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Biomedical Imaging (ISBI), 2012 9th IEEE International Symposium on; 01/2012
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01/2011: pages 405-420; , ISBN: 9781439845578
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ABSTRACT: Our long-term research goal is to develop a fully automated, image-based diagnostic system for early diagnosis of pulmonary nodules that may lead to lung cancer. This paper focuses on validating our approach for monitoring the development of lung nodules detected in successive chest low dose computed tomography (LDCT) scans of a patient. Our methodology for monitoring the detected lung nodules includes 3-D LDCT data registration, which is non-rigid and involves two steps: (i) global target-to-prototype alignment of one scan to another using the learned prior appearance model followed by (ii) local alignment in order to correct for intricate relative deformations. This approach has been validated on elastic lung phantoms constructed using state-of-the-art microfluidics technology. The elastic lung phantoms are fabricated from a flexible transparent polymer, i.e., polydimethylsiloxane (PDMS). These Phantoms mimic the contractions and expansions of the lung and nodules seen during normal breathing. Experiments confirm the high accuracy of the proposed approach for measuring the growth rate of the detected lung nodules.
Image Processing (ICIP), 2010 17th IEEE International Conference on; 10/2010
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ABSTRACT: A novel approach is proposed for generating data driven models of the lung nodules appearing in low dose CT (LDCT) scans of the human chest. Four types of common lung nodules are analyzed using Active Appearance Model methods to create descriptive lung nodule models. The proposed approach is also applicable for automatic classification of nodules into pathologies given a descriptive database. This approach is a major step forward for early diagnosis of lung cancer. We show the performance of the new nodule models on clinical datasets which illustrates significant improvements in both sensitivity and specificity.
Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE; 10/2010
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ABSTRACT: An alternative method of diagnosing malignant lung nodules by their visual appearance rather than conventional growth rate is proposed. Spatial distribution of image intensities (or Hounsfield values) comprising the malignant nodule appearance is accurately modeled with a rotation invariant second-order Markov-Gibbs random field. Its neighborhood system and potentials are analytically learned from a training set of nodule images with normalized intensity ranges. Preliminary experiments on 109 lung nodules (51 malignant and 58 benign ones) resulted in the 96.3% correct classification (for the 95% confidence interval), showing the proposed method is a promising supplement to current technologies for early diagnostics of lung cancer.
Biomedical Imaging: From Nano to Macro, 2010 IEEE International Symposium on; 05/2010
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ABSTRACT: New techniques for more accurate segmentation of a 3D cerebrovascular system from time-of-flight (TOF) magnetic resonance angiography (MRA) data are proposed. In this paper, we describe TOF-MRA images and desired maps of regions (blood vessels and the other brain tissues) by a joint Markov-Gibbs random field model (MGRF) of independent image signals and interdependent region labels but focus on most accurate model identification. To better specify region borders, each empirical distribution of signals is precisely approximated by a Linear Combination of Discrete Gaussians (LCDG) with positive and negative components. We modify a conventional Expectation-Maximization (EM) algorithm to deal with the LCDG and develop a sequential EM-based technique to get an initial LCDG-approximation for the modified EM algorithm. The initial segmentation based on the LCDG-models is then iteratively refined using a GMRF model with analytically estimated potentials. To validate the accuracy of our algorithm, a special 3D geometrical phantom motivated by statistical analysis of the MRA-TOF data is designed. Experiments with both the phantom and 50 real data sets confirm high accuracy of the proposed approach.
Biomedical Imaging: From Nano to Macro, 2009. ISBI '09. IEEE International Symposium on; 08/2009
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ABSTRACT: Our long term research goal is to develop a fully automated, image-based diagnostic system for early diagnosis of pulmonary nodules that may lead to lung cancer. This paper focuses on monitoring the development of lung nodules detected in successive chest low dose (LD) CT scans of a patient. We propose a new methodology for 3D LDCT data registration which is non-rigid and involves two steps: (i) global alignment of one scan (target) to another scan (reference or prototype) using the learned prior appearance model followed by (ii) local alignment in order to correct for intricate deformations. After equalizing signals for two subsequent chest scans, visual appearance of these chest images is modeled with a Markov-Gibbs random field with pairwise interaction. We estimate the affine transformation that globally register the target to the prototype by gradient descent maximization of a special Gibbs energy function. To handle local deformations, we deform each voxel of the target over evolving closed equi-spaced surfaces (iso-surfaces) to closely match the prototype. The evolution of the iso-surfaces is guided by an exponential speed function in the directions that minimize distances between the corresponding voxel pairs on the iso-surfaces in both the data sets. Preliminary results on the 135 LDCT data sets from 27 patients show that our proper registration could lead to precise diagnosis and identification of the development of the detected pulmonary nodules.
Pattern Recognition, 2008. ICPR 2008. 19th International Conference on; 01/2009
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ABSTRACT: Our long term research goal is to develop a fully automated, image- based diagnostic system for early diagnosis of pulmonary nodules that may lead to lung cancer. This paper focuses on monitoring the development of lung nodules detected in successive chest low dose (LD) CT scans of a patient. We propose a new methodology for 3D LDCT data registration which is non-rigid and involves two steps: (i) global alignment of one scan (target) to another scan (reference or prototype) using the learned prior appearance model followed by (ii) local alignment in order to correct for intricate deformations. After equalizing signals for two subsequent chest scans, visual appearance of these chest images is modeled with a Markov-Gibbs random field with pairwise interaction. We estimate the affine transformation that globally register the target to the prototype by gradient descent maximization of a special Gibbs energy function. To handle local deformations, we deform each voxel of the target over evolving closed equi-spaced surfaces (iso-surfaces) to closely match the prototype. The evolution of the iso-surfaces is guided by an exponential speed function in the directions that minimize distances between the corresponding voxel pairs on the iso-surfaces in both the data sets. Preliminary results on the 135 LDCT data sets from 27 patients show that our proper registration could lead to precise diagnosis and identification of the development of the detected pulmonary nodules.
Biomedical Imaging: From Nano to Macro, 2008. ISBI 2008. 5th IEEE International Symposium on; 06/2008
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ABSTRACT: In this paper, we propose a novel algorithm for isolating lung abnormalities (nodules) from low dose spiral chest CT scans. The proposed algorithm consists of three main steps. The first step isolates the lung nodules, arteries, veins, bronchi, and bronchioles from the surrounding anatomical structures. The second step detects lung nodules using deformable 2D and 3D templates describing typical geometry and gray level distribution within the nodules of the same type. The detection combines the normalized cross-correlation template matching and genetic optimization algorithm. The final step eliminates the false positive nodules (FPNs) using three features that robustly define the true lung nodules. Accurate density estimation for these three features is obtained using logistic regression model and linear combination of Gaussians (LCG) with positive and negative components. This paper focuses on the second and third steps. Experiments with 200 patients' CT scans demonstrate the accuracy of our approach.
Image Processing, 2004. ICIP '04. 2004 International Conference on; 11/2004
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ABSTRACT: Automatic detection and recognition of lung cancer during mass screening of spiral computer tomographic (CT) chest scans is one of the most important problems of today's medical image analysis. We propose an algorithm for isolating lung abnormalities (nodules) from arteries, veins, bronchi, and bronchioles after all these objects have been already separated from the surrounding anatomical structures. The separation is presented elsewhere, and this paper focuses on nodule detection using deformable 3D and 2D templates describing typical geometry and gray level distribution within the nodules of the same type. The detection combines normalized cross-correlation template matching by genetic optimization and Bayesian post-classification. Experiments with 200 spiral low dose CT (LDCT) scans confirm the accuracy of our approach.
Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on; 09/2004
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ABSTRACT: Our aim is to develop a fully automatic computer-assisted diagnosis (CAD) system for lung cancer screening using chest spiral CT scans. A screening program on 1000 subjects aims at quantification of the effectiveness of low dose spiral CT scans for early diagnosis of lung cancer, and evaluation of its possible impact on improving the mortality rate of cancer patients. The paper presents an image analysis system for 3D reconstruction of the lungs and trachea, detection of lung abnormalities, identification/classification of these abnormalities with respect to specific diagnosis, and distributed visualization of the results over computer networks. We present two novel approaches for segmentation of the lung tissues from the surrounding structures in the chest cavity, and detection of abnormalities in the lungs. The segmentation algorithm is hierarchical, first isolating the background from the chest cavity, then isolating the lungs from surrounding structures (e.g., ribs, liver, and other organs). Abnormalities in the lungs are detected by analyzing the segmented lung tissues and extracting the isolated lumps that appear in various connected regions. 3D reconstructions are also generated for these abnormalities, to be used for subsequent identification/classification steps. Results on 50 subjects are shown, and have been evaluated against radiologists. Our image analysis approach has provided comparable results with respect to the experts. The approach is quite fast, and lends itself to distributed visualization over computer networks.
Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03). 2003 IEEE International Conference on; 05/2003 · 4.63 Impact Factor
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ABSTRACT: Curvature-based geometric features have been proven to be important for colonic polyp detection. In this paper, we present an automatic detection framework and color coding scheme to highlight the detected polyps. The key idea is to place the detected polyps at the same locations in a newly created polygonal dataset with the same topology and geometry properties as the triangulated mesh surface of real colon dataset, and assign different colors to the two separated datasets to highlight the polyps. Finally, we validate the proposed framework by computer simulated and real colon datasets. For fifteen synthetic polyps with different shapes and different sizes, the sensitivity is 100%, and false positive is 0. For four real colon datasets, the proposed algorithm has achieved the sensitivity of 75%.
Image Processing, 2007. ICIP 2007. IEEE International Conference on;
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ABSTRACT: A pulmonary nodule is the most common manifestation of lung cancer. Lung nodules are approximately-spherical regions of relatively high density that are visible in X-ray images of the lung. Large (generally defined as greater than 1 cm in diameter) malignant nodules can be easily detected with traditional imaging equipment and can be diagnosed by needle biopsy or bronchoscopy techniques. However, the diagnostic options for small malignant nodules are limited due to problems associated with accessing small tumors, especially if they are located deep in the tissue or away from the large airways; therefore, additional diagnostic and imaging techniques are needed. One of the most promising techniques for detecting small cancerous nodules relies on characterizing the nodule based on its growth rate. The growth rate is estimated by measuring the volumetric change of the detected lung nodules over time, so it is important to accurately measure the volume of the nodules to quantify their growth rate over time. In this paper, we introduce a novel Computer Assisted Diagnosis (CAD) system for early diagnosis of lung cancer. The proposed CAD system consists of five main steps. These steps are: (i) segmentation of lung tissues from low dose computed tomography (LDCT) images, (ii) detection of lung nodules from segmented lung tissues, (iii) a non-rigid registration approach to align two successive LDCT scans and to correct the motion artifacts caused by breathing and patient motion, (iv) segmentation of the detected lung nodules, and (v) quantification of the volumetric changes. Our preliminary classification results based on the analysis of the growth rate of both benign and malignant nodules for 10 patients (6 patients diagnosed as malignant and 4 diagnosed as benign) were 100% for 95% confidence interval. The preliminary results of the proposed image analysis have yielded promising results that would supplement the use of current technologies for diagnosing lung cancer.
Image Processing, 2007. ICIP 2007. IEEE International Conference on;
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ABSTRACT: Our long term research goal is to develop an image-based approach for early diagnosis of lung nodules that may lead to lung cancer. This paper focuses on monitoring the progress of detected lung nodules in successive chest low dose CT (LDCT) scans of a patient using non-rigid registration. In this paper, we propose a new methodology for 3D LDCT data registration. The registration methodology is non-rigid and involves two steps: global alignment of one scan (target data) to another scan (reference data) using the learned prior appearance model followed by local alignments in order to correct for intricate deformations. From two subsequent chest scans, visual appearance of the chest images, after equalizing their signals, are modeled with a Markov-Gibbs random field with pairwise interaction. Our approach is based on finding the affine transformation to register one data set (target data) to another data set (reference data) by maximizing a special Gibbs energy function using a gradient descent algorithm. To get accurate appearance model, we developed a new approach to an automatically select the most important cliques that describe the visual appearance of LDCT data. To handle local deformations, we propose a new approach based on deforming each voxel over evolving closed and equi-spaced surfaces (iso-surfaces) to closely match the prototype. The evolution of the iso-surfaces is guided by an exponential speed function in the directions minimizing distances between corresponding pixel pairs on the iso-surfaces on both data sets. Our preliminary results on 10 patients show that the proper registration could lead to precise identification of the progress of the detected lung nodules.
Image Processing, 2007. ICIP 2007. IEEE International Conference on;
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ABSTRACT: Our long term research goal is to develop a fully automated, image-based diagnostic system for early diagnosis of pulmonary nodules that may lead to lung cancer. This paper focuses on monitoring the development of lung nodules detected in successive chest low dose (LD) CT scans of a patient. We propose a new methodology for 3D LDCT data registration which is non-rigid and involves two steps: (i) global target-to-prototype alignment of one scan to another using the learned prior appearance model followed by (ii) local alignment in order to correct for intricate relative deformations. After equalizing signals for two subsequent chest scans, visual appearance of these chest images is described using a Markov–Gibbs random field (MGRF) model with multiple pairwise interaction. An affine transformation that globally registers a target to a prototype is estimated by the gradient ascent-based maximization of a special Gibbs energy function. To get an accurate visual appearance model, we developed a new approach to automatic selection of most characteristic second-order cliques that describe pairwise interactions in the LDCT data. To handle local deformations, we displace each voxel of the target over evolving closed equi-spaced surfaces (iso-surfaces) to closely match the prototype. The evolution of the iso-surfaces is guided by a speed function in the directions that minimize distances between the corresponding voxel pairs on the iso-surfaces in both the data sets. Preliminary results on the 135 LDCT data sets from 27 patients show that the proposed accurate registration could lead to precise diagnosis and identification of the development of the detected pulmonary nodules.
Pattern Recognition.
