&lt;title&gt;Automated detection of masses and clustered microcalcifications on mammograms&lt;/title&gt;

Fujita, Hiroshi; Endo, Tokiko; Matsubara, Tomoko; Hirako, Kenichi; Hara, Takeshi; Ueda, Hitoshi; Torisu, Yasuhiro; Alam, Nader Riahi; Horita, Katsuhei; Kido, Choichiro; Ishigaki, Takeo

doi:10.1117/12.208741

Cited by 9 publications

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“…On the other hand, this may impose a heavy burden on the physicians who make these diagnoses by observing mammographic images because it must be essentially avoided that any affected area will be missed out or the images will be misinterpreted due to excessive dependence on their own subjective judgment. To address this problem, we have made and are currently making attempts to develop a computer-assisted diagnosis (CAD) system, which assumes the role of an auxiliary imaging system for breast cancer diagnosis [1]. Breast cancer is largely classified into two categories: mass and microcalcification.…”

Section: Introductionmentioning

confidence: 99%

Development of an automated detection system for microcalcifications on mammograms by using the higher-order autocorrelation features

et al. 2004

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The purpose of this work is to develop a new pattern recognition method using the higher-order autocorrelation features (HOAFs), and to apply this to our microcalcification detection system on mammographic images. Microcalcification is a typical sign of breast cancer and tends to show up as very subtle shadows. We developed a triplering filter for detecting microcalcifications, and the prototype detection system is nearly complete. However, our prototype system does not allow for the detection of three types of microcalcifications, two of which are amorphous and linear microcalcifications and the third is obscured microcalcifications which is often confused with the background or circumference that have almost the same density. We targeted the amorphous type of microcalcification, which has a low contrast and easily goes undetected. The various features of microcalcifications and false-positive (FP) shadows were extracted and trained using the multi-regression analysis, and unknown images were recognized as a result of this training. As a result, amorphous microcalcifications were successfully detected with no increase in the number of FPs compared with our existing detection method.

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Section: Introductionmentioning

confidence: 99%

Development of an automated detection system for microcalcifications on mammograms by using the higher-order autocorrelation features

et al. 2004

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“…Usually, breast cancer appears as masses and clustered microcalcifications in mammograms. Many studies on automated detection schemes of mass shadows have been reported recently [1]- [7]. For example, Lai et al presented a method for detecting circumscribed masses [2].…”

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Development of an automated method for detecting mammographic masses with a partial loss of region

Hatanaka

Hara

Fujita

et al. 2001

IEEE Trans. Med. Imaging

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Abstract-Recently, we have been developing several automated algorithms for detecting masses on mammograms. For our algorithm, we devised an adaptive thresholding technique for detecting masses, but our system failed to detect masses with a partial loss of region that were located on the edge of the film. This is a common issue in all of the algorithms developed so far by other groups. In order to deal with this problem, we propose a new method in the present study. The partial loss masses are identified by their similarity to a sector-form model in the template matching process. To calculate the similarity, four features are applied: 1) average pixel value; 2) standard deviation of pixel values; 3) standard correlation coefficient defined by the sector-form model; and 4) concentration feature determined from the density gradient. After employing the new method to 335 digitized mammograms, the detection sensitivity for the partial loss masses jumped from 70% to 90% when the number of false positives was kept constant (0.2/image). Moreover, a combination of the existing method and the new method improved the true-positive rate up to 97%. Such results indicate that the new technique may improve the performance of our computeraided diagnosis system for mammographic masses effectively.Index Terms-Breast cancer, computer-aided diagnosis, mammography, mass detection, template matching.

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“…• Implementação de bancos de imagens para fins científicos, como por exemplo: utilização em sistemas CAD [Fujita, 1995], validação de algoritmos de simulação de estruturas anatômicas [Neves, 2005], aplicação de técnicas de processamento de imagens para determinação de parâmetros e automatização de processos [Nascimento, 2005], entre outros;…”

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Modelagem computacional de estruturas anatômicas em 3D e simulação de suas imagens radiográficas

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Dedico este trabalho aos meus pais, Antônio e Maria Aparecida, ao meu irmão Cledson e a todos que de alguma forma contribuíram para a sua realização, em especialà Prof a. Annie. AGRADECIMENTOS A Prof a. Annie France Frère Slaets, pela oportunidade, dedicação, colaboração e competência como orientadora, fatores indispensáveis para a realização desse trabalho. Ao professor e amigo Márcio Alexandre Marques, principal responsável pelo início da minha vida acadêmica na cidade de São Carlos. A Coordenação de Aperfeiçoamento de Pessoal de Nível Superior-CAPES, pelo apoio financeiro. Aos meus pais, Antônio e Maria Aparecida, agradeço por tudo, a começar por minha existência e a de meu irmão. Por uma vida dedicada ao trabalho e a família, pelas inúmeras oportunidades proporcionadas, pelos exemplos de conduta, amor e dedicação demonstrados durante todos os anos da minha vida, valores essenciais e determinantes na formação do meu caráter e personalidade. Ao meu querido irmão Cledson, pessoa de fundamental importância em minha vida, pela amizade e lealdade, sempre pronto e disposto a me ouvir a qualquer momento, hora ou lugar.

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<title>Automated detection of masses and clustered microcalcifications on mammograms</title>

Cited by 9 publications

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Development of an automated detection system for microcalcifications on mammograms by using the higher-order autocorrelation features

Development of an automated detection system for microcalcifications on mammograms by using the higher-order autocorrelation features

Development of an automated method for detecting mammographic masses with a partial loss of region

Modelagem computacional de estruturas anatômicas em 3D e simulação de suas imagens radiográficas

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