9 research outputs found
Denoising and enhancement of mammographic images under the assumption of heteroscedastic additive noise by an optimal subband thresholding
Mammographic images suffer from low contrast and signal dependent noise, and a very small size of tumoral signs is not easily detected, especially for an early diagnosis of breast cancer. In this context, many methods proposed in literature fail for lack of generality. In particular, too weak assumptions on the noise model, e.g., stationary normal additive noise, and an inaccurate choice of the wavelet family that is applied, can lead to an information loss, noise emphasizing, unacceptable enhancement results, or in turn an unwanted distortion of the original image aspect. In this paper, we consider an optimal wavelet thresholding, in the context of Discrete Dyadic Wavelet Transforms, by directly relating all the parameters involved in both denoising and contrast enhancement to signal dependent noise variance (estimated by a robust algorithm) and to the size of cancer signs. Moreover, by performing a reconstruction from a zero-approximation in conjunction with a Gaussian smoothing filter, we are able to extract the background and the foreground of the image separately, as to compute suitable contrast improvement indexes. The whole procedure will be tested on high resolution X-ray mammographic images and compared with other techniques. Anyway, the visual assessment of the results by an expert radiologist will be also considered as a subjective evaluation
Mini Kirsch Edge Detection and Its Sharpening Effect
In computer vision, edge detection is a crucial step in identifying the objects’ boundaries in an image. The existing edge detection methods function in either spatial domain or frequency domain, fail to outline the high continuity boundaries of the objects. In this work, we modified four-directional mini Kirsch edge detection kernels which enable full directional edge detection. We also introduced the novel involvement of the proposed method in image sharpening by adding the resulting edge map onto the original input image to enhance the edge details in the image. From the edge detection performance tests, our proposed method acquired the highest true edge pixels and true non-edge pixels detection, yielding the highest accuracy among all the comparing methods. Moreover, the sharpening effect offered by our proposed framework could achieve a more favorable visual appearance with a competitive score of peak signal-to-noise ratio and structural similarity index value compared to the most widely used unsharp masking and Laplacian of Gaussian sharpening methods. The edges of the sharpened image are further enhanced could potentially contribute to better boundary tracking and higher segmentation accuracy
BREAST CANCER DETECTION USING COMPUTATIONAL INTELLIGENCE
Mammograms are the best tool to detect an early disease of breast cancer. In
mammography, medical experts look for clustered microcalcifications and
irregular density masses. As microcalcification is a tiny speck of calcium in
breast, it appears as white spot in mammogram. Problem occurred when the
clinician reads the mammograms using a magnifying glass, as it is difficult to
detect calcification because there is a wide range of abnormalities and it also due
to the small size and their similarity with other tissue structure. One of the
problems is to distinguish between malignant and benign tumors. Thus, the
objectives of this project are to enhance mammogram image using image
processing technique and to provide a pattern recognition system by signifying
whether further investigation is needed, therefore it may assist medical expert in
detection of breast cancer. Accordingly, the scope of this project is based on the
pattern recognition system, which includes preprocessing, feature extraction, and
classification. The task for the project is divided into two parts. The first part is
the enhancement of the image and the detection of calcification. The second part
of the project is to design, develop, and test the network whether it run as
expected. As the result, mammogram images have been processed through image
processing by using MATLAB, and opening morphological operation has been
used for the detection. A pattern recognition system has been developed by the
use of neural network. As a conclusion, a successful implementation of pattern
recognition system as one way to detect breast cancer could help medical field in
diagnosing breast cancer
Mammography Techniques and Review
Mammography remains at the backbone of medical tools to examine the human breast. The early detection of breast cancer typically uses adjunct tests to mammogram such as ultrasound, positron emission mammography, electrical impedance, Computer-aided detection systems and others. In the present digital era it is even more important to use the best new techniques and systems available to improve the correct diagnosis and to prevent mortality from breast cancer. The first part of this book deals with the electrical impedance mammographic scheme, ultrasound axillary imaging, position emission mammography and digital mammogram enhancement. A detailed consideration of CBR CAD System and the availability of mammographs in Brazil forms the second part of this book. With the up-to-date papers from world experts, this book will be invaluable to anyone who studies the field of mammography
Development of Impulsive Noise Detection Schemes for Selective Filtering in Images
Image Noise Suppression is a highly demanded approach in digital imaging
systems design. Impulsive noise is one such noise, which is frequently encountered
problem in acquistion, transmission and processing of images. In the area of image
restoration, many state-of-the art filters consist of two main processes, classification
(detection) and reconstruction (filtering). Classification is used to separate
uncorrupted pixels from corrupted pixels. Reconstruction involves replacing the
corrupted pixels by certain approximation technique. In this thesis such schemes
of impulsive noise detection and filtering thereof are proposed.
Impulsive noise can be Salt & Pepper Noise (SPN) or Random Valued Impulsive
Noise (RVIN). Only RVIN model is considered in this thesis because of its realistic
presence. In the RVIN model a corrupted pixel can take any value in the valid
range.
Adaptive threshold selection is emphasized for all the four proposed noise detection
schemes. Incorporation of adaptive threshold into the noise detection
process led to more reliable and more efficient detection of noise. Based on the
noisy image characteristics and their statistics, threshold values are selected.
To validate the efficacy of proposed noise filtering schemes, an application to
image sharpening has been investigated under the noise conditions. It has been
observed, if the noisy image passes through the sharpening scheme, the noise
gets amplified and as a result the restored results are distorted. However, the
prefiltering operations using the proposed schemes enhances the result to a greater
extent.
Extensive simulations and comparisons are done with competent schemes. It is
observed, in general, that the proposed schemes are better in suppressing impulsive
noise at different noise ratios than their counterparts
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
Imaging of the Breast
Early detection of breast cancer combined with targeted therapy offers the best outcome for breast cancer patients. This volume deal with a wide range of new technical innovations for improving breast cancer detection, diagnosis and therapy. There is a special focus on improvements in mammographic image quality, image analysis, magnetic resonance imaging of the breast and molecular imaging. A chapter on targeted therapy explores the option of less radical postoperative therapy for women with early, screen-detected breast cancers