Método de detección de distorsiones de la arquitectura de la glándula mamaria a partir de imágenes radiológicas

Este documento de tesis presenta la planeación, implementación y pruebas de un nuevo método que sirve como soporte para la detección de distorsiones de la arquitectura en la glándula mamaria a partir de imágenes de radiología de mama. El método asiste a los especialistas en el proceso de decisión diagnóstica como segundo intérprete en el análisis de mamografías, mediante la integración de cuatro etapas principales: preprocesamiento, detección de regiones de interés que sean candidatas a la posible presencia de distorsión de la arquitectura de la glándula mamaria, extracción y selección de características de las regiones de interés detectadas y finalmente clasificación de esas regiones de interés con base en las características extraídas de las mismas. El método propuesto se valida mediante el análisis de imágenes mamográficas de la base de datos DDSM, logrando valores de precisión general hasta de un 90.7% lo cual lo convierte en una base importante en la búsqueda de la reducción del alto número de diagnósticos errados que conducen a las altas tasas de morbilidad por cáncer de mama que se presentan en el mundo./Abstract. This thesis presents the design, implementation and test of a new method that serves as support for the detection of architectural distortion in the mammary gland from breast radiology images. The method proposed here assists the specialists in the diagnosis of breast cáncer through four main phases: preprocessing, detection of regions of interest that are candidates for the possible presence of architectural distortion of the mammary gland, feature selection and extraction and finally classification of these regions of interest based on the extracted features. The method proposed in this thesis is validated through the analysis of mammographic images from DDSM obtaining values of 90.7% in the overall accuracy. This result is a very important contribution and encourage the research in order to reduce the high number of misdiagnoses that are currently presented and lead to high rates of morbidity from breast cáncer.Maestrí

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

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