105 research outputs found
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
Fuzzy technique for microcalcifications clustering in digital mammograms
Background
Mammography has established itself as the most efficient technique for the identification of the pathological breast lesions. Among the various types of lesions, microcalcifications are the most difficult to identify since they are quite small (0.1-1.0 mm) and often poorly contrasted against an images background. Within this context, the Computer Aided Detection (CAD) systems could turn out to be very useful in breast cancer control.
Methods
In this paper we present a potentially powerful microcalcifications cluster enhancement method applicable to digital mammograms. The segmentation phase employs a form filter, obtained from LoG filter, to overcome the dependence from target dimensions and to optimize the recognition efficiency. A clustering method, based on a Fuzzy C-means (FCM), has been developed. The described method, Fuzzy C-means with Features (FCM-WF), was tested on simulated clusters of microcalcifications, implying that the location of the cluster within the breast and the exact number of microcalcifications are known.The proposed method has been also tested on a set of images from the mini-Mammographic database provided by Mammographic Image Analysis Society (MIAS) publicly available.
Results
The comparison between FCM-WF and standard FCM algorithms, applied on both databases, shows that the former produces better microcalcifications associations for clustering than the latter: with respect to the private and the public database we had a performance improvement of 10% and 5% with regard to the Merit Figure and a 22% and a 10% of reduction of false positives potentially identified in the images, both to the benefit of the FCM-WF. The method was also evaluated in terms of Sensitivity (93% and 82%), Accuracy (95% and 94%), FP/image (4% for both database) and Precision (62% and 65%).
Conclusions
Thanks to the private database and to the informations contained in it regarding every single microcalcification, we tested the developed clustering method with great accuracy. In particular we verified that 70% of the injected clusters of the private database remained unaffected if the reconstruction is performed with the FCM-WF. Testing the method on the MIAS databases allowed also to verify the segmentation properties of the algorithm, showing that 80% of pathological clusters remained unaffected
Comparison of similarity measures for the task of template matching of masses on serial mammograms
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134879/1/mp1892.pd
Prototypes for Content-Based Image Retrieval in Clinical Practice
Content-based image retrieval (CBIR) has been proposed as key technology for computer-aided diagnostics (CAD). This paper reviews the state of the art and future challenges in CBIR for CAD applied to clinical practice
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Detection of breast cancer microcalcifications in digitized mammograms. Developing segmentation and classification techniques for the processing of MIAS database mammograms based on the Wavelet Decomposition Transform and Support Vector Machines.
Mammography is used to aid early detection and diagnosis systems. It takes an x-ray
image of the breast and can provide a second opinion for radiologists. The earlier
detection is made, the better treatment works. Digital mammograms are dealt with by
Computer Aided Diagnosis (CAD) systems that can detect and analyze abnormalities in
a mammogram. The purpose of this study is to investigate how to categories cropped
regions of interest (ROI) from digital mammogram images into two classes; normal and
abnormal regions (which contain microcalcifications).
The work proposed in this thesis is divided into three stages to provide a concept
system for classification between normal and abnormal cases. The first stage is the
Segmentation Process, which applies thresholding filters to separate the abnormal
objects (foreground) from the breast tissue (background). Moreover, this study has been
carried out on mammogram images and mainly on cropped ROI images from different
sizes that represent individual microcalcification and ROI that represent a cluster of
microcalcifications. The second stage in this thesis is feature extraction. This stage
makes use of the segmented ROI images to extract characteristic features that would
help in identifying regions of interest. The wavelet transform has been utilized for this
process as it provides a variety of features that could be examined in future studies. The
third and final stage is classification, where machine learning is applied to be able to
distinguish between normal ROI images and ROI images that may contain
microcalcifications. The result indicated was that by combining wavelet transform and
SVM we can distinguish between regions with normal breast tissue and regions that
include microcalcifications
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