Anke Meyer-baese - Academia.edu (original) (raw)

Papers by Anke Meyer-baese

Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging, 2018

Neo-adjuvant chemotherapy (NAC) is the treatment of choice in patients with locally advanced brea... more Neo-adjuvant chemotherapy (NAC) is the treatment of choice in patients with locally advanced breast cancer to reduce tumor burden, and potentially enable breast conservation. Response to treatment is assessed by histopathology from surgical specimen, a pathological complete response (pCR), or a minimal residual disease are associated with an improved disease-free, and overall survival. Early identification of non-responders is crucial as these patients might require different, or more aggressive treatment. Multi-parametric magnetic resonance imaging (mpMRI) using different morphological and functional MRI parameters such as T2-weighted, dynamic contrast-enhanced (DCE) MRI, and diffusion weighted imaging (DWI) has emerged as the method of choice for the early response assessments to NAC. Although, mpMRI is superior to conventional mammography for predicting treatment response, and evaluating residual disease, yet there is still room for improvement. In the past decade, the field of medical imaging analysis has grown exponentially, with an increased numbers of pattern recognition tools, and an increase in data sizes. These advances have heralded the field of radiomics. Radiomics allows the high-throughput extraction of the quantitative features that result in the conversion of images into mineable data, and the subsequent analysis of the data for an improved decision support with response monitoring during NAC being no exception. In this paper, we determine the importance and ranking of the extracted parameters from mpMRI using T2-weighted, DCE, and DWI for prediction of pCR and patient outcomes with respect to metastases and disease-specific death.

Big Data: Learning, Analytics, and Applications, 2019

Big data has been one of the hottest topics of scientific discussions in the recent years. In ear... more Big data has been one of the hottest topics of scientific discussions in the recent years. In early 2000s, an industry analyst attempted to describe big data as the three Vs: Volume, Velocity, and Variability. With the new technologies such as Hadoop, it is now feasible to store and use extremely large volumes of data that comes in at an unprecedented velocity. The variability of this data can be large as it can come in different formats such as text documents, voice or video, and financial transactions. Big data analytics has been proven to be useful is various fields such as science, sports, advertising, health care, genomic sequence data, and medical imaging. This study presents a brief overview of big data analytics in medical imaging approaches with considering the importance of contemporary machine learning techniques such as deep learning.

Cancers

The precise initial characterization of contrast-enhancing brain tumors has significant consequen... more The precise initial characterization of contrast-enhancing brain tumors has significant consequences for clinical outcomes. Various novel neuroimaging methods have been developed to increase the specificity of conventional magnetic resonance imaging (cMRI) but also the increased complexity of data analysis. Artificial intelligence offers new options to manage this challenge in clinical settings. Here, we investigated whether multiclass machine learning (ML) algorithms applied to a high-dimensional panel of radiomic features from advanced MRI (advMRI) and physiological MRI (phyMRI; thus, radiophysiomics) could reliably classify contrast-enhancing brain tumors. The recently developed phyMRI technique enables the quantitative assessment of microvascular architecture, neovascularization, oxygen metabolism, and tissue hypoxia. A training cohort of 167 patients suffering from one of the five most common brain tumor entities (glioblastoma, anaplastic glioma, meningioma, primary CNS lymphom...

Complexity, 2018

This paper aims at developing new theory-driven biomarkers by implementing and evaluating novel t... more This paper aims at developing new theory-driven biomarkers by implementing and evaluating novel techniques from resting-state scans that can be used in relapse prediction for nicotine-dependent patients and future treatment efficacy. Two classes of patients were studied. One class took the drug N-acetylcysteine and the other class took a placebo. Then, the patients underwent a double-blind smoking cessation treatment and the resting-state fMRI scans of their brains before and after treatment were recorded. The scientific research goal of this study was to interpret the fMRI connectivity maps based on machine learning algorithms to predict the patient who will relapse and the one who will not. In this regard, the feature matrix was extracted from the image slices of brain employing voxel selection schemes and data reduction algorithms. Then, the feature matrix was fed into the machine learning classifiers including optimized CART decision tree and Naive-Bayes classifier with standard...

Computational Imaging III, 2018

Accurate methods for breast cancer diagnosis are of capital importance for selection and guidance... more Accurate methods for breast cancer diagnosis are of capital importance for selection and guidance of treatment and optimal patient outcomes. In dynamic contrast enhancing magnetic resonance imaging (DCE-MRI), the accurate differentiation of benign and malignant breast tumors that present as non-mass enhancing (NME) lesions is challenging, often resulting in unnecessary biopsies. Here we propose a new approach for the accurate diagnosis of such lesions with high resolution DCE-MRI by taking advantage of seven robust classification methods to discriminate between malignant and benign NME lesions using their dynamic curves at the voxel level, and test it in a manually delineated dataset. The tested approaches achieve a diagnostic accuracy up to 94% accuracy, sensitivity of 99 % and specificity of 90% respectively, with superiority of high temporal compared to high spatial resolution sequences.

Sensing and Analysis Technologies for Biomedical and Cognitive Applications 2016, 2016

Diagnostically challenging lesions pose a challenge both for the radiological reading and also fo... more Diagnostically challenging lesions pose a challenge both for the radiological reading and also for current CAD systems. They are not well-defined in both morphology (geometric shape) and kinetics (temporal enhancement) and pose a problem to lesion detection and classification. Their strong phenotypic differences can be visualized by MRI. Radiomics represents a novel approach to achieve a detailed quantification of the tumour phenotypes by analyzing a large number of image descriptors. In this paper, we apply a quantitative radiomics approach based on shape, texture and kinetics tumor features and evaluate it in comparison to a reduced-order feature approach in a computer-aided diagnosis system applied to diagnostically challenging lesions.

Medical Imaging 2022: Biomedical Applications in Molecular, Structural, and Functional Imaging

Signals and communication technology, 2014

Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging, 2018

Imaging connectomics emerged as an important field in modern neuroimaging to represent the intera... more Imaging connectomics emerged as an important field in modern neuroimaging to represent the interaction of structural and functional brain areas. Static graph networks are the mathematical structure to capture these interactions modeled by Pearson correlations between the representative area signals. Dynamical functional resting state networks seen in most fMRI experiments can not be represented by the classic correlation graph network. The changes in brain connectivity observed in many neuro-degenerative diseases in longitudinal data series suggest that more sophisticated graph networks to capture the dynamical properties of the brain networks are required. Furthermore, certain brain areas seem to act as "disease epicenters" being responsible for the spread of neuro-degenerative diseases. To mathematically describe these aspects, we propose a novel framework based on pinning controllability applied to dynamic graphs and seek to determine the changes in the "driver nodes" during the course of the disease. In contrast to other current research in pinning controllability, we aim to identify the best driver nodes describing disease evolution with respect to connectivity changes and location of the best driver nodes in functional 18 F-Fluorodeoxyglucose Positron Emission Tomography (18 FDG-PET) and structural Magnetic Resonance Imaging (MRI) connectivity graphs in healthy controls (CN), and patients with mild cognitive impairment (MCI), and Alzheimer's disease (AD). We present the theoretical framework for determining the best driver nodes in connectivity graphs and their relation to disease evolution in dementia. We revolutionize the current graph analysis in brain networks and apply the concept of dynamic graph theory in connection with pinning controllability to reveal differences in the location of "disease epicenters" that play an important role in the temporal evolution of dementia. The described research will constitute a leap in biomedical research related to novel disease prediction trajectories and precision dementia therapies.

Proceedings of SPIE, Mar 25, 1998

Based on Amaris mathematical formulation of the self- organization of synaptic efficacies and neu... more Based on Amaris mathematical formulation of the self- organization of synaptic efficacies and neural response fields under the influence of external stimuli we show that if the map is a contraction, then the system has a unique equilibrium which is globally asymptotically stable; consequently the system acts as a stable encoder of external input stimuli. The system converges to a fixed point representing the steady- state of the neural activity which has as an upper bound the superposition of the spatial integrals of the weight function between neighboring neurons and the stimulus autocorrelation function.

The 2006 IEEE International Joint Conference on Neural Network Proceedings, 2006

Unsupervised competitive neural networks (UCNN) are an established technique in pattern recogniti... more Unsupervised competitive neural networks (UCNN) are an established technique in pattern recognition for feature extraction and cluster analysis. A novel model of an unsupervised competitive neural network implementing a multi-time scale dynamics is proposed in this paper. The global asymptotic stability of the equilibrium points of this continuous-time recurrent system whose weights are adapted based on a competitive learning law is mathematically analyzed. The proposed neural network and the derived results are compared with those obtained from other multi-time scale architectures.

Contrast Media & Molecular Imaging, Oct 24, 2018

Nonmass-enhancing (NME) lesions constitute a diagnostic challenge in dynamic contrast-enhanced ma... more Nonmass-enhancing (NME) lesions constitute a diagnostic challenge in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the breast. Computer-aided diagnosis (CAD) systems provide physicians with advanced tools for analysis, assessment, and evaluation that have a signi cant impact on the diagnostic performance. Here, we propose a new approach to address the challenge of NME lesion detection and segmentation, taking advantage of independent component analysis (ICA) to extract data-driven dynamic lesion characterizations. A set of independent sources was obtained from the DCE-MRI dataset of breast cancer patients, and the dynamic behavior of the di erent tissues was described by multiple dynamic curves, together with a set of eigenimages describing the scores for each voxel. A new test image is projected onto the independent source space using the unmixing matrix, and each voxel is classi ed by a support vector machine (SVM) that has already been trained with manually delineated data. A solution to the high false-positive rate problem is proposed by controlling the SVM hyperplane location, outperforming previously published approaches.

Contrast Media & Molecular Imaging, 2018

Nonmass-enhancing (NME) lesions constitute a diagnostic challenge in dynamic contrast-enhanced ma... more Nonmass-enhancing (NME) lesions constitute a diagnostic challenge in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the breast. Computer-aided diagnosis (CAD) systems provide physicians with advanced tools for analysis, assessment, and evaluation that have a significant impact on the diagnostic performance. Here, we propose a new approach to address the challenge of NME lesion detection and segmentation, taking advantage of independent component analysis (ICA) to extract data-driven dynamic lesion characterizations. A set of independent sources was obtained from the DCE-MRI dataset of breast cancer patients, and the dynamic behavior of the different tissues was described by multiple dynamic curves, together with a set of eigenimages describing the scores for each voxel. A new test image is projected onto the independent source space using the unmixing matrix, and each voxel is classified by a support vector machine (SVM) that has already been trained with manually...

Medical Imaging 2023: Biomedical Applications in Molecular, Structural, and Functional Imaging

Fraud has been a worldwide issue that is facing the major economies of the world. Within an econo... more Fraud has been a worldwide issue that is facing the major economies of the world. Within an economical system, undetected and unpunished fraudulent activities can erode the public trust in law enforcement institutions and even incentivize more fraud. Therefore, detection of fraudulent activities and prosecution of responsible entities is of utmost importance for financial regulatory bodies around the globe. Of the challenges rising with this task is the scarcity of detection resources (auditors) and the fraudsters constantly adapting to the new circumstances of the market. To address these issues, this paper proposes an evolutionary framework for credit fraud detection with the ability to incorporate (and adapt to) the incoming data in real-time. The goal of the framework is to identify the entities with high a risk of fraud for efficient targeting of the scarce resources. The data that is generated as a result of the audits are fed into the framework for further training.

Proceedings of SPIE, Mar 28, 2005

Exploratory data analysis techniques are applied to the segmentation of lesions in MRI mammograph... more Exploratory data analysis techniques are applied to the segmentation of lesions in MRI mammography as a first step of a computer-aided diagnosis system. ICA and clustering techniques are tested on biomedical time-series representing breast MRI scans. This techniques enable the extraction of spatial and temporal features of dynamic MRI data stemming from patients with confirmed lesion diagnosis. By revealing regional

Proceedings of SPIE, Mar 24, 2016

Prostate cancer is reported to be the second most frequently diagnosed cancer of men in the world... more Prostate cancer is reported to be the second most frequently diagnosed cancer of men in the world. In practise, diagnosis can be affected by multiple factors which reduces the chance to detect the potential lesions. In the last decades, new imaging techniques mainly based on MRI are developed in conjunction with Computer-Aided Diagnosis (CAD) systems to help radiologists for such diagnosis. CAD systems are usually designed as a sequential process consisting of four stages: pre-processing, segmentation, registration and classification. As a pre-processing, image normalization is a critical and important step of the chain in order to design a robust classifier and overcome the inter-patients intensity variations. However, little attention has been dedicated to the normalization of T2W-Magnetic Resonance Imaging (MRI) prostate images. In this paper, we propose two methods to normalize T2W-MRI prostate images: (i) based on a Rician a priori and (ii) based on a Square-Root Slope Function (SRSF) representation which does not make any assumption regarding the Probability Density Function (PDF) of the data. A comparison with the state-of-the-art methods is also provided. The normalization of the data is assessed by comparing the alignment of the patient PDFs in both qualitative and quantitative manners. In both evaluation, the normalization using Rician a priori outperforms the other state-of-the-art methods.

Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging, 2018

Neo-adjuvant chemotherapy (NAC) is the treatment of choice in patients with locally advanced brea... more Neo-adjuvant chemotherapy (NAC) is the treatment of choice in patients with locally advanced breast cancer to reduce tumor burden, and potentially enable breast conservation. Response to treatment is assessed by histopathology from surgical specimen, a pathological complete response (pCR), or a minimal residual disease are associated with an improved disease-free, and overall survival. Early identification of non-responders is crucial as these patients might require different, or more aggressive treatment. Multi-parametric magnetic resonance imaging (mpMRI) using different morphological and functional MRI parameters such as T2-weighted, dynamic contrast-enhanced (DCE) MRI, and diffusion weighted imaging (DWI) has emerged as the method of choice for the early response assessments to NAC. Although, mpMRI is superior to conventional mammography for predicting treatment response, and evaluating residual disease, yet there is still room for improvement. In the past decade, the field of medical imaging analysis has grown exponentially, with an increased numbers of pattern recognition tools, and an increase in data sizes. These advances have heralded the field of radiomics. Radiomics allows the high-throughput extraction of the quantitative features that result in the conversion of images into mineable data, and the subsequent analysis of the data for an improved decision support with response monitoring during NAC being no exception. In this paper, we determine the importance and ranking of the extracted parameters from mpMRI using T2-weighted, DCE, and DWI for prediction of pCR and patient outcomes with respect to metastases and disease-specific death.

Big Data: Learning, Analytics, and Applications, 2019

Big data has been one of the hottest topics of scientific discussions in the recent years. In ear... more Big data has been one of the hottest topics of scientific discussions in the recent years. In early 2000s, an industry analyst attempted to describe big data as the three Vs: Volume, Velocity, and Variability. With the new technologies such as Hadoop, it is now feasible to store and use extremely large volumes of data that comes in at an unprecedented velocity. The variability of this data can be large as it can come in different formats such as text documents, voice or video, and financial transactions. Big data analytics has been proven to be useful is various fields such as science, sports, advertising, health care, genomic sequence data, and medical imaging. This study presents a brief overview of big data analytics in medical imaging approaches with considering the importance of contemporary machine learning techniques such as deep learning.

Cancers

The precise initial characterization of contrast-enhancing brain tumors has significant consequen... more The precise initial characterization of contrast-enhancing brain tumors has significant consequences for clinical outcomes. Various novel neuroimaging methods have been developed to increase the specificity of conventional magnetic resonance imaging (cMRI) but also the increased complexity of data analysis. Artificial intelligence offers new options to manage this challenge in clinical settings. Here, we investigated whether multiclass machine learning (ML) algorithms applied to a high-dimensional panel of radiomic features from advanced MRI (advMRI) and physiological MRI (phyMRI; thus, radiophysiomics) could reliably classify contrast-enhancing brain tumors. The recently developed phyMRI technique enables the quantitative assessment of microvascular architecture, neovascularization, oxygen metabolism, and tissue hypoxia. A training cohort of 167 patients suffering from one of the five most common brain tumor entities (glioblastoma, anaplastic glioma, meningioma, primary CNS lymphom...

Complexity, 2018

This paper aims at developing new theory-driven biomarkers by implementing and evaluating novel t... more This paper aims at developing new theory-driven biomarkers by implementing and evaluating novel techniques from resting-state scans that can be used in relapse prediction for nicotine-dependent patients and future treatment efficacy. Two classes of patients were studied. One class took the drug N-acetylcysteine and the other class took a placebo. Then, the patients underwent a double-blind smoking cessation treatment and the resting-state fMRI scans of their brains before and after treatment were recorded. The scientific research goal of this study was to interpret the fMRI connectivity maps based on machine learning algorithms to predict the patient who will relapse and the one who will not. In this regard, the feature matrix was extracted from the image slices of brain employing voxel selection schemes and data reduction algorithms. Then, the feature matrix was fed into the machine learning classifiers including optimized CART decision tree and Naive-Bayes classifier with standard...

Computational Imaging III, 2018

Accurate methods for breast cancer diagnosis are of capital importance for selection and guidance... more Accurate methods for breast cancer diagnosis are of capital importance for selection and guidance of treatment and optimal patient outcomes. In dynamic contrast enhancing magnetic resonance imaging (DCE-MRI), the accurate differentiation of benign and malignant breast tumors that present as non-mass enhancing (NME) lesions is challenging, often resulting in unnecessary biopsies. Here we propose a new approach for the accurate diagnosis of such lesions with high resolution DCE-MRI by taking advantage of seven robust classification methods to discriminate between malignant and benign NME lesions using their dynamic curves at the voxel level, and test it in a manually delineated dataset. The tested approaches achieve a diagnostic accuracy up to 94% accuracy, sensitivity of 99 % and specificity of 90% respectively, with superiority of high temporal compared to high spatial resolution sequences.

Sensing and Analysis Technologies for Biomedical and Cognitive Applications 2016, 2016

Diagnostically challenging lesions pose a challenge both for the radiological reading and also fo... more Diagnostically challenging lesions pose a challenge both for the radiological reading and also for current CAD systems. They are not well-defined in both morphology (geometric shape) and kinetics (temporal enhancement) and pose a problem to lesion detection and classification. Their strong phenotypic differences can be visualized by MRI. Radiomics represents a novel approach to achieve a detailed quantification of the tumour phenotypes by analyzing a large number of image descriptors. In this paper, we apply a quantitative radiomics approach based on shape, texture and kinetics tumor features and evaluate it in comparison to a reduced-order feature approach in a computer-aided diagnosis system applied to diagnostically challenging lesions.

Medical Imaging 2022: Biomedical Applications in Molecular, Structural, and Functional Imaging

Signals and communication technology, 2014

Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging, 2018

Imaging connectomics emerged as an important field in modern neuroimaging to represent the intera... more Imaging connectomics emerged as an important field in modern neuroimaging to represent the interaction of structural and functional brain areas. Static graph networks are the mathematical structure to capture these interactions modeled by Pearson correlations between the representative area signals. Dynamical functional resting state networks seen in most fMRI experiments can not be represented by the classic correlation graph network. The changes in brain connectivity observed in many neuro-degenerative diseases in longitudinal data series suggest that more sophisticated graph networks to capture the dynamical properties of the brain networks are required. Furthermore, certain brain areas seem to act as "disease epicenters" being responsible for the spread of neuro-degenerative diseases. To mathematically describe these aspects, we propose a novel framework based on pinning controllability applied to dynamic graphs and seek to determine the changes in the "driver nodes" during the course of the disease. In contrast to other current research in pinning controllability, we aim to identify the best driver nodes describing disease evolution with respect to connectivity changes and location of the best driver nodes in functional 18 F-Fluorodeoxyglucose Positron Emission Tomography (18 FDG-PET) and structural Magnetic Resonance Imaging (MRI) connectivity graphs in healthy controls (CN), and patients with mild cognitive impairment (MCI), and Alzheimer's disease (AD). We present the theoretical framework for determining the best driver nodes in connectivity graphs and their relation to disease evolution in dementia. We revolutionize the current graph analysis in brain networks and apply the concept of dynamic graph theory in connection with pinning controllability to reveal differences in the location of "disease epicenters" that play an important role in the temporal evolution of dementia. The described research will constitute a leap in biomedical research related to novel disease prediction trajectories and precision dementia therapies.

Proceedings of SPIE, Mar 25, 1998

Based on Amaris mathematical formulation of the self- organization of synaptic efficacies and neu... more Based on Amaris mathematical formulation of the self- organization of synaptic efficacies and neural response fields under the influence of external stimuli we show that if the map is a contraction, then the system has a unique equilibrium which is globally asymptotically stable; consequently the system acts as a stable encoder of external input stimuli. The system converges to a fixed point representing the steady- state of the neural activity which has as an upper bound the superposition of the spatial integrals of the weight function between neighboring neurons and the stimulus autocorrelation function.

The 2006 IEEE International Joint Conference on Neural Network Proceedings, 2006

Unsupervised competitive neural networks (UCNN) are an established technique in pattern recogniti... more Unsupervised competitive neural networks (UCNN) are an established technique in pattern recognition for feature extraction and cluster analysis. A novel model of an unsupervised competitive neural network implementing a multi-time scale dynamics is proposed in this paper. The global asymptotic stability of the equilibrium points of this continuous-time recurrent system whose weights are adapted based on a competitive learning law is mathematically analyzed. The proposed neural network and the derived results are compared with those obtained from other multi-time scale architectures.

Contrast Media & Molecular Imaging, Oct 24, 2018

Nonmass-enhancing (NME) lesions constitute a diagnostic challenge in dynamic contrast-enhanced ma... more Nonmass-enhancing (NME) lesions constitute a diagnostic challenge in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the breast. Computer-aided diagnosis (CAD) systems provide physicians with advanced tools for analysis, assessment, and evaluation that have a signi cant impact on the diagnostic performance. Here, we propose a new approach to address the challenge of NME lesion detection and segmentation, taking advantage of independent component analysis (ICA) to extract data-driven dynamic lesion characterizations. A set of independent sources was obtained from the DCE-MRI dataset of breast cancer patients, and the dynamic behavior of the di erent tissues was described by multiple dynamic curves, together with a set of eigenimages describing the scores for each voxel. A new test image is projected onto the independent source space using the unmixing matrix, and each voxel is classi ed by a support vector machine (SVM) that has already been trained with manually delineated data. A solution to the high false-positive rate problem is proposed by controlling the SVM hyperplane location, outperforming previously published approaches.

Contrast Media & Molecular Imaging, 2018

Nonmass-enhancing (NME) lesions constitute a diagnostic challenge in dynamic contrast-enhanced ma... more Nonmass-enhancing (NME) lesions constitute a diagnostic challenge in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the breast. Computer-aided diagnosis (CAD) systems provide physicians with advanced tools for analysis, assessment, and evaluation that have a significant impact on the diagnostic performance. Here, we propose a new approach to address the challenge of NME lesion detection and segmentation, taking advantage of independent component analysis (ICA) to extract data-driven dynamic lesion characterizations. A set of independent sources was obtained from the DCE-MRI dataset of breast cancer patients, and the dynamic behavior of the different tissues was described by multiple dynamic curves, together with a set of eigenimages describing the scores for each voxel. A new test image is projected onto the independent source space using the unmixing matrix, and each voxel is classified by a support vector machine (SVM) that has already been trained with manually...

Medical Imaging 2023: Biomedical Applications in Molecular, Structural, and Functional Imaging

Fraud has been a worldwide issue that is facing the major economies of the world. Within an econo... more Fraud has been a worldwide issue that is facing the major economies of the world. Within an economical system, undetected and unpunished fraudulent activities can erode the public trust in law enforcement institutions and even incentivize more fraud. Therefore, detection of fraudulent activities and prosecution of responsible entities is of utmost importance for financial regulatory bodies around the globe. Of the challenges rising with this task is the scarcity of detection resources (auditors) and the fraudsters constantly adapting to the new circumstances of the market. To address these issues, this paper proposes an evolutionary framework for credit fraud detection with the ability to incorporate (and adapt to) the incoming data in real-time. The goal of the framework is to identify the entities with high a risk of fraud for efficient targeting of the scarce resources. The data that is generated as a result of the audits are fed into the framework for further training.

Proceedings of SPIE, Mar 28, 2005

Exploratory data analysis techniques are applied to the segmentation of lesions in MRI mammograph... more Exploratory data analysis techniques are applied to the segmentation of lesions in MRI mammography as a first step of a computer-aided diagnosis system. ICA and clustering techniques are tested on biomedical time-series representing breast MRI scans. This techniques enable the extraction of spatial and temporal features of dynamic MRI data stemming from patients with confirmed lesion diagnosis. By revealing regional

Proceedings of SPIE, Mar 24, 2016

Prostate cancer is reported to be the second most frequently diagnosed cancer of men in the world... more Prostate cancer is reported to be the second most frequently diagnosed cancer of men in the world. In practise, diagnosis can be affected by multiple factors which reduces the chance to detect the potential lesions. In the last decades, new imaging techniques mainly based on MRI are developed in conjunction with Computer-Aided Diagnosis (CAD) systems to help radiologists for such diagnosis. CAD systems are usually designed as a sequential process consisting of four stages: pre-processing, segmentation, registration and classification. As a pre-processing, image normalization is a critical and important step of the chain in order to design a robust classifier and overcome the inter-patients intensity variations. However, little attention has been dedicated to the normalization of T2W-Magnetic Resonance Imaging (MRI) prostate images. In this paper, we propose two methods to normalize T2W-MRI prostate images: (i) based on a Rician a priori and (ii) based on a Square-Root Slope Function (SRSF) representation which does not make any assumption regarding the Probability Density Function (PDF) of the data. A comparison with the state-of-the-art methods is also provided. The normalization of the data is assessed by comparing the alignment of the patient PDFs in both qualitative and quantitative manners. In both evaluation, the normalization using Rician a priori outperforms the other state-of-the-art methods.