Segmentation and Feature Extraction of Tumors from Digital Mammograms

Mammography is one of the available techniques for the early detection of masses or abnormalities which is related to breast cancer. Breast Cancer is the uncontrolled of cells in the breast region, which may affect the other parts of the body. The most common abnormalities that might indicate breast cancer are masses and calcifications. Masses appear in a mammogram as fine, granular clusters and also masses will not have sharp boundaries, so often difficult to identify in a raw mammogram. Digital Mammography is one of the best available technologies currently being used for the early detection of breast cancer. Computer Aided Detection System has to be developed for the detection of masses and calcifications in Digital Mammogram, which acts as a secondary tool for the radiologists for diagnosing the breast cancer. In this paper, we have proposed a secondary tool for the radiologists that help them in the segmentation and feature extraction process. Keywords: Mammography, Breast Cancer, Masses, Calcification, Digital Mammography, Computer Aided Detection System, Segmentation, Feature Extractio

Semiautomatic contour detection of breast lesions in ultrasonic images with morphological operators and average radial derivative function

AbstractThis work presents a computerized lesion segmentation technique on breast ultrasound images. There were applied known techniques such as morphological filtering, Watershed transformation and average radial derivative function. To evaluate the performance of the proposed method, two protocols were established. For the first, the resulting segmentation contours were compared with those of 24 gold standard simulated ultrasound-like images, and, for second, with 36 breast US images manually delineated by two senior radiologists. Further, two evaluation parameters were used: the percentage of coincidence (CP) and the proportional distance (PD). The former indicates the similarity between contours, while the latter express the dissimilarity. The accuracy of the proposed method was evaluated by considering images with CP>80% and PD<10% as adequately delineated. It was higher than 80% for real images and higher than 88% for simulated images

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

Redes neurais artificiais no reconhecimento e classificação de padrões de calcificações mamárias em imagens de mamografia

Trabalho de Conclusão de Curso (graduação)—Universidade de Brasília, Faculdade UnB Gama, 2017.O câncer de mama é um dos mais incidentes no mundo e, apesar de sua alta taxa de cura, possui um processo complexo de diagnóstico. Por esse motivo, diversas técnicas vêm sendo desenvolvidas para possibilitar o diagnóstico precoce desse tipo de câncer. A alta complexidade da interpretação de exames mamográficos tem motivado a criação de Sistemas de Apoio ao Diagnóstico (SAD), que servem como uma segunda opinião para o profissional. Nesse contexto, essa pesquisa apresenta o desenvolvimento de uma metodologia para o reconhecimento e classificação automática de calcificações em imagens de mamografia para atuar como um SAD. Foram disponibilizadas para o estudo 70 imagens mamográficas, classificadas na categoria 4 pelo Breast Imaging-Reporting and Data System (B-IRADS). Com o objetivo de assegurar a qualidade do treinamento do sistema, o grupo inicial de 70 imagens foi aumentado artificialmente por meio de manipulações computacionais, sendo 140 o número final de imagens utilizadas. Em seguida, as imagens foram submetidas a técnicas de Processamento Digital de Imagens (PDI), afim de melhorar a qualidade e facilitar a extração de informações morfológicas das calcificações. Essas imagens foram utilizadas como entrada da Rede Neural Artificial (RNA) do tipo Multilayer Perceptron (MLP), que realizou o reconhecimento e a classificação dos achados. Para garantir sua capacidade de generalização, a RNA foi testada com imagens desconhecidas pelo sistema. Nesse caso, a acurácia apresentada pelo sistema foi de 61,9%.Breast cancer is one of the most frequent in the world, and despite its high cure rate, it has a complex diagnostic process. For this reason, several techniques has been developed to allow the early diagnosis of this type of cancer. The high complexity of the interpretation of mammographic exams has motivated the creation of Computer-Aided Diagnosis (CAD), which serve as a second opinion for the professional. In this context, this research presents the development of a methodology for the automatic recognition and classification of calcifications in mammography images to act as a CAD. To develop this study, 70 mammographic images, classified in category 4 by the Breast Imaging-Reporting and Data System (B-IRADS), were made available. In order to ensure the quality of system training, the initial group of 70 images was artificially increased by means of computational manipulations, being 140 the final number of images used. The images were then submitted to Digital Image Processing (DIP) techniques, in order to improve the quality and facilitate the extraction of morphological information from calcifications. These images were used as a input to the Multilayer Perceptron (MLP) type Artificial Neural Network (ANN), which performed the recognition and classification of the findings. To ensure its generalization capacity, the RNA was tested with images unknown to the system. In this case, the system accuracy was 61,9%

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

Modular Machine Learning Methods for Computer-Aided Diagnosis of Breast Cancer

The purpose of this study was to improve breast cancer diagnosis by reducing the number of benign biopsies performed. To this end, we investigated modular and ensemble systems of machine learning methods for computer-aided diagnosis (CAD) of breast cancer. A modular system partitions the input space into smaller domains, each of which is handled by a local model. An ensemble system uses multiple models for the same cases and combines the models\u27 predictions. Five supervised machine learning techniques (LDA, SVM, BP-ANN, CBR, CART) were trained to predict the biopsy outcome from mammographic findings (BIRADS™) and patient age based on a database of 2258 cases mixed from multiple institutions. The generalization of the models was tested on second set of 2177 cases. Clusters were identified in the database using a priori knowledge and unsupervised learning methods (agglomerative hierarchical clustering followed by K-Means, SOM, AutoClass). The performance of the global models over the clusters was examined and local models were trained for clusters. While some local models were superior to some global models, we were unable to build a modular CAD system that was better than the global BP-ANN model. The ensemble systems based on simplistic combination schemes did not result in significant improvements and more complicated combination schemes were found to be unduly optimistic. One of the most striking results of this dissertation was that CAD systems trained on a mixture of lesion types performed much better on masses than on calcifications. Our study of the institutional effects suggests that models built on cases mixed between institutions may overcome some of the weaknesses of models built on cases from a single institution. It was suggestive that each of the unsupervised methods identified a cluster of younger women with well-circumscribed or obscured, oval-shaped masses that accounted for the majority of the BP-ANN’s recommendations for follow up. From the cluster analysis and the CART models, we determined a simple diagnostic rule that performed comparably to the global BP-ANN. Approximately 98% sensitivity could be maintained while providing approximately 26% specificity. This should be compared to the clinical status quo of 100% sensitivity and 0% specificity on this database of indeterminate cases already referred to biopsy

Recognizing deviations from normalcy for brain tumor segmentation

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.Includes bibliographical references (p. 180-189).A framework is proposed for the segmentation of brain tumors from MRI. Instead of training on pathology, the proposed method trains exclusively on healthy tissue. The algorithm attempts to recognize deviations from normalcy in order to compute a fitness map over the image associated with the presence of pathology. The resulting fitness map may then be used by conventional image segmentation techniques for honing in on boundary delineation. Such an approach is applicable to structures that are too irregular, in both shape and texture, to permit construction of comprehensive training sets. We develop the method of diagonalized nearest neighbor pattern recognition, and we use it to demonstrate that recognizing deviations from normalcy requires a rich understanding of context. Therefore, we propose a framework for a Contextual Dependency Network (CDN) that incorporates context at multiple levels: voxel intensities, neighborhood coherence, intra-structure properties, inter-structure relationships, and user input. Information flows bi-directionally between the layers via multi-level Markov random fields or iterated Bayesian classification. A simple instantiation of the framework has been implemented to perform preliminary experiments on synthetic and MRI data.by David Thomas Gering.Ph.D