Computerized classification of suspicious regions in chest radiographs using subregion Hotelling observers

Virtual clinical trials in medical imaging: a review

The accelerating complexity and variety of medical imaging devices and methods have outpaced the ability to evaluate and optimize their design and clinical use. This is a significant and increasing challenge for both scientific investigations and clinical applications. Evaluations would ideally be done using clinical imaging trials. These experiments, however, are often not practical due to ethical limitations, expense, time requirements, or lack of ground truth. Virtual clinical trials (VCTs) (also known as in silico imaging trials or virtual imaging trials) offer an alternative means to efficiently evaluate medical imaging technologies virtually. They do so by simulating the patients, imaging systems, and interpreters. The field of VCTs has been constantly advanced over the past decades in multiple areas. We summarize the major developments and current status of the field of VCTs in medical imaging. We review the core components of a VCT: computational phantoms, simulators of different imaging modalities, and interpretation models. We also highlight some of the applications of VCTs across various imaging modalities.

Radiation dose reduction and static image quality assessment using a channelized hotelling observer on an angiography system upgraded with clarity IQ

The goal of this paper was the comparison of radiation dose and imaging quality before and after the Clarity IQ technology installation in a Philips AlluraXper FD20/20 angiography system using a Channelized Hotelling Observer model (CHO). The core characteristics of the Allura Clarity IQ technology are its real-time noise reduction algorithms (NRT) combined with state-of-the-art hardware; this technology allows to implement acquisition protocols able to significantly reduce patient entrance dose. To measure the system performances in terms of image quality we used a contrast detail phantom in a clinical scatter condition. A Leeds TO10 phantom has been imaged between two 10 cm thick homogeneous solid water slabs. Fluoroscopy images were acquired using a cerebral protocol at 3 dose levels (low, medium and high) with a field- of view (FOV) of 31 cm. Cineangiography images were acquired using a cerebral protocol at 2 fps. Thus, 4 acquisitions were obtained for the conventional technology and 4 acquisitions were taken after the Clarity IQ upgrade, for a total of 8 different image sets. A validated 40 Gabor channels CHO with an internal noise model compared the image sets. Human observers' studies were carried out to tune the internal noise parameter. We showed that the CHO did not detect any significant difference between any of the image sets acquired using the two technologies. Consequently, this x-ray imaging technology provides a non-inferior image quality with an average patient dose reduction of 57% and 28% respectively in cineangiography and fluoroscopy. The Clarity IQ installation has certainly allowed a considerable improvement in patient and staff safety, while maintaining the same image quality.

Implementation of a channelized Hotelling observer model to assess image quality of x-ray angiography systems

Evaluation of flat-panel angiography equipment through conventional image quality metrics is limited by the scope of standard spatial-domain image quality metric(s), such as contrast-to-noise ratio and spatial resolution, or by restricted access to appropriate data to calculate Fourier domain measurements, such as modulation transfer function, noise power spectrum, and detective quantum efficiency. Observer models have been shown capable of overcoming these limitations and are able to comprehensively evaluate medical-imaging systems. We present a spatial domain-based channelized Hotelling observer model to calculate the detectability index (DI) of our different sized disks and compare the performance of different imaging conditions and angiography systems. When appropriate, changes in DIs were compared to expectations based on the classical Rose model of signal detection to assess linearity of the model with quantum signal-to-noise ratio (SNR) theory. For these experiments, the estimated uncertainty of the DIs was less than 3%, allowing for precise comparison of imaging systems or conditions. For most experimental variables, DI changes were linear with expectations based on quantum SNR theory. DIs calculated for the smallest objects demonstrated nonlinearity with quantum SNR theory due to system blur. Two angiography systems with different detector element sizes were shown to perform similarly across the majority of the detection tasks.

A novel computer-aided lung nodule detection system for CT images

PURPOSE

The paper presents a complete computer-aided detection (CAD) system for the detection of lung nodules in computed tomography images. A new mixed feature selection and classification methodology is applied for the first time on a difficult medical image analysis problem.

METHODS

The CAD system was trained and tested on images from the publicly available Lung Image Database Consortium (LIDC) on the National Cancer Institute website. The detection stage of the system consists of a nodule segmentation method based on nodule and vessel enhancement filters and a computed divergence feature to locate the centers of the nodule clusters. In the subsequent classification stage, invariant features, defined on a gauge coordinates system, are used to differentiate between real nodules and some forms of blood vessels that are easily generating false positive detections. The performance of the novel feature-selective classifier based on genetic algorithms and artificial neural networks (ANNs) is compared with that of two other established classifiers, namely, support vector machines (SVMs) and fixed-topology neural networks. A set of 235 randomly selected cases from the LIDC database was used to train the CAD system. The system has been tested on 125 independent cases from the LIDC database.

RESULTS

The overall performance of the fixed-topology ANN classifier slightly exceeds that of the other classifiers, provided the number of internal ANN nodes is chosen well. Making educated guesses about the number of internal ANN nodes is not needed in the new feature-selective classifier, and therefore this classifier remains interesting due to its flexibility and adaptability to the complexity of the classification problem to be solved. Our fixed-topology ANN classifier with 11 hidden nodes reaches a detection sensitivity of 87.5% with an average of four false positives per scan, for nodules with diameter greater than or equal to 3 mm. Analysis of the false positive items reveals that a considerable proportion (18%) of them are smaller nodules, less than 3 mm in diameter.

CONCLUSIONS

A complete CAD system incorporating novel features is presented, and its performance with three separate classifiers is compared and analyzed. The overall performance of our CAD system equipped with any of the three classifiers is well with respect to other methods described in literature.

Projection profile analysis for automated detection of abnormalities in chest radiographs

Abnormalities in chest images often present as abnormal opacity or abnormal asymmetry. We have developed a novel method for automated detection of abnormalities in chest radiographs by use of these features. Our method is based on an analysis of the projection profile obtained by projecting the pixels data of a frontal chest image on to the mediolateral axis. Two indices, lung opacity index and lung symmetry index, are computed from the projection profile. Lung opacity index and lung symmetry index are then combined to detect gross abnormalities in chest radiographs. The values of lung opacity index are found to be 0.38 +/- 0.05 and 0.37 +/- 0.06 for normal right and left lung, respectively. The values of lung symmetry index are found to be 0.018 +/- 0.014 for normal chest images. The discrimination for the combination of the two indices is evaluated by linear discriminant analysis and receiver operating characteristic (ROC) analysis. Area Az under the ROC curve with the combination of the two indices in the classification of normal and abnormal chest images is 0.963.

Computer aided detection of masses in mammography using subregion Hotelling observers

We propose to investigate the use of the subregion Hotelling observer for the basis of a computer aided detection scheme for masses in mammography. A database of 1320 regions of interest (ROIs) was selected from the DDSM database collected by the University of South Florida using the Lumisys scanner cases. The breakdown of the cases was as follows: 656 normal ROIs, 307 benign ROIs, and 357 cancer ROIs. Each ROI was extracted at a size of 1024 x 1024 pixels and sub-sampled to 128 x 128 pixels. For the detection task, cancer and benign cases were considered positive and normal was considered negative. All positive cases had the lesion centered in the ROI. We chose to investigate the subregion Hotelling observer as a classifier to detect masses. The Hotelling observer incorporates information about the signal, the background, and the noise correlation for prediction of positive and negative and is the optimal detector when these are known. For our study, 225 subregion Hotelling observers were set up in a 15 x 15 grid across the center of the ROIs. Each separate observer was designed to "observe," or discriminate, an 8 x 8 pixel area of the image. A leave one out training and testing methodology was used to generate 225 "features," where each feature is the output of the individual observers. The 225 features derived from separate Hotelling observers were then narrowed down by using forward searching linear discriminants (LDs). The reduced set of features was then analyzed using an additional LD with receiver operating characteristic (ROC) analysis. The 225 Hotelling observer features were searched by the forward searching LD, which selected a subset of 37 features. This subset of 37 features was then analyzed using an additional LD, which gave a ROC area under the curve of 0.9412 +/- 0.006 and a partial area of 0.6728. Additionally, at 98% sensitivity the overall classifier had a specificity of 55.9% and a positive predictive value of 69.3%. Preliminary results suggest that using subregion Hotelling observers in combination with LDs can provide a strong backbone for a CAD scheme to help radiologists with detection. Such a system could be used in conjunction with CAD systems for false positive reduction.