which is the classification of candidate microcalcifications, the best values for MLFFNN classifier were obtained using EWD2 and GLCM methods, whereas the best result in experiments employing CSM classifier was obtained using wavelet method. At the end of the study, MC based breast cancer detection system called BCDS with a GUI was developed using MATLAB. The developed software is a dynamic and well suited structure into which new classifier models and extraction methods can be integrated in the future

Computer-aided detection and diagnosis of breast cancer in 2D and 3D medical imaging through multifractal analysis

This Thesis describes the research work performed in the scope of a doctoral research program and presents its conclusions and contributions. The research activities were carried on in the industry with Siemens S.A. Healthcare Sector, in integration with a research team. Siemens S.A. Healthcare Sector is one of the world biggest suppliers of products, services and complete solutions in the medical sector. The company offers a wide selection of diagnostic and therapeutic equipment and information systems. Siemens products for medical imaging and in vivo diagnostics include: ultrasound, computer tomography, mammography, digital breast tomosynthesis, magnetic resonance, equipment to angiography and coronary angiography, nuclear imaging, and many others. Siemens has a vast experience in Healthcare and at the beginning of this project it was strategically interested in solutions to improve the detection of Breast Cancer, to increase its competitiveness in the sector. The company owns several patents related with self-similarity analysis, which formed the background of this Thesis. Furthermore, Siemens intended to explore commercially the computer- aided automatic detection and diagnosis eld for portfolio integration. Therefore, with the high knowledge acquired by University of Beira Interior in this area together with this Thesis, will allow Siemens to apply the most recent scienti c progress in the detection of the breast cancer, and it is foreseeable that together we can develop a new technology with high potential. The project resulted in the submission of two invention disclosures for evaluation in Siemens A.G., two articles published in peer-reviewed journals indexed in ISI Science Citation Index, two other articles submitted in peer-reviewed journals, and several international conference papers. This work on computer-aided-diagnosis in breast led to innovative software and novel processes of research and development, for which the project received the Siemens Innovation Award in 2012. It was very rewarding to carry on such technological and innovative project in a socially sensitive area as Breast Cancer.No cancro da mama a deteção precoce e o diagnóstico correto são de extrema importância na prescrição terapêutica e caz e e ciente, que potencie o aumento da taxa de sobrevivência à doença. A teoria multifractal foi inicialmente introduzida no contexto da análise de sinal e a sua utilidade foi demonstrada na descrição de comportamentos siológicos de bio-sinais e até na deteção e predição de patologias. Nesta Tese, três métodos multifractais foram estendidos para imagens bi-dimensionais (2D) e comparados na deteção de microcalci cações em mamogramas. Um destes métodos foi também adaptado para a classi cação de massas da mama, em cortes transversais 2D obtidos por ressonância magnética (RM) de mama, em grupos de massas provavelmente benignas e com suspeição de malignidade. Um novo método de análise multifractal usando a lacunaridade tri-dimensional (3D) foi proposto para classi cação de massas da mama em imagens volumétricas 3D de RM de mama. A análise multifractal revelou diferenças na complexidade subjacente às localizações das microcalci cações em relação aos tecidos normais, permitindo uma boa exatidão da sua deteção em mamogramas. Adicionalmente, foram extraídas por análise multifractal características dos tecidos que permitiram identi car os casos tipicamente recomendados para biópsia em imagens 2D de RM de mama. A análise multifractal 3D foi e caz na classi cação de lesões mamárias benignas e malignas em imagens 3D de RM de mama. Este método foi mais exato para esta classi cação do que o método 2D ou o método padrão de análise de contraste cinético tumoral. Em conclusão, a análise multifractal fornece informação útil para deteção auxiliada por computador em mamogra a e diagnóstico auxiliado por computador em imagens 2D e 3D de RM de mama, tendo o potencial de complementar a interpretação dos radiologistas