Images from an ordinary consumer digital camera convey information at a wide range of spatial (an... more Images from an ordinary consumer digital camera convey information at a wide range of spatial (and temporal) scales and enable the viewer to decompose the image into regions that are uniform in some way (colour, texture, ...), recognize familiar objects, determine spatial relationships between objects, and detect abnormalities (e.g. textural markings on a region expected to be plain). Though modern digital cameras are equipped with low noise electronics and excellent lenses that minimize pincushion (and similar) distortions, images also contain noise and artefacts such as red-eye in flash images. Widely distributed software packages such as Photoshop provide a set of ‘‘filtering’’ operations which enable the user to improve the image in some way: from image smoothing (typically local averaging) that removes noise and high frequencies, sharpening that increases high frequency content, contrast stretching, through to specialized algorithms, for example for red-eye reduction. Such image filtering is designed to improve the appearance of an image, relying on the human visual system to disregard any unwanted change of content of the image. Medical image analysis poses a far tougher challenge. First, there is an even greater need for image filtering, because medical images have a poorer noise-to-signal ratio than scenes taken with a digital camera, the spatial resolution is often frustratingly low, the contrast between anatomically distinct structures is often too low to be computed reliably using a standard image processing technique, and artefacts are common (e.g. motion and bias field in MRI). Second, changes to image content must be done in a highly controlled and reliable way that does not compromise clinical decision-making. For example, whereas it is generally acceptable to filter out local bright patches of noise, care must be taken in the case of mammography not to remove microcalcifications. This paper briefly explores some of the key areas of development in the area of filtering in Medical Imaging and how these techniques impact generally available software packages in routine use in a diagnostic setting. It is interesting to note that a great deal of image filtering takes place at what is usually regarded as a ‘‘preprocessing’’ stage in the formation of a medical image and is relatively invisible to a radiologist. However, there is an increasing awareness of the impact of post-processing algorithms – particularly filtering – in diagnostic software applications and an awareness of these types of techniques is useful. Noise equalization
Breast density has been shown to be one of the most significant risks for developing breast cance... more Breast density has been shown to be one of the most significant risks for developing breast cancer. The Breast Imaging Reporting and Data System (BI-RADS) has a four category classification scheme that describes the different breast densities. Yet, there is great interand intraobserver variability for density classification. This work presents a novel texture classification method and its application for the development of a completely automated breast density classification system. Textons can be thought of as the building block of texture. A new algorithm is proposed that captures the mammographic appearance of the different density patterns by evaluating the texton spatial dependence matrix (TDSM) for the breast region’s corresponding texton map. The TSDM is a new texture model that captures both statistical and structural/spatial texture characteristics. The TSDMs are evaluated for different density classes and corresponding texture models are established. Classification is achi...
4th IET International Conference on Advances in Medical, Signal and Information Processing (MEDSIP 2008), 2008
ABSTRACT The extraction of features for automated assessment for breast cancer detection and diag... more ABSTRACT The extraction of features for automated assessment for breast cancer detection and diagnosis requires identification of the breast tissue. The pectoral muscle in medio-lateral oblique (MLO) mammogram images is one of the few landmarks in the breast. Yet, it can bias and affect the results of any mammogram processing method. To avoid such effects it is often necessary to automatically identify and segment the pectoral muscle prior to breast tissue image analysis. We propose the use of Independent Component Analysis (ICA) for identification and subsequent removal of the pectoral muscle. The identification is posed as classification of image subsections corresponding to pectoral muscle and breast tissue as represented by a set of ICA basis functions. Average classification rates 97.3% and 83.3% for pectoral muscle and breast tissue respectively have been obtained.
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2012
The degree of stenosis of the common carotid artery (CCA) but also the characteristics of the art... more The degree of stenosis of the common carotid artery (CCA) but also the characteristics of the arterial wall including plaque size, composition and elasticity represent important predictors used in the assessment of the risk for future cardiovascular events. This paper proposes and evaluates an integrated system for the segmentation of atherosclerotic carotid plaque in ultrasound video of the CCA based on normalization, speckle reduction filtering (with the hybrid median filter) and parametric active contours. The algorithm is initialized in the first video frame of the cardiac cycle with human assistance and the moving atherosclerotic plaque borders are tracked and segmented in the subsequent frames. The algorithm is evaluated on 10 real CCA digitized videos from B-mode longitudinal ultrasound segments and is compared with the manual segmentations of an expert, for every 20 frames in a time span of 3-5 seconds, covering in general 2 cardiac cycles. The segmentation results are very ...
Journal of neuroradiology. Journal de neuroradiologie, Jan 23, 2014
This study investigates the application of texture analysis methods on brain T2-white matter lesi... more This study investigates the application of texture analysis methods on brain T2-white matter lesions detected with magnetic resonance imaging (MRI) for the prognosis of future disability in subjects diagnosed with clinical isolated syndrome (CIS) of multiple sclerosis (MS). Brain lesions and normal appearing white matter (NAWM) from 38 symptomatic untreated subjects diagnosed with CIS as well as normal white matter (NWM) from 20 healthy volunteers, were manually segmented, by an experienced MS neurologist, on transverse T2-weighted images obtained from serial brain MR imaging scans (0 and 6-12 months). Additional clinical information in the form of the Expanded Disability Status Scale (EDSS), a scale from 0 to 10, which provides a way of quantifying disability in MS and monitoring the changes over time in the level of disability, were also provided. Shape and most importantly different texture features including GLCM and laws were then extracted for all above regions, after image in...
Images from an ordinary consumer digital camera convey information at a wide range of spatial (an... more Images from an ordinary consumer digital camera convey information at a wide range of spatial (and temporal) scales and enable the viewer to decompose the image into regions that are uniform in some way (colour, texture, ...), recognize familiar objects, determine spatial relationships between objects, and detect abnormalities (e.g. textural markings on a region expected to be plain). Though modern digital cameras are equipped with low noise electronics and excellent lenses that minimize pincushion (and similar) distortions, images also contain noise and artefacts such as red-eye in flash images. Widely distributed software packages such as Photoshop provide a set of ‘‘filtering’’ operations which enable the user to improve the image in some way: from image smoothing (typically local averaging) that removes noise and high frequencies, sharpening that increases high frequency content, contrast stretching, through to specialized algorithms, for example for red-eye reduction. Such image filtering is designed to improve the appearance of an image, relying on the human visual system to disregard any unwanted change of content of the image. Medical image analysis poses a far tougher challenge. First, there is an even greater need for image filtering, because medical images have a poorer noise-to-signal ratio than scenes taken with a digital camera, the spatial resolution is often frustratingly low, the contrast between anatomically distinct structures is often too low to be computed reliably using a standard image processing technique, and artefacts are common (e.g. motion and bias field in MRI). Second, changes to image content must be done in a highly controlled and reliable way that does not compromise clinical decision-making. For example, whereas it is generally acceptable to filter out local bright patches of noise, care must be taken in the case of mammography not to remove microcalcifications. This paper briefly explores some of the key areas of development in the area of filtering in Medical Imaging and how these techniques impact generally available software packages in routine use in a diagnostic setting. It is interesting to note that a great deal of image filtering takes place at what is usually regarded as a ‘‘preprocessing’’ stage in the formation of a medical image and is relatively invisible to a radiologist. However, there is an increasing awareness of the impact of post-processing algorithms – particularly filtering – in diagnostic software applications and an awareness of these types of techniques is useful. Noise equalization
Breast density has been shown to be one of the most significant risks for developing breast cance... more Breast density has been shown to be one of the most significant risks for developing breast cancer. The Breast Imaging Reporting and Data System (BI-RADS) has a four category classification scheme that describes the different breast densities. Yet, there is great interand intraobserver variability for density classification. This work presents a novel texture classification method and its application for the development of a completely automated breast density classification system. Textons can be thought of as the building block of texture. A new algorithm is proposed that captures the mammographic appearance of the different density patterns by evaluating the texton spatial dependence matrix (TDSM) for the breast region’s corresponding texton map. The TSDM is a new texture model that captures both statistical and structural/spatial texture characteristics. The TSDMs are evaluated for different density classes and corresponding texture models are established. Classification is achi...
4th IET International Conference on Advances in Medical, Signal and Information Processing (MEDSIP 2008), 2008
ABSTRACT The extraction of features for automated assessment for breast cancer detection and diag... more ABSTRACT The extraction of features for automated assessment for breast cancer detection and diagnosis requires identification of the breast tissue. The pectoral muscle in medio-lateral oblique (MLO) mammogram images is one of the few landmarks in the breast. Yet, it can bias and affect the results of any mammogram processing method. To avoid such effects it is often necessary to automatically identify and segment the pectoral muscle prior to breast tissue image analysis. We propose the use of Independent Component Analysis (ICA) for identification and subsequent removal of the pectoral muscle. The identification is posed as classification of image subsections corresponding to pectoral muscle and breast tissue as represented by a set of ICA basis functions. Average classification rates 97.3% and 83.3% for pectoral muscle and breast tissue respectively have been obtained.
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2012
The degree of stenosis of the common carotid artery (CCA) but also the characteristics of the art... more The degree of stenosis of the common carotid artery (CCA) but also the characteristics of the arterial wall including plaque size, composition and elasticity represent important predictors used in the assessment of the risk for future cardiovascular events. This paper proposes and evaluates an integrated system for the segmentation of atherosclerotic carotid plaque in ultrasound video of the CCA based on normalization, speckle reduction filtering (with the hybrid median filter) and parametric active contours. The algorithm is initialized in the first video frame of the cardiac cycle with human assistance and the moving atherosclerotic plaque borders are tracked and segmented in the subsequent frames. The algorithm is evaluated on 10 real CCA digitized videos from B-mode longitudinal ultrasound segments and is compared with the manual segmentations of an expert, for every 20 frames in a time span of 3-5 seconds, covering in general 2 cardiac cycles. The segmentation results are very ...
Journal of neuroradiology. Journal de neuroradiologie, Jan 23, 2014
This study investigates the application of texture analysis methods on brain T2-white matter lesi... more This study investigates the application of texture analysis methods on brain T2-white matter lesions detected with magnetic resonance imaging (MRI) for the prognosis of future disability in subjects diagnosed with clinical isolated syndrome (CIS) of multiple sclerosis (MS). Brain lesions and normal appearing white matter (NAWM) from 38 symptomatic untreated subjects diagnosed with CIS as well as normal white matter (NWM) from 20 healthy volunteers, were manually segmented, by an experienced MS neurologist, on transverse T2-weighted images obtained from serial brain MR imaging scans (0 and 6-12 months). Additional clinical information in the form of the Expanded Disability Status Scale (EDSS), a scale from 0 to 10, which provides a way of quantifying disability in MS and monitoring the changes over time in the level of disability, were also provided. Shape and most importantly different texture features including GLCM and laws were then extracted for all above regions, after image in...
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Papers by Styliani Petroudi