Image processing and machine learning techniques used in computer-aided detection system for mammogram screening - a review

This paper aims to review the previously developed Computer-aided detection (CAD) systems for mammogram screening because increasing death rate in women due to breast cancer is a global medical issue and it can be controlled only by early detection with regular screening. Till now mammography is the widely used breast imaging modality. CAD systems have been adopted by the radiologists to increase the accuracy of the breast cancer diagnosis by avoiding human errors and experience related issues. This study reveals that in spite of the higher accuracy obtained by the earlier proposed CAD systems for breast cancer diagnosis, they are not fully automated. Moreover, the false-positive mammogram screening cases are high in number and over-diagnosis of breast cancer exposes a patient towards harmful overtreatment for which a huge amount of money is being wasted. In addition, it is also reported that the mammogram screening result with and without CAD systems does not have noticeable difference, whereas the undetected cancer cases by CAD system are increasing. Thus, future research is required to improve the performance of CAD system for mammogram screening and make it completely automated

Deep-Learning-Based Computer- Aided Systems for Breast Cancer Imaging: A Critical Review

[EN] This paper provides a critical review of the literature on deep learning applications in breast tumor diagnosis using ultrasound and mammography images. It also summarizes recent advances in computer-aided diagnosis/detection (CAD) systems, which make use of new deep learning methods to automatically recognize breast images and improve the accuracy of diagnoses made by radiologists. This review is based upon published literature in the past decade (January 2010-January 2020), where we obtained around 250 research articles, and after an eligibility process, 59 articles were presented in more detail. The main findings in the classification process revealed that new DL-CAD methods are useful and effective screening tools for breast cancer, thus reducing the need for manual feature extraction. The breast tumor research community can utilize this survey as a basis for their current and future studies.This project has been co-financed by the Spanish Government Grant PID2019-107790RB-C22, "Software development for a continuous PET crystal systems applied to breast cancer".Jiménez-Gaona, Y.; Rodríguez Álvarez, MJ.; Lakshminarayanan, V. (2020). Deep-Learning-Based Computer- Aided Systems for Breast Cancer Imaging: A Critical Review. Applied Sciences. 10(22):1-29. https://doi.org/10.3390/app10228298S1291022Jemal, A., Bray, F., Center, M. M., Ferlay, J., Ward, E., & Forman, D. (2011). Global cancer statistics. CA: A Cancer Journal for Clinicians, 61(2), 69-90. doi:10.3322/caac.20107Gao, F., Chia, K.-S., Ng, F.-C., Ng, E.-H., & Machin, D. (2002). Interval cancers following breast cancer screening in Singaporean women. International Journal of Cancer, 101(5), 475-479. doi:10.1002/ijc.10636Munir, K., Elahi, H., Ayub, A., Frezza, F., & Rizzi, A. (2019). 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Development of breast cancer diagnosis system based on fuzzy logic and probabilistic neural network

Breast cancer is one of the most common kinds of cancers that infect females in the whole world. It has happened when the cells in breast tissues start to grow in an uncontrollable way. Because it leads to death, early detection and diagnosis is a very important task to save the patient's life. Due to the restriction of human observers, computer plays a significant role in detecting early cancer signs. The proposed system uses a multi-resolution analysis and a top-hat operation for detecting the suspicious regions in a mammogram image. The discrete wavelet transform feature analysis is utilized for extracting features from the region of interest. Fuzzy Logic (FL) and Probabilistic Neural Network (PNN) are utilized for classifying the tumor into normal or abnormal. The differences between the proposed system and other researches are the use of adaptive threshold value depending on each image, by using Discrete Wavelet Transform (DWT) in both segmentation and feature extraction phases, which decrease complexity and time. Additionally, the detection of more than one tumor in the breast mammogram image and the utilization of FL and PNN work on increasing the system efficiency that led to raising the accuracy rate of the system and reducing the time. The obtained results of accuracy, sensitivity, and specificity were equal to 99 %, 98 %, and 47 %, respectively, and these results showed that the proposed system is more accurate than the other previous related work

Classification of Mammogram Images by Using SVM and KNN

Breast cancer is a fairly diverse illness that affects a large percentage of women in the west. A mammogram is an X-ray-based evaluation of a woman's breasts to see if she has cancer. One of the earliest prescreening diagnostic procedures for breast cancer is mammography. It is well known that breast cancer recovery rates are significantly increased by early identification. Mammogram analysis is typically delegated to skilled radiologists at medical facilities. Human mistake, however, is always a possibility. Fatigue of the observer can commonly lead to errors, resulting in intraobserver and interobserver variances. The image quality affects the sensitivity of mammographic screening as well. The goal of developing automated techniques for detection and grading of breast cancer images is to reduce various types of variability and standardize diagnostic procedures. The classification of breast cancer images into benign (tumor increasing, but not harmful) and malignant (cannot be managed, it causes death) classes using a two-way classification algorithm is shown in this study. The two-way classification data mining algorithms are utilized because there are not many abnormal mammograms. The first classification algorithm, k-means, divides a given dataset into a predetermined number of clusters. Support Vector Machine (SVM), a second classification algorithm, is used to identify the optimal classification function to separate members of the two classes in the training dat

Visibility recovery on images acquired in attenuating media. Application to underwater, fog, and mammographic imaging

136 p.When acquired in attenuating media, digital images of ten suffer from a particularly complex degradation that reduces their visual quality, hindering their suitability for further computational applications, or simply decreasing the visual pleasan tness for the user. In these cases, mathematical image processing reveals it self as an ideal tool to recover some of the information lost during the degradation process. In this dissertation,we deal with three of such practical scenarios in which this problematic is specially relevant, namely, underwater image enhancement, fogremoval and mammographic image processing. In the case of digital mammograms,X-ray beams traverse human tissue, and electronic detectorscapture them as they reach the other side. However, the superposition on a bidimensional image of three-dimensional structures produces low contraste dimages in which structures of interest suffer from a diminished visibility, obstructing diagnosis tasks. Regarding fog removal, the loss of contrast is produced by the atmospheric conditions, and white colour takes over the scene uniformly as distance increases, also reducing visibility.For underwater images, there is an added difficulty, since colour is not lost uniformly; instead, red colours decay the fastest, and green and blue colours typically dominate the acquired images. To address all these challenges,in this dissertation we develop new methodologies that rely on: a)physical models of the observed degradation, and b) the calculus of variations.Equipped with this powerful machinery, we design novel theoreticaland computational tools, including image-dependent functional energies that capture the particularities of each degradation model. These energie sare composed of different integral terms that are simultaneous lyminimized by means of efficient numerical schemes, producing a clean,visually-pleasant and use ful output image, with better contrast and increased visibility. In every considered application, we provide comprehensive qualitative (visual) and quantitative experimental results to validateour methods, confirming that the developed techniques out perform other existing approaches in the literature

A Comparative Study on the Methods Used for the Detection of Breast Cancer

Among women in the world, the death caused by the Breast cancer has become the leading role. At an initial stage, the tumor in the breast is hard to detect. Manual attempt have proven to be time consuming and inefficient in many cases. Hence there is a need for efficient methods that diagnoses the cancerous cell without human involvement with high accuracy. Mammography is a special case of CT scan which adopts X-ray method with high resolution film. so that it can detect well the tumors in the breast. This paper describes the comparative study of the various data mining methods on the detection of the breast cancer by using image processing techniques

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

Breast Cancer : automatic detection and risk analysis through machine learning algorithms, using mammograms

Tese de Mestrado Integrado, Engenharia Biomédica e Biofísica (Engenharia Clínica e Instrumentação Médica), 2021, Universidade de Lisboa, Faculdade de CiênciasCom 2.3 milhões de casos diagnosticados em todo o Mundo, durante o ano de 2020, o cancro da mama tornou-se aquele com maior incidência, nesse mesmo ano, considerando ambos os sexos. Anualmente, em Portugal, são diagnosticados aproximadamente sete mil (7000) novos casos de cancro da mama, com mil oitocentas (1800) mulheres a morrerem, todos os anos, devido a esta doença - indicando uma taxa de mortalidade de aproximadamente 5 mulheres por dia. A maior parte dos diagnósticos de cancro da mama ocorrem ao nível de programas de rastreio, que utilizam mamografia. Esta técnica de imagem apresenta alguns problemas: o facto de ser uma imagem a duas dimensões leva a que haja sobreposição de tecidos, o que pode mascarar a presença de tumores; e a fraca sensibilidade a mamas mais densas, sendo estas caraterísticas de mulheres com risco de cancro da mama mais elevado. Como estes dois problemas dificultam a leitura das mamografias, grande parte deste trabalhou focou-se na verificação do desempenho de métodos computacionais na tarefa de classificar mamografias em duas classes: cancro e não-cancro. No que diz respeito à classe “não cancro” (N = 159), esta foi constituída por mamografias saudáveis (N=84), e por mamografias que continham lesões benignas (N=75). Já a classe “cancro” continha apenas mamografias com lesões malignas (N = 73). A discriminação entre estas duas classes foi feita com recurso a algoritmos de aprendizagem automática. Múltiplos classificadores foram otimizados e treinados (Ntreino=162, Nteste = 70), recorrendo a um conjunto de características previamente selecionado, que descreve a textura de toda a mamografia, em vez de apenas uma única Região de Interesse. Estas características de textura baseiam-se na procura de padrões: sequências de pixéis com a mesma intensidade, ou pares específicos de pixéis. O classificador que apresentou uma performance mais elevada foi um dos Support Vector Machine (SVM) treinados – AUC= 0.875, o que indica um desempenho entre o bom e o excelente. A Percent Mammographic Density (%PD) é um importante fator de risco no que diz respeito ao desenvolvimento da doença, pelo que foi estudado se a sua adição ao set de features selecionado resultaria numa melhor performance dos classificadores. O classificador, treinado e otimizado utilizando as features de textura e os cálculos de %PD, com maior capacidade discriminativa foi um Linear Discriminant Analysis (LDA) – AUC = 0.875. Uma vez que a performance é igual à obtida com o classificador que utiliza apenas features de textura, conclui-se que a %PD parece não contribuir com informação relevante. Tal pode ocorrer porque as próprias características de textura já têm informação sobre a densidade da mama. De forma a estudar-se de que modo o desempenho destes métodos computacionais pode ser afetado por piores condições de aquisição de imagem, foi simulado ruído gaussiano, e adicionado ao set de imagens utilizado para testagem. Este ruído, adicionado a cada imagem com quatro magnitudes diferentes, resultou numa AUC de 0.765 para o valor mais baixo de ruído, e numa AUC de 0.5 para o valor de ruído mais elevado. Tais resultados indicam que, para níveis de ruído mais baixo, o classificador consegue, ainda assim, manter uma performance satisfatória – o que deixa de se verificar para valores mais elevados de ruído. Estudou-se, também, se a aplicação de técnicas de filtragem – com um filtro mediana – poderia ajudar a recuperar informação perdida aquando da adição de ruído. A aplicação do filtro a todas as imagens ruidosas resultou numa AUC de 0.754 para o valor mais elevado de ruído, atingindo assim um desempenho similar ao set de imagens menos ruidosas, antes do processo de filtragem (AUC=0.765). Este resultados parecem indicar que, na presença de más condições de aquisição, a aplicação de um filtro mediana pode ajudar a recuperar informação, conduzindo assim a um melhor desempenho dos métodos computacionais. No entanto, esta mesma conclusão parece não se verificar para valores de ruído mais baixo onde a AUC após filtragem acaba por ser mais reduzida. Tal resultado poderá indicar que, em situações onde o nível de ruído é mais baixo, a técnica de filtragem não só remove o ruído, como acaba também por, ela própria, remover informação ao nível da textura da imagem. De modo a verificar se mamas com diferentes densidades afetavam a performance do classificador, foram criados três sets de teste diferentes, cada um deles contendo imagens de mamas com a mesma densidade (1, 2, e 3). Os resultados obtidos indicam-nos que um aumento na densidade das mamas analisadas não resulta, necessariamente, numa diminuição da capacidade em discriminar as classes definidas (AUC = 0.864, AUC = 0.927, AUC= 0.905; para as classes 1, 2, e 3 respetivamente). A utilização da imagem integral para analisar de textura, e a utilização de imagens de datasets diferentes (com dimensões de imagem diferentes), poderiam introduzir um viés na classificação, especialmente no que diz respeito às diferentes áreas da mama. Para verificar isso mesmo, utilizando o coeficiente de correlação de Pearson, ρ = 0.3, verificou-se que a área da mama (e a percentagem de ocupação) tem uma fraca correlação com a classificação dada a cada imagem. A construção do classificador, para além de servir de base a todos os testes apresentados, serviu também o propósito de criar uma interface interativa, passível de ser utilizada como ficheiro executável, sem necessidade de instalação de nenhum software. Esta aplicação permite que o utilizador carregue imagens de mamografia, exclua background desnecessário para a análise da imagem, extraia features, teste o classificador construído e dê como output, no ecrã, a classe correspondente à imagem carregada. A análise de risco de desenvolvimento da doença foi conseguida através da análise visual da variação dos valores das features de textura ao longo dos anos para um pequeno set (N=11) de mulheres. Esta mesma análise permitiu descortinar aquilo que parece ser uma tendência apresentada apenas por mulheres doentes, na mamografia imediatamente anterior ao diagnóstico da doença. Todos os resultados obtidos são descritos profundamente ao longo deste documento, onde se faz, também, uma referência pormenorizada a todos os métodos utilizados para os obter. O resultado da classificação feita apenas com as features de textura encontra-se dentro dos valores referenciados no estado-da-arte, indicando que o uso de features de textura, por si só, demonstrou ser profícuo. Para além disso, tal resultado serve também de indicação que o recurso a toda a imagem de mamografia, sem o trabalho árduo de definição de uma Região de Interesse, poderá ser utilizado com relativa segurança. Os resultados provenientes da análise do efeito da densidade e da área da mama, dão também confiança no uso do classificador. A interface interativa que resultou desta primeira fase de trabalho tem, potencialmente, um diferenciado conjunto de aplicações: no campo médico, poderá servir de auxiliar de diagnóstico ao médico; já no campo da análise computacional, poderá servir para a definição da ground truth de potenciais datasets que não tenham legendas definidas. No que diz respeito à análise de risco, a utilização de um dataset de dimensões reduzidas permitiu, ainda assim, compreender que existem tendências nas variações das features ao longo dos anos, que são especificas de mulheres que desenvolveram a doença. Os resultados obtidos servem, então, de indicação que a continuação desta linha de trabalho, procurando avaliar/predizer o risco, deverá ser seguida, com recurso não só a datasets mais completos, como também a métodos computacionais de aprendizagem automática.Two million and three hundred thousand Breast Cancer (BC) cases were diagnosed in 2020, making it the type of cancer with the highest incidence that year, considering both sexes. Breast Cancer diagnosis usually occurs during screening programs using mammography, which has some downsides: the masking effect due to its 2-D nature, and its poor sensitivity concerning dense breasts. Since these issues result in difficulties reading mammograms, the main part of this work aimed to verify how a computer vision method would perform in classifying mammograms into two classes: cancer and non-cancer. The ‘non-cancer group’ (N=159) was composed by images with healthy tissue (N=84) and images with benign lesions (N=75), while the cancer group (N=73) contained malignant lesions. To achieve this, multiple classifiers were optimized and trained (Ntrain = 162, Ntest = 70) with a previously selected ideal sub-set of features that describe the texture of the entire image, instead of just one small Region of Interest (ROI). The classifier with the best performance was Support Vector Machine (SVM), (AUC = 0.875), which indicates a good-to-excellent capability discriminating the two defined groups. To assess if Percent Mammographic Density (%PD), an important risk factor, added important information, a new classifier was optimized and trained using the selected sub-set of texture features plus the %PD calculation. The classifier with the best performance was a Linear Discriminant Analysis (LDA), (AUC=0.875), which seems to indicate, once it achieves the same performance as the classifier using only texture features, that there is no relevant information added from %PD calculations. This happens because texture already includes information on breast density. To understand how the classifier would perform in worst image acquisition conditions, gaussian noise was added to the test images (N=70), with four different magnitudes (AUC= 0.765 for the lowest noise value vs. AUC ≈ 0.5 for the highest). A median filter was applied to the noised images towards evaluating if information could be recovered. For the highest noise value, after filtering, the AUC was very close to the one obtained for the lowest noise value before filtering (0.754 vs 0.765), which indicates information recovery. The effect of density in classifier performance was evaluated by constructing three different test sets, each containing images from a density class (1,2,3). It was seen that an increase in density did not necessarily resulted in a decrease in performance, which indicates that the classifier is robust to density variation (AUC = 0.864, AUC= 0.927, AUC= 0.905 ; for class 1, 2, and 3 respectively). Since the entire image is being analyzed, and images come from different datasets, it was verified if breast area was adding bias to classification. Pearson correlation coefficient provided an output of ρ = 0.22, showing that there is a weak correlation between these two variables. Finally, breast cancer risk was assessed by visual texture feature analysis through the years, for a small set of women (N=11). This visual analysis allowed to unveil what seems to be a pattern amongst women who developed the disease, in the mammogram immediately before diagnosis. The details of each phase, as well as the associated final results are deeply described throughout this document. The work done in the first classification task resulted in a state-of-the-art performance, which may serve as foundation for new research in the area, without the laborious work of ROI definition. Besides that, the use of texture features alone proved to be fruitful. Results concerning risk may serve as basis for future work in the area, with larger datasets and the incorporation of Computer Vision methods