74 research outputs found
Detection of microcalcifications in mammograms using error of prediction and statistical measures
A two-stage method for detecting microcalcifications in
mammograms is presented. In the first stage, the determination of
the candidates for microcalcifications is performed. For this purpose,
a 2-D linear prediction error filter is applied, and for those pixels
where the prediction error is larger than a threshold, a statistical
measure is calculated to determine whether they are candidates for
microcalcifications or not. In the second stage, a feature vector is
derived for each candidate, and after a classification step using a
support vector machine, the final detection is performed. The algorithm
is tested with 40 mammographic images, from Screen Test:
The Alberta Program for the Early Detection of Breast Cancer with
50- m resolution, and the results are evaluated using a freeresponse
receiver operating characteristics curve. Two different
analyses are performed: an individual microcalcification detection
analysis and a cluster analysis. In the analysis of individual microcalcifications,
detection sensitivity values of 0.75 and 0.81 are obtained
at 2.6 and 6.2 false positives per image, on the average,
respectively. The best performance is characterized by a sensitivity
of 0.89, a specificity of 0.99, and a positive predictive value of 0.79.
In cluster analysis, a sensitivity value of 0.97 is obtained at 1.77
false positives per image, and a value of 0.90 is achieved at 0.94
false positive per imag
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
Detecting microcalcification clusters in digital mammograms: Study for inclusion into computer aided diagnostic prompting system
Among signs of breast cancer encountered in digital mammograms radiologists point to microcalcification clusters (MCCs). Their detection is a challenging problem from both medical and image processing point of views. This work presents two concurrent methods for MCC detection, and studies their possible inclusion to a computer aided diagnostic prompting system. One considers Wavelet Domain Hidden Markov Tree (WHMT) for modeling microcalcification edges. The model is used for differentiation between MC and non-MC edges based on the weighted maximum likelihood (WML) values. The classification of objects is carried out using spatial filters. The second method employs SUSAN edge detector in the spatial domain for mammogram segmentation. Classification of objects as calcifications is carried out using another set of spatial filters and Feedforward Neural Network (NN). A same distance filter is employed in both methods to find true clusters. The analysis of two methods is performed on 54 image regions from the mammograms selected randomly from DDSM database, including benign and cancerous cases as well as cases which can be classified as hard cases from both radiologists and the computer perspectives. WHMT/WML is able to detect 98.15% true positive (TP) MCCs under 1.85% of false positives (FP), whereas the SUSAN/NN method achieves 94.44% of TP at the cost of 1.85% for FP. The comparison of these two methods suggests WHMT/WML for the computer aided diagnostic prompting. It also certifies the low false positive rates for both methods, meaning less biopsy tests per patient
Computer-aided Diagnosis in Breast Ultrasound
Cancer remains a leading cause of death in Taiwan, and the prevalence of breast cancer has increased in recent years. The early detection and diagnosis of breast cancer is the key to ensuring prompt treatment and a reduced death rate. Mammography and ultrasound (US) are the main imaging techniques used in the detection of breast cancer. The heterogeneity of breast cancers leads to an overlap in benign and malignant ultrasonography images, and US examinations are also operator dependent. Recently, computer-aided diagnosis (CAD) has become a major research topic in medical imaging and diagnosis. Technical advances such as tissue harmonic imaging, compound imaging, split screen imaging and extended field-of-view imaging, Doppler US, the use of intravenous contrast agents, elastography, and CAD systems have expanded the clinical application of breast US. Breast US CAD can be an efficient computerized model to provide a second opinion and avoid interobserver variation. Various breast US CAD systems have been developed using techniques which combine image texture extraction and a decision-making algorithm. However, the textural analysis is system dependent and can only be performed well using one specific US system. Recently, several researchers have demonstrated the use of such CAD systems with various US machines mainly for preprocessing techniques designed to homogenize textural features between systems. Morphology-based CAD systems used for the diagnosis of solid breast tumors have the advantage of being nearly independent of either the settings of US systems or different US machines. Future research on CAD systems should include pathologically specific tissue-related and hormonerelated conjecture, which could be applied to picture archiving and communication systems or teleradiology
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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
A New Approach for Clustered MCs Classification with Sparse Features Learning and TWSVM
In digital mammograms, an early sign of breast cancer is the existence of microcalcification clusters (MCs), which is very important to the early breast cancer detection. In this paper, a new approach is proposed to classify and detect MCs. We formulate this classification problem as sparse feature learning based classification on behalf of the test samples with a set of training samples, which are also known as a “vocabulary” of visual parts. A visual information-rich vocabulary of training samples is manually built up from a set of samples, which include MCs parts and no-MCs parts. With the prior ground truth of MCs in mammograms, the sparse feature learning is acquired by the lP-regularized least square approach with the interior-point method. Then we designed the sparse feature learning based MCs classification algorithm using twin support vector machines (TWSVMs). To investigate its performance, the proposed method is applied to DDSM datasets and compared with support vector machines (SVMs) with the same dataset. Experiments have shown that performance of the proposed method is more efficient or better than the state-of-art methods
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