[show abstract][hide abstract] ABSTRACT: This paper proposes a stratified regularity measure: a novel entropic measure to describe data regularity as a function of data domain stratification. Jensen-Shannon divergence is used to compute a set-similarity of intensity distributions derived from stratified data. We prove that derived regularity measures form a continuum as a function of the stratificationpsilas granularity and also upper-bounded by the Shannon entropy. This enables to interpret it as a generalized Shannon entropy with an intuitive spatial parameterization. This measure is applied as a novel feature extraction method for a real-world medical image analysis problem. The proposed measure is employed to describe ground-glass lung nodules whose shape and intensity distribution tend to be more irregular than typical lung nodules. Derived descriptors are then incorporated into a machine learning-based computer-aided detection system. Our ROC experiment resulted in 83% success rate with 5 false positives per patient, demonstrating an advantage of our approach toward solving this clinically significant problem.
Computer Vision and Pattern Recognition Workshops, 2008. CVPR Workshops 2008. IEEE Computer Society Conference on; 07/2008
[show abstract][hide abstract] ABSTRACT: A method of extracting a spinal cord from a digitized medical image includes providing a digitized medical image, selecting a set of points from said image as candidates for belonging to the spine, initializing a probability for each candidate point to belong to said spine, minimizing a weighted sum of square differences of image intensities of said candidate points and intensities determined by a mathematical model of said spine to estimate parameter values for said model, calculating a residual error for each point from the differences at each point between an estimated image intensity calculated from said estimated model parameters and an actual image intensity, updating the candidate point probabilities from said residual errors, and eliminating candidate points whose probability falls below a predetermined value.
[show abstract][hide abstract] ABSTRACT: We have developed a general-purpose registration algorithm for medical images and volumes. The transformation between images is modeled as locally affine but globally smooth, and explicitly accounts for local and global variations in image intensities. An explicit model of missing data is also incorporated, allowing us to simultaneously segment and register images with partial or missing data. The algorithm is built upon a differential multiscale framework and incorporates the expectation maximization algorithm. We show that this approach is highly effective in registering a range of synthetic and clinical medical images.
Medical Image Analysis 07/2006; 10(3):452-64. · 4.09 Impact Factor
[show abstract][hide abstract] ABSTRACT: A novel approach for generating a set of features derived from properties of patterns of curvature is introduced as a part of a computer aided colonic polyp detection system. The resulting sensitivity was 84% with 4.8 false positives per volume on an independent test set of 72 patients (56 polyps). When used in conjunction with other features, it allowed the detection system to reach an overall sensitivity of 94% with a false positive rate of 4.3 per volume.
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 02/2006; 9(Pt 2):169-76.
[show abstract][hide abstract] ABSTRACT: This paper describes a novel classification method for com- puter aided detection (CAD) that identifies structures of in- terest from medical images. CAD problems are challenging largely due to the following three characteristics. Typical CAD training data sets are large and extremely unbalanced between positive and negative classes. When searching for descriptive features, researchers often deploy a large set of experimental features, which consequently introduces irrel- evant and redundant features. Finally, a CAD system has to satisfy stringent real-time requirements. This work is distinguished by three key contributions. The first is a cascade classification approach which is able to tackle all the above difficulties in a unified framework by employing an asymmetric cascade of sparse classifiers each trained to achieve high detection sensitivity and satisfac- tory false positive rates. The second is the incorporation of feature computational costs in a linear program formu- lation that allows the feature selection process to take into account different evaluation costs of various features. The third is a boosting algorithm derived from column genera- tion optimization to effectively solve the proposed cascade linear programs. We apply the proposed approach to the problem of detect- ing lung nodules from helical multi-slice CT images. Our approach demonstrates superior performance in comparison against support vector machines, linear discriminant analy- sis and cascade AdaBoost. Especially, the resulting detec- tion system is significantly sped up with our approach.
Proceedings of the Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Philadelphia, PA, USA, August 20-23, 2006; 01/2006
[show abstract][hide abstract] ABSTRACT: Colorectal cancer is the third most common cancer in both men and women. It is estimated that in 2004, nearly 147,000 cases of colon and rectal cancer will be diagnosed in the USA, and approximately 57,000 people would die from the disease; however, only 44% of the eligible population undergoes any type of colorectal cancer screening. Many reasons have been identified for non-compliance, with key ones being patient comfort, bowel preparation and cost. Virtual colonoscopy derived from computed tomography (CT) images is gaining broader acceptance as a screening method for colorectal neoplasia. Our research suggests that computer-aided detection (CAD) as a second reader has great potential in improving polyp detection. The ColonCAD prototype presented in this paper was developed and tested on cases representative of the variability and quality in true clinical practice. Results of this study with 150 patients demonstrate that: the developed algorithm generalises well: the sensitivity for polyps > or = 6 mm is on average 90%; and the median false positive rate is a manageable 3 per volume.
British Journal of Radiology 02/2005; 78 Spec No 1:S57-62. · 1.22 Impact Factor
[show abstract][hide abstract] ABSTRACT: Early detection of lung nodules is an important clinical indication for obtaining routine CT studies of the thorax. To date, research has mainly focused on the sensitivity of CAD compared with expert chest radiologists using data obtained from single or multi-detector CT scanners. But, beside sensitivity and specificity it is also import to know how well the CAD system does perform on datasets from different sites with different slice thicknesses and differences in the dosage. The present study focuses on the generalization ability of a prototype CAD system that is not yet commercially available. It describes the architecture of a recent CAD system and assesses the performance on a heterogeneous dataset collected from multiple geographically diverse sites.
International Congress Series 01/2005; 1281:1104-1108.
[show abstract][hide abstract] ABSTRACT: Colorectal cancer is the third most common cancer in both men and women and it was estimated that in 2003 nearly 150,000 cases would be diagnosed and 57,000 people would die. Screening has been accepted as a means for early detection of the disease, yet only a portion of the eligible population undergoes colorectal cancer screening, partially due to comfort issues of undergoing a full colonoscopic examination. Virtual colonoscopy (VC) has been demonstrated to be an effective means of performing screening and given the number of people who are candidate for screening better tools, such as Computer Aided Detection (CAD), are required to fulfill this increasing need. The developed CAD system, presented in this paper, has focused on the detection of polyps of sizes up to and including 20 mm. The results have demonstrated that: the developed algorithm generalizes, the sensitivity and specificity for middle- to large-sized polyps is on the average 95% while the overall sensitivity is roughly 88% and the false positive has remained at a manageable 4 per volume.
CARS 2004. Computer Assisted Radiology and Surgery. Proceedings of the 18th International Congress and Exhibition, Chicago, USA, June 23-26, 2004; 01/2004
[show abstract][hide abstract] ABSTRACT: We have developed a general-purpose registration algorithm for medical images and volumes. This method models the transformation between images as locally affine but globally smooth. The model also explicitly accounts for local and global variations in image intensities. This approach is built upon a differential multiscale framework, allowing us to capture both large- and small-scale transformations. We show that this approach is highly effective across a broad range of synthetic and clinical medical images.
IEEE Transactions on Medical Imaging 08/2003; 22(7):865-74. · 4.03 Impact Factor
[show abstract][hide abstract] ABSTRACT: We have developed a general purpose registration algorithm for medical images and volumes. The transformation between images is modelled as locally a#ne but globally smooth, and explicitly accounts for local and global variations in image intensities. An explicit model of missing data is also incorporated, allowing us to simultaneously segment and register images with partial or missing data. The algorithm is built upon a di#erential multiscale framework and incorporates the expectation maximization algorithm. We show that this approach is highly e#ective in registering a range of synthetic and clinical medical images.
[show abstract][hide abstract] ABSTRACT: Introduction There are a variety of methods for medical image registration (see [9, 10, 26, 19, 17] for general surveys) . Differential registration techniques, however, are often cited as being ineffective, and as such have received little attention (exceptions include [4, 16, 20, 21]). This is unfortunate as differential motion techniques have been quite effective in the Computer Vision community (e.g., [15, 18, 25, 1, 14, 2, 13, 5, 12, 6, 7, 24]). Here we present an effective technique for elastic image registration built upon a differential framework. This technique models the mapping between images as a locally affine but globally smooth warp, and explicitly accounts for variations in image intensities. The resulting registration is simple and automatic. Results from several synthetic and clinical images are shown. 2. Methods We formulate the problem of image registration between a source and target image within a differential (non-feature based) framework. This formulation
[show abstract][hide abstract] ABSTRACT: We have applied techniques from differential motion estimation in the context of automatic registration of medical images. This method uses optical-flow and Fourier techniques for local/global registration. A six parameter affine model is used to estimate shear, rotation, scale and translation. We show the efficacy of this method with images of similar and different contrasts.
[show abstract][hide abstract] ABSTRACT: We present an automatic, multi-resolution correlation based approach for elastic image registration. The technique presented assumes no a priori information (such as landmarks or segmentation), which makes it suitable for a wide class of image registration tasks. We also present preliminary results of the technique on a variety of images.
[show abstract][hide abstract] ABSTRACT: We have developed a multiscale algorithm for elastic registration of images. Rigid registration has many applications but it is often limited by distortions in the images. For example, different views of the same object produce distortions. Common examples of slightly different views producing a distortion can be found in medical imaging, such as matching a current mammogram or chest radiograph with one from a previous year, and in remote sensing, such as matching images taken from different satellite positions. We have developed two methods of elastic registration. Both are multiscale but one used an iterative minimization of the local error and the other uses a windowed correlation. We present preliminary results of the elastic registration method used on windowed correlations.
Journal of Digital Imaging 09/1998; 11(3 Suppl 1):59-65. · 1.10 Impact Factor
[show abstract][hide abstract] ABSTRACT: To register two images means to align them so that common features overlap and dierences—for example, a tumor that has grown— are readily apparent. Being able to easily spot dierences between two images is obviously very important in applications. This paper is an intro- duction to image registration as applied to medical imaging. We first define image registration, breaking the problem down into its constituent compo- nent. We then discuss various techniques, reflecting dierent choices that can be made in developing an image registration technique. We conclude with a brief discussion.