336 research outputs found
Hybrid Region-based Image Compression Scheme for Mamograms and Ultrasound Images
The need for transmission and archive of mammograms and ultrasound Images has
dramatically increased in tele-healthcare applications. Such images require large
amount of' storage space which affect transmission speed. Therefore an effective
compression scheme is essential. Compression of these images. in general. laces a
great challenge to compromise between the higher compression ratio and the relevant
diagnostic information. Out of the many studied compression schemes. lossless
.
IPl. (i-
LS and lossy SPII IT are found to he the most efficient ones. JPEG-LS and SI'll IT are
chosen based on a comprehensive experimental study carried on a large number of
mammograms and ultrasound images of different sizes and texture. The lossless
schemes are evaluated based on the compression ratio and compression speed. The
distortion in the image quality which is introduced by lossy methods evaluated based
on objective criteria using Mean Square Error (MSE) and Peak signal to Noise Ratio
(PSNR). It is found that lossless compression can achieve a modest compression ratio
2: 1 - 4: 1. bossy compression schemes can achieve higher compression ratios than
lossless ones but at the price of the image quality which may impede diagnostic
conclusions. In this work, a new compression approach called Ilvbrid Region-based Image
Compression Scheme (IIYRICS) has been proposed for the mammograms and
ultrasound images to achieve higher compression ratios without compromising the
diagnostic quality. In I LYRICS, a modification for JPI; G-LS is introduced to encode
the arbitrary shaped disease affected regions. Then Shape adaptive SPIT IT is applied
on the remaining non region of interest. The results clearly show that this hybrid
strategy can yield high compression ratios with perfect reconstruction of diagnostic
relevant regions, achieving high speed transmission and less storage requirement. For
the sample images considered in our experiment, the compression ratio increases
approximately ten times. However, this increase depends upon the size of the region
of interest chosen. It is also föund that the pre-processing (contrast stretching) of
region of interest improves compression ratios on mammograms but not on ultrasound
images
Wavelets and Imaging Informatics: A Review of the Literature
AbstractModern medicine is a field that has been revolutionized by the emergence of computer and imaging technology. It is increasingly difficult, however, to manage the ever-growing enormous amount of medical imaging information available in digital formats. Numerous techniques have been developed to make the imaging information more easily accessible and to perform analysis automatically. Among these techniques, wavelet transforms have proven prominently useful not only for biomedical imaging but also for signal and image processing in general. Wavelet transforms decompose a signal into frequency bands, the width of which are determined by a dyadic scheme. This particular way of dividing frequency bands matches the statistical properties of most images very well. During the past decade, there has been active research in applying wavelets to various aspects of imaging informatics, including compression, enhancements, analysis, classification, and retrieval. This review represents a survey of the most significant practical and theoretical advances in the field of wavelet-based imaging informatics
Multiscale wavelet representations for mammographic feature analysis
This paper introduces a novel approach for accomplishing mammographic feature analysis through multiresolution representations. We show that efficient (nonredundant) representations may be identified from digital mammography and used to enhance specific mammographic features within a continuum of scale space. The multiresolution decomposition of wavelet transforms provides a natural hierarchy in which to embed an interactive paradigm for accomplishing scale space feature analysis. Choosing wavelets (or analyzing functions) that are simultaneously localized in both space and frequency, results in a powerful methodology for image analysis. Multiresolution and orientation selectivity, known biological mechanisms in primate vision, are ingrained in wavelet representations and inspire the techniques presented in this paper. Our approach includes local analysis of complete multiscale representations. Mammograms are reconstructed from wavelet coefficients, enhanced by linear, exponential and constant weight functions localized in scale space. By improving the visualization of breast pathology we can improve the changes of early detection of breast cancers (improve quality) while requiring less time to evaluate mammograms for most patients (lower costs)
The 1993 Space and Earth Science Data Compression Workshop
The Earth Observing System Data and Information System (EOSDIS) is described in terms of its data volume, data rate, and data distribution requirements. Opportunities for data compression in EOSDIS are discussed
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 Ã
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
extraÃdas por análise multifractal caracterÃsticas 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
Morphological quantitation software in breast MRI: application to neoadjuvant chemotherapy patients
The work in this thesis examines the use of texture analysis techniques and shape descriptors to analyse MR images of the breast and their application as a potential quantitative tool for prognostic indication.Textural information is undoubtedly very heavily used in a radiologist’s decision making process. However, subtle variations in texture are often missed, thus by quantitatively analysing MR images the textural properties that would otherwise be impossible to discern by simply visually inspecting the image can be obtained. Texture analysis is commonly used in image classification of aerial and satellite photography, studies have also focussed on utilising texture in MRI especially in the brain. Recent research has focussed on other organs such as the breast wherein lesion morphology is known to be an important diagnostic and prognostic indicator. Recent work suggests benefits in assessing lesion texture in dynamic contrast-enhanced (DCE) images, especially with regards to changes during the initial enhancement and subsequent washout phases. The commonest form of analysis is the spatial grey-level dependence matrix method, but there is no direct evidence concerning the most appropriate pixel separation and number of grey levels to utilise in the required co-occurrence matrix calculations. The aim of this work is to systematically assess the efficacy of DCE-MRI based textural analysis in predicting response to chemotherapy in a cohort of breast cancer patients. In addition an attempt was made to use shape parameters in order to assess tumour surface irregularity, and as a predictor of response to chemotherapy.In further work this study aimed to texture map DCE MR images of breast patients utilising the co-occurrence method but on a pixel by pixel basis in order to determine threshold values for normal, benign and malignant tissue and ultimately creating functionality within the in house developed software to highlight hotspots outlining areas of interest (possible lesions). Benign and normal data was taken from MRI screening data and malignant data from patients referred with known malignancies.This work has highlighted that textural differences between groups (based on response, nodal status, triple negative and biopsy grade groupings) are apparent and appear to be most evident 1-3 minutes post-contrast administration. Whilst the large number of statistical tests undertaken necessitates a degree of caution in interpreting the results, the fact that significant differences for certain texture parameters and groupings are consistently observed is encouraging.With regards to shape analysis this thesis has highlighted that some differences between groups were seen in shape descriptors but that shape may be limited as a prognostic indicator. Using textural analysis gave a higher proportion of significant differences whilst shape analysis results showed inconsistency across time points.With regards to the mapping this work successfully analysed the texture maps for each case and established lesion detection is possible. The study successfully highlighted hotspots in the breast patients data post texture mapping, and has demonstrated the relationship between sensitivity and false positive rate via hotspot thresholding
Hybrid Region-based Image Compression Scheme for Mamograms and Ultrasound Images
The need for transmission and archive of mammograms and ultrasound Images has
dramatically increased in tele-healthcare applications. Such images require large
amount of' storage space which affect transmission speed. Therefore an effective
compression scheme is essential. Compression of these images. in general. laces a
great challenge to compromise between the higher compression ratio and the relevant
diagnostic information. Out of the many studied compression schemes. lossless
.
IPl. (i-
LS and lossy SPII IT are found to he the most efficient ones. JPEG-LS and SI'll IT are
chosen based on a comprehensive experimental study carried on a large number of
mammograms and ultrasound images of different sizes and texture. The lossless
schemes are evaluated based on the compression ratio and compression speed. The
distortion in the image quality which is introduced by lossy methods evaluated based
on objective criteria using Mean Square Error (MSE) and Peak signal to Noise Ratio
(PSNR). It is found that lossless compression can achieve a modest compression ratio
2: 1 - 4: 1. bossy compression schemes can achieve higher compression ratios than
lossless ones but at the price of the image quality which may impede diagnostic
conclusions. In this work, a new compression approach called Ilvbrid Region-based Image
Compression Scheme (IIYRICS) has been proposed for the mammograms and
ultrasound images to achieve higher compression ratios without compromising the
diagnostic quality. In I LYRICS, a modification for JPI; G-LS is introduced to encode
the arbitrary shaped disease affected regions. Then Shape adaptive SPIT IT is applied
on the remaining non region of interest. The results clearly show that this hybrid
strategy can yield high compression ratios with perfect reconstruction of diagnostic
relevant regions, achieving high speed transmission and less storage requirement. For
the sample images considered in our experiment, the compression ratio increases
approximately ten times. However, this increase depends upon the size of the region
of interest chosen. It is also föund that the pre-processing (contrast stretching) of
region of interest improves compression ratios on mammograms but not on ultrasound
images
Perceptual lossless medical image coding
A novel perceptually lossless coder is presented for the compression of medical images. Built on the JPEG 2000 coding framework, the heart of the proposed coder is a visual pruning function, embedded with an advanced human vision model to identify and to remove visually insignificant/irrelevant information. The proposed coder offers the advantages of simplicity and modularity with bit-stream compliance. Current results have shown superior compression ratio gains over that of its information lossless counterparts without any visible distortion. In addition, a case study consisting of 31 medical experts has shown that no perceivable difference of statistical significance exists between the original images and the images compressed by the proposed coder
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