As new imaging technologies, such as Digital Radiograph (DR), advance, radiologists nowadays are able to detect smaller nodules than before. However, inter-observer variations exhibited in diagnosis still remain as critical challenges that need to be studied and addressed. In this research, inter-observer variation of pulmonary nodule marking and characterizing on DR images was studied in two phases, with the first phase focused on the analysis of inter-observer variations, and the second phase focused on the reduction of variations by using a computer system (IQQA(R)-Chest) that provides intelligent qualitative and quantitative analysis to help radiologists in the softcopy reading of DR chest images. Large inter-observer variations in pulmonary nodule identification and characterization on DR chest images were observed, even between expert radiologists. Experimental results also showed that less experienced radiologists could greatly benefit from the computer assistance, including substantial decrease of inter-observer variation and improvement of nodule detection rates. Moreover, radiologists with different levels of skillfulness may achieve similar high level performance after using the computer system. The computer system showed a high potential for providing a valuable assistance to the examination of DR chest images, especially as DR is adopted to screen large populations for lung cancer.
Multi-slice CT (MSCT) scanners allow nodules as small as 3mm to be identified during screening. However the associated large data sets make it challenging for radiologists to identify all small nodules in a reasonable amount of time. Computer-aided detection may play a critical role in identifying missed nodules. 13 MSCT screening studies, initially interpreted as "non-actionable" by a radiologist, were selected from participants in a lung cancer screening study. The study protocol defines "actionable" studies as those containing at least 1 solid non-calcified nodule larger than 3mm, for which follow-up studies are recommended to exclude interval growth. An automatic detection algorithm was applied to the 13 studies to determine whether it might detect missed nodules, and whether any of these were of sufficient size to be considered "actionable". There were a total of 138 automatically detected candidate nodules, an average of 10.6 per patient. 83 candidates were characterized as true positives, yielding a positive predictive value of 60.1%. 10 automatically detected candidates were judged to be actionable nodules greater than 3mm in diameter. 6 of 13 (46%) patients had at least one "actionable" finding detected by the computer that had been overlooked in the initial exam.
In this paper, we present a method to correct for intensity artifacts in mosaic composition of Computed Radiography (CR) images. The white band artifacts not only distort diagnostic information, but also cause visual disturbances in the examination by physicians. We propose a hybrid method to enhance the image intensity and to correct the brightness differences. A nonlinear transformation method is presented for enhancement, whereas a linear regression method is utilized to compensate for the intensity differences between the white band and normal exposure regions. A knowledge-based method is proposed which can autonomously decide whether the nonlinear enhancement step needs to be bypassed, since in some cases over-enhancement may result from the correction algorithm. Experimental results with different images are presented to show the effectiveness of the proposed method.
In this paper we propose an appearance-based method to heart detection by principal component analysis (PCA). In contrast to conventional methods of PCA-based training, there is no brightness and contrast normalization since such normalization is usually based on maximum and minimum intensity values and is very sensitive to noises. We propose to integrate the normalization procedure into the detection phase. This is achieved by projecting the intensity-transformed image (with unknown scale and shift parameters) onto the eigen-images and minimizing the error of fit. This leads to a set of equations on both the intensity transformation parameters and the projection coefficients. By using the least-squares method, these equations can be easily solved for the scale and shift parameters. After an initial detection of heart positions is conducted, robust fitting of the heart trajectory is used to correct any detection errors. Besides, we also propose an eigen-image re-orthonormalization method for multiple resolution detection without extra training on multiple scales.
Multi-slice computed tomography (CT) provides a promising technology for lung cancer detection and treatment. To optimize automatic detections of a more complete spectrum of lung nodules on CT requires multiple specialized algorithms in a coherently integrated detection system. We have developed a knowledge-based system for automatic lung nodule detection and analysis, which coherently integrates several robust novel detection algorithms to detect different types of nodules, including those attached to the chest wall, nodules adjacent to or fed by vessels, and solitary nodules, simultaneously. The system architecture can be easily extended in the future to include a still greater range of nodule types, most importantly so-called ground-glass opacities (GGOs). In addition, automatic local adaptive histogram analysis, dynamic cross-correlation analysis, and the automatic volume projection analysis by using by data dimension reduction method, are used in nodule detection. The proposed system has been applied to 10 patients screened with low-dose multi-slice CT. Preliminary clinical tests show that (1) the false positive rate averages about 3.2 per study; and (2) by using the system radiologists are able to detect nearly twice the number of nodules as compared with working alone.
In the single-photon emission computed tomography (SPECT), it is highly desirable to provide physicians with a measure of the strength of the liver-heart cross talk as a means of assessing the quality of the images, so that appropriate actions can be taken to avoid false diagnosis. Liver-heart cross talk is an phenomenon in which the liver count interferes with the heart count in 3D reconstruction, which generates artifacts in the reconstructed images. In this paper, we propose an automatic method for quantification of such liver-heart cross talk. The system performs heart detection followed by non-heart organ segmentation and quantification of their activities. An appearance-based approach is applied to find the heart center in each image, with invariance to image intensity and contrast. Then heart and non-heart activities are quantified in each image. A measurement formula is proposed to compute the amount of liver-heart cross talk as a function of the size of the non-heart activity regions, of the strengths of the heart and non-heart activities, and of the distance of the non-heart regions to the heart. The method has been tested on 150 patient studies of different isotopes and acquisition types, with very promising results.
We propose in this paper a novel approach to the automatic segmentation of lung nodules in a given volume of interest (VOI) from high resolution multi-slice CT images by dynamically initializing and adjusting a 3D template and analyzing its cross correlation with the structure of interest. First, thresholding techniques are used to separate the background voxels. The structure of interest, comprising of a nodule candidate and possible attached vessels, is then extracted by excluding any part of the chest wall inside the VOI. Afterwards, the proposed segmentation method finds the core of the structure of interest, which corresponds to the nodule, analyzes its orientation and size, and initializes a 3D template accordingly. Next, The template gradually expands, with its cross correlation to the original structure of interest being computed at each step. The template is then optimized based on the analysis of the cross correlation curve. A segmentation of the nodule is first roughly obtained by doing an 'AND' operation between the optimal template and the extracted structure and then refined by a spatial reasoning method. Template parameters can be recorded and recalled in later diagnosis so that reproducibility and consistency can be achieved. Preliminary results show that segmentation results are consistent, with a mean intra-scan volume measurement deviation of 2.8% for phantom data and 8.1% for real patient data.
With low dose multi-slice CT for screening of lung cancer, physicians are now finding and examining increasingly smaller nodules. However as the size of detectable nodules becomes smaller, there may be greater differences among physicians as to what is detected and what constitutes a nodule. In this study, 10 CT screening studies of smokers were individually evaluated by three thoracic radiologists. After consensus to determine a gold standard, the number of nodules detected by individual radiologists ranged from 1.4 to 2.1 detections per patient. Each radiologist detected nodules missed by the other two. Although a total of 26 true nodules were detected by one or more radiologists, only 8 (31%) were detected by all three radiologists. The number of true nodules detected by an integrated automatic detection algorithm was 3.2 per patient after radiologist validation. Including these nodules in the gold standard set reduced the sensitivity of nodule detection by each radiologist to less than half. The sensitivity of nodule detection by the computer was better at 64%, proving especially efficacious for detecting smaller and more central nodules. Use of the automatic detection module would allow individual radiologists to increase the number of detected nodules by 114% to 207%.
KEYWORDS: Spine, X-ray imaging, X-rays, Data compression, Radiography, Visualization, Image compression, Silicon, Information operations, Medical research
Endplates and pedicles are important anatomies for deformity analysis of spines in radiographs. The first part of the paper presents an evidence-reasoning approach to endplate detection. Multiple pieces of local visual evidence about the presence of an endplate at an image point are first computed. They are then combined with some prior knowledge about vertebra shape to arrive at a consistent and robust detection. In the second part, the paper presents a learning-based method for pedicle detection. Variations in pedicle shapes are learned automatically. Data compression techniques are used to both reduce the data dimension for a fast training and detection, and to enable a multi-scale search without multi-scale training. 15
This paper proposes a novel, fully automatic method for composing mosaic images in Computed Radiography. The method combines the detection of white-band edges with a cross- correlation technique. The white-band edges are positions of overlap lines. Several new kinds of measurements are proposed to evaluate the likelihood of a position to be a white-band edge. Multiple checks are used to reject less likely candidates. An error measure is defined for picking up the most likely candidates. The white-band candidate positions are fed to a cross-correlation method to compute the final alignment parameters for mosaic composition.
We describe in this paper a novel, efficient method to automatically detect lung nodules from low-dose, high- resolution CT (HRCT) images taken with a multi-slice scanner. First, the program identifies initial anatomical seeds, including lung nodule candidates, airways, vessels, and other features that appear as bright opacities in CT images. Next, a 3D region growing method is applied to each seed. The thresholds for segmentation are adaptively adjusted based upon automatic analysis of the local histogram. Once an object has been examined, vessels and other non-nodule objects are quickly excluded from future study, thus saving computation time. Finally, extracted 3D objects are classified a nodule candidates or non-nodule structures. Anatomical knowledge and multiple measurements, such as volume and sphericity, are used to categorize each object. The detected nodules are presented to the user for examination and verification. The proposed method was applied to 14 low dose HRCT patient studies. Since the CT images were taken with a multi-slice scanner, the average number of slices per study was 292. In every case the x-ray exposure was about 20 mAs, a suitable dosage for screening. In our preliminary results, the method detected an average of 8 nodules per study, with an average size of 3.3 mm in diameter.
Body motion and heart upward creep are among the most frequent sources of artifacts in the single-photon emission computed tomography (SPECT) of nuclear medicine. This paper provides a new method for automatic correction of such motions. Under the formulation of a variable length correlation, abrupt body motions, gradual body motions, and heart upward creep are corrected in sequential passes. An affine transformation is used to compensate for changes in heart appearance caused by varying view angles in image acquisition. Additionally, a method is proposed for automatic exclusion of non-cardiac organs. The effectiveness of the method has been demonstrated with experimental results.
We have developed a new algorithm for determining the cross- section range of the heart from a sequence of SPECT projection images. The new algorithm provides accurate estimation of the heart range for a fully automatic myocardial perfusion SPECT processing system. The limits of the heart range are used for reconstructing transverse images for the subsequent analysis. The basis of the approach is the 1D pseudo motion analysis which has three major components, spatial feature to position mapping, knowledge-driven analysis of heart region, and heart range determination. The main advantage of the algorithm is that the processing is fully automatic regarding no user intervention and is less sensitive to the image intensity distribution comparing to other existing methods.
We have developed a new algorithm that is capable of combining a sequence of peripheral angiographic images into a long-leg display automatically. In peripheral angiography, the field-of-view of the scanner cannot cover the entire peripheral region in one image. Instead, the peripheral region is divided into subregions and scanner steps to each of the location and acquires images. The adjacent stepping images overlap partially with each other during the image acquisition. Therefore, the problem for reconstructing a long-leg image display is transformed into finding the best image-matching in each adjacent image pair. The new algorithm solves this matching problem by maximizing the 'overlapping ratio' for global matching in each designate image pair. The 'overlapping ratio' is defined as the degree of feature agreement, in the overlapping area calculated from anatomical information of bone and vessels, between two adjacent image pairs. The experimental results indicate that he new approach is robust and generates accurate and reliable image matching. In addition, each leg is fine-tuned to determine the individual matching parameters to compensate the possible leg motion. Based on the matching parameters, we generate long-leg image display in both reduced and full resolutions so that cross reference of region-of-interest can be done interactively.
The goal of this work is to provide a powerful computer- aided-perception tool for physicians to visualize low- contrast blood vessel structures with exquisite details and hence to facilitate the extraction of valuable diagnostic information from angiographic images. In x-ray angiography, blood vessels often exhibit low intensity contrast with respect to their surrounding soft tissues. The problem is particularly severe for fine vessel structures. A major challenge for enhancement is the ability to emphasize vessel structures without creating artifacts such as edge overshot and noise magnification. In this work, a multi-scale adaptive contrast enhancement algorithm is developed. A pyramid of intensity images is generated using wavelet decomposition. At each pyramid level, an enhancement mask is computed which captures the fine vessel structures in the image at that scale. To generate this mask, we first compute directional sensitive Laplacian which is capable of extracting fine lines with very low contrast to its surroundings. An adaptive non-linear weighting function is then applied to the Laplacian to form an enhancement mask. The non-linearity is crucial for virtually eliminating edge overshots. These masks are then combined recursively to form a single composite mask of full resolution. Finally, the enhanced image is obtained by adding this composite mask to the original image. Extensive testing demonstrates remarkable contrast improvement in blood vessels without noticeable artifacts.
Diagnostic medical imaging often contains variations of patient anatomies, camera mispositioning, or other imperfect imaging condiitons. These variations contribute to uncertainty about shapes and boundaries of objects in images. As the results sometimes image features, such as traditional edges, may not be identified reliably and completely. We describe a knowledge based system that is able to reason about such uncertainties and use partial and locally ambiguous information to infer about shapes and lcoation of objects in an image. The system uses directional topographic features (DTFS), such as ridges and valleys, labeled from the underlying intensity surface to correlate to the intrinsic anatomical information. By using domain specific knowledge, the reasoning system can deduce significant anatomical landmarks based upon these DTFS, and can cope with uncertainties and fill in missing information. A succession of levels of representation for visual information and an active process of uncertain reasoning about this visual information are employed to realiably achieve the goal of image analysis. These landmarks can then be used in localization of anatomy of interest, image registration, or other clinical processing. The successful application of this system to a large set of planar cardiac images of nuclear medicine studies has demonstrated its efficiency and accuracy.
Unlike MSS LANDSAT imagery and other photography the specific characteristics of the intensity of water and shadow in an SAlt image make the task of discriminating them extremely difficult. In this paper we present a scene analysis system for automatically identifying the water regions and shadow regions whose differences appear as subtle differences in tone and texture. In the preprocessing a region dependent Multi-threshold Adaptive Filter (MTAF) is proposed for texture preserving noise removal. In the low-level labeling a probabilistic relaxation algorithm with dynamic adaptive compatibility coefficients is provided to extract the initial object regions. In the high-level interpretation a relational graph model based on the knowledge of water and shadow regions on SAR imagery is constructed. Then spatial reasoning is carried out according to this relational model. The contextual information from different processing modules either at the pixel level or region level is consistently combined to reduce the labeling ambiguities. The experiments shown that more than 85 of shadow regions and water regions on a set of four SAR images can be identified correctly by this system. 1.
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