Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images

A fully automatic method for lung parenchyma segmentation and repairing

Considering that the traditional lung segmentation algorithms are not adaptive for the situations that most of the juxtapleural nodules, which are excluded as fat, and lung are not segmented perfectly. In this paper, several methods are comprehensively utilized including optimal iterative threshold, three-dimensional connectivity labeling, three-dimensional region growing for the initial segmentation of the lung parenchyma, based on improved chain code, and Bresenham algorithms to repair the lung parenchyma. The paper thus proposes a fully automatic method for lung parenchyma segmentation and repairing. Ninety-seven lung nodule thoracic computed tomography scans and 25 juxtapleural nodule scans are used to test the proposed method and compare with the most-cited rolling-ball method. Experimental results show that the algorithm can segment lung parenchyma region automatically and accurately. The sensitivity of juxtapleural nodule inclusion is 100 %, the segmentation accuracy of juxtapleural nodule regions is 98.6 %, segmentation accuracy of lung parenchyma is more than 95.2 %, and the average segmentation time is 0.67 s/frame. The algorithm can achieve good results for lung parenchyma segmentation and repairing in various cases that nodules/tumors adhere to lung wall.

Efficient PET-CT image retrieval using graphs embedded into a vector space

Combined positron emission tomography and computed tomography (PET-CT) produces functional data (from PET) in relation to anatomical context (from CT) and it has made a major contribution to improved cancer diagnosis, tumour localisation, and staging. The ability to retrieve PET-CT images from large archives has potential applications in diagnosis, education, and research. PET-CT image retrieval requires the consideration of modality-specific 3D image features and spatial contextual relationships between features in both modalities. Graph-based retrieval methods have recently been applied to represent contextual relationships during PET-CT image retrieval. However, accurate methods are computationally complex, often requiring offline processing, and are unable to retrieve images at interactive rates. In this paper, we propose a method for PET-CT image retrieval using a vector space embedding of graph descriptors. Our method defines the vector space in terms of the distance between a graph representing a PET-CT image and a set of fixed-sized prototype graphs; each vector component measures the dissimilarity of the graph and a prototype. Our evaluation shows that our method is significantly faster (≈800× speedup, p <; 0.05) than retrieval using the graph-edit distance while maintaining comparable precision (5% difference, p > 0.05).

Multi-stage thresholded region classification for whole-body PET-CT lymphoma studies

Positron emission tomography computed tomography (PET-CT) is the preferred imaging modality for the evaluation of the lymphomas. Disease involvement in the lymphomas usually appear as foci of increased Fluorodeoxyglucose (FDG) uptake. Thresholding methods are applied to separate different regions of involvement. However, the main limitation of thresholding is that it also includes regions where there is normal FDG excretion and FDG uptake (NEUR) in structures such as the brain, bladder, heart and kidneys. We refer to these regions as NEURs (the normal excretion and uptake (of FDG) regions). NEURs can make image interpretation problematic. The ability to identify and label NEURs and separate them from abnormal regions is an important process that could improve the sensitivity of lesion detection and image interpretation. In this study, we propose a new method to automatically separate NEURs in thresholded PET images. We propose to group thresholded regions of the same structure with spatial and texture based clustering; we then classified NEURs on PET-CT contextual features. Our findings were that our approach had better accuracy when compared to conventional methods.

Automated Lung Segmentation and Image Quality Assessment for Clinical 3D/4D Computed Tomography

Four-dimensional computed tomography (4DCT) provides not only a new dimension of patient-specific information for radiation therapy planning and treatment but also a challenging scale of data volume to process and analyze. Manual analysis using existing 3D tools is unable to keep up with vastly increased 4D data volume, automated processing and analysis are thus needed to process 4DCT data effectively and efficiently. In this work, we applied ideas and algorithms from image/signal processing, computer vision and machine learning to 4DCT lung data so that lungs can be reliably segmented in a fully-automated manner, lung features can be visualized and measured on-the-fly via user interactions, and data quality classifications can be computed in a robust manner. Comparisons of our results with an established treatment planning system and calculation by experts demonstrated negligible discrepancies (within ±2%) for volume assessment but one to two orders of magnitude performance enhancement. An empirical Fourier-analysis-based quality measure delivered performances closely emulating human experts. Three machine learners are inspected to justify the viability of machine learning techniques used to robustly identify data quality of 4DCT images in the scalable manner. The resultant system provides tools that speeds up 4D tasks in the clinic and facilitates clinical research to improve current clinical practice.

Tissue Cancellation in Dual Energy Mammography Using a Calibration Phantom Customized for Direct Mapping

An easily implementable tissue cancellation method for dual energy mammography is proposed to reduce anatomical noise and enhance lesion visibility. For dual energy calibration, the images of an imaging object are directly mapped onto the images of a customized calibration phantom. Each pixel pair of the low and high energy images of the imaging object was compared to pixel pairs of the low and high energy images of the calibration phantom. The correspondence was measured by absolute difference between the pixel values of imaged object and those of the calibration phantom. Then the closest pixel pair of the calibration phantom images is marked and selected. After the calibration using direct mapping, the regions with lesion yielded different thickness from the background tissues. Taking advantage of the different thickness, the visibility of cancerous lesions was enhanced with increased contrast-to-noise ratio, depending on the size of lesion and breast thickness. However, some tissues near the edge of imaged object still remained after tissue cancellation. These remaining residuals seem to occur due to the heel effect, scattering, nonparallel X-ray beam geometry and Poisson distribution of photons. To improve its performance further, scattering and the heel effect should be compensated.

Relationships between airflow obstruction and quantitative CT measurements of emphysema, air trapping, and airways in subjects with and without chronic obstructive pulmonary disease

OBJECTIVE

This study evaluates the relationships between quantitative CT (QCT) and spirometric measurements of disease severity in cigarette smokers with and without chronic obstructive pulmonary disease (COPD).

MATERIALS AND METHODS

Inspiratory and expiratory CT scans of 4062 subjects in the Genetic Epidemiology of COPD (COPDGene) Study were evaluated. Measures examined included emphysema, defined as the percentage of low-attenuation areas≤-950 HU on inspiratory CT, which we refer to as "LAA-950I"; air trapping, defined as the percentage of low-attenuation areas≤-856 HU on expiratory CT, which we refer to as "LAA-856E"; and the inner diameter, inner and outer areas, wall area, airway wall thickness, and square root of the wall area of a hypothetical airway of 10-mm internal perimeter of segmental and subsegmental airways. Correlations were determined between spirometry and several QCT measures using statistics software (SAS, version 9.2).

RESULTS

QCT measurements of low-attenuation areas correlate strongly and significantly (p<0.0001) with spirometry. The correlation between LAA-856E and forced expiratory volume in 1 second (FEV1) and the ratio of FEV1 to forced vital capacity (FVC) (r=-0.77 and -0.84, respectively) is stronger than the correlation between LAA-950I and FEV1 and FEV1/FVC (r=-0.67 and r=-0.76). Inspiratory and expiratory volume changes decreased with increasing disease severity, as measured by the Global Initiative for Chronic Obstructive Pulmonary Disease (GOLD) staging system (p<0.0001). When airway variables were included with low-attenuation area measures in a multiple regression model, the model accounted for a statistically greater proportion of variation in FEV1 and FEV1/FVC (R2=0.72 and 0.77, respectively). Airway measurements alone are less correlated with spirometric measures of FEV1 (r=0.15 to -0.44) and FEV1/FVC (r=0.19 to -0.34).

CONCLUSION

QCT measurements are strongly associated with spirometric results showing impairment in smokers. LAA-856E strongly correlates with physiologic measurements of airway obstruction. Airway measurements can be used concurrently with QCT measures of low-attenuation areas to accurately predict lung function.

A graph-based approach for the retrieval of multi-modality medical images

In this paper, we address the retrieval of multi-modality medical volumes, which consist of two different imaging modalities, acquired sequentially, from the same scanner. One such example, positron emission tomography and computed tomography (PET-CT), provides physicians with complementary functional and anatomical features as well as spatial relationships and has led to improved cancer diagnosis, localisation, and staging. The challenge of multi-modality volume retrieval for cancer patients lies in representing the complementary geometric and topologic attributes between tumours and organs. These attributes and relationships, which are used for tumour staging and classification, can be formulated as a graph. It has been demonstrated that graph-based methods have high accuracy for retrieval by spatial similarity. However, naïvely representing all relationships on a complete graph obscures the structure of the tumour-anatomy relationships. We propose a new graph structure derived from complete graphs that structurally constrains the edges connected to tumour vertices based upon the spatial proximity of tumours and organs. This enables retrieval on the basis of tumour localisation. We also present a similarity matching algorithm that accounts for different feature sets for graph elements from different imaging modalities. Our method emphasises the relationships between a tumour and related organs, while still modelling patient-specific anatomical variations. Constraining tumours to related anatomical structures improves the discrimination potential of graphs, making it easier to retrieve similar images based on tumour location. We evaluated our retrieval methodology on a dataset of clinical PET-CT volumes. Our results showed that our method enabled the retrieval of multi-modality images using spatial features. Our graph-based retrieval algorithm achieved a higher precision than several other retrieval techniques: gray-level histograms as well as state-of-the-art methods such as visual words using the scale- invariant feature transform (SIFT) and relational matrices representing the spatial arrangements of objects.

A fast deformable registration method for 4D lung CT in hybrid framework

PURPOSE

A pulmonary respiration model for deformable registration of lung CT for the surgery path planning and surgical navigation is an important, difficult, and time-consuming task. This paper presents a new fast deformable registration method for 4D lung CT in a hybrid framework incorporating point set registration with mutual information registration.

METHOD

The point sets of the lung surface and vessels are automatically extracted. Their displacement vectors are obtained by point set registration. The sum of squared Euclidean distance between the displacement vectors of these point sets and the displacement vectors based on the B-spline transformation model is minimized as a novel similarity measure to derive the rough transformation function. Finally, the rough transformation function is refined by using the mutual information-based registration method. To evaluate the effectiveness of the proposed method, the authors performed registrations on 20 4D lung volume cases from two different CT scanners. The proposed method was compared with the point set-based method, the mutual information-based method, and the ANTS method, which is a state-of-the-art deformable registration technique.

RESULTS

The results show that the landmark distance errors and computation time of the proposed method decreased an average of 5 and 70 %, respectively, when compared to the mutual information-alone-based method. The proposed method results in an average of 28 % lower landmark distance error than registration method based on point sets in spite of increase in computation time. Moreover, compared with ANTS, the computation time of the proposed method is reduced by an average of 93 % in the case of comparable landmark distance errors.

CONCLUSION

The accuracy and speed of the proposed deformable registration method indicate that the method is suitable for use in a clinical image-guided intervention system.

Quantitative computed tomography-derived clusters: redefining airway remodeling in asthmatic patients

BACKGROUND

Asthma heterogeneity is multidimensional and requires additional tools to unravel its complexity. Computed tomography (CT)-assessed proximal airway remodeling and air trapping in asthmatic patients might provide new insights into underlying disease mechanisms.

OBJECTIVES

The aim of this study was to explore novel, quantitative, CT-determined asthma phenotypes.

METHODS

Sixty-five asthmatic patients and 30 healthy subjects underwent detailed clinical, physiologic characterization and quantitative CT analysis. Factor and cluster analysis techniques were used to determine 3 novel, quantitative, CT-based asthma phenotypes.

RESULTS

Patients with severe and mild-to-moderate asthma demonstrated smaller mean right upper lobe apical segmental bronchus (RB1) lumen volume (LV) in comparison with healthy control subjects (272.3 mm(3) [SD, 112.6 mm(3)], 259.0 mm(3) [SD, 53.3 mm(3)], 366.4 mm(3) [SD, 195.3 mm(3)], respectively; P = .007) but no difference in RB1 wall volume (WV). Air trapping measured based on mean lung density expiratory/inspiratory ratio was greater in patients with severe and mild-to-moderate asthma compared with that seen in healthy control subjects (0.861 [SD, 0.05)], 0.866 [SD, 0.07], and 0.830 [SD, 0.06], respectively; P = .04). The fractal dimension of the segmented airway tree was less in asthmatic patients compared with that seen in control subjects (P = .007). Three novel, quantitative, CT-based asthma clusters were identified, all of which demonstrated air trapping. Cluster 1 demonstrates increased RB1 WV and RB1 LV but decreased RB1 percentage WV. On the contrary, cluster 3 subjects have the smallest RB1 WV and LV values but the highest RB1 percentage WV values. There is a lack of proximal airway remodeling in cluster 2 subjects.

CONCLUSIONS

Quantitative CT analysis provides a new perspective in asthma phenotyping, which might prove useful in patient selection for novel therapies.

Data processing and presentation for a personalised, image-driven medical graphical avatar

With the continuing digital revolution in the healthcare industry, patients are being confronted with the difficult task of managing their digital medical data. Current personal health record (PHR) systems are able to store and consolidate this data, but they are limited in providing tools to facilitate patients' understanding and management of the data. One reason for this stems from the limited use of contextual information, especially in presenting spatial details such as in volumetric images and videos, as well as time-based temporal data. Further, lack of meaningful visualisation techniques exist to represent the data stored in PHRs. In this paper we propose a medical graphical avatar (MGA) constructed from whole-body patient images, and a navigable timeline of the patient's medical records. A data mapping framework is presented that extracts information from medical multimedia data such as images, video and text, to populate our PHR timeline, while also embedding spatial and textual annotations such as regions of interest (ROIs) that are automatically derived from image processing algorithms. We developed a prototype to process the various forms of PHR data and present the data in a graphical avatar. We analysed the usefulness of our system under various scenarios of patient data use and present preliminary results that indicate that our system performs well on standard consumer hardware.

Estimating the 4D respiratory lung motion by spatiotemporal registration and super-resolution image reconstruction

PURPOSE

One of the main challenges in lung cancer radiation therapy is how to reduce the treatment margin but accommodate the geometric uncertainty of moving tumor. 4D-CT is able to provide the full range of motion information for the lung and tumor. However, accurate estimation of lung motion with respect to the respiratory phase is difficult due to various challenges in image registration, e.g., motion artifacts and large interslice thickness in 4D-CT. Meanwhile, the temporal coherence across respiration phases is usually not guaranteed in the conventional registration methods which consider each phase image in 4D-CT independently. To address these challenges, the authors present a unified approach to estimate the respiratory lung motion with two iterative steps.

METHODS

First, the authors propose a novel spatiotemporal registration algorithm to align all phase images of 4D-CT (in low-resolution) to a high-resolution group-mean image in the common space. The temporal coherence of registration is maintained by a set of temporal fibers that delineate temporal correspondences across different respiratory phases. Second, a super-resolution technique is utilized to build the high-resolution group-mean image with more anatomical details than any individual phase image, thus largely alleviating the registration uncertainty especially in correspondence detection. In particular, the authors use the concept of sparse representation to keep the group-mean image as sharp as possible.

RESULTS

The performance of our 4D motion estimation method has been extensively evaluated on both the simulated datasets and real lung 4D-CT datasets. In all experiments, our method achieves more accurate and consistent results in lung motion estimation than all other state-of-the-art approaches under comparison.

CONCLUSIONS

The authors have proposed a novel spatiotemporal registration method to estimate the lung motion in 4D-CT. Promising results have been obtained, which indicates the high applicability of our method in clinical lung cancer radiation therapy.

Deformable registration of the inflated and deflated lung in cone-beam CT-guided thoracic surgery: initial investigation of a combined model- and image-driven approach

PURPOSE

Surgical resection is the preferred modality for curative treatment of early stage lung cancer, but localization of small tumors (<10 mm diameter) during surgery presents a major challenge that is likely to increase as more early-stage disease is detected incidentally and in low-dose CT screening. To overcome the difficulty of manual localization (fingers inserted through intercostal ports) and the cost, logistics, and morbidity of preoperative tagging (coil or dye placement under CT-fluoroscopy), the authors propose the use of intraoperative cone-beam CT (CBCT) and deformable image registration to guide targeting of small tumors in video-assisted thoracic surgery (VATS). A novel algorithm is reported for registration of the lung from its inflated state (prior to pleural breach) to the deflated state (during resection) to localize surgical targets and adjacent critical anatomy.

METHODS

The registration approach geometrically resolves images of the inflated and deflated lung using a coarse model-driven stage followed by a finer image-driven stage. The model-driven stage uses image features derived from the lung surfaces and airways: triangular surface meshes are morphed to capture bulk motion; concurrently, the airways generate graph structures from which corresponding nodes are identified. Interpolation of the sparse motion fields computed from the bounding surface and interior airways provides a 3D motion field that coarsely registers the lung and initializes the subsequent image-driven stage. The image-driven stage employs an intensity-corrected, symmetric form of the Demons method. The algorithm was validated over 12 datasets, obtained from porcine specimen experiments emulating CBCT-guided VATS. Geometric accuracy was quantified in terms of target registration error (TRE) in anatomical targets throughout the lung, and normalized cross-correlation. Variations of the algorithm were investigated to study the behavior of the model- and image-driven stages by modifying individual algorithmic steps and examining the effect in comparison to the nominal process.

RESULTS

The combined model- and image-driven registration process demonstrated accuracy consistent with the requirements of minimally invasive VATS in both target localization (∼3-5 mm within the target wedge) and critical structure avoidance (∼1-2 mm). The model-driven stage initialized the registration to within a median TRE of 1.9 mm (95% confidence interval (CI) maximum = 5.0 mm), while the subsequent image-driven stage yielded higher accuracy localization with 0.6 mm median TRE (95% CI maximum = 4.1 mm). The variations assessing the individual algorithmic steps elucidated the role of each step and in some cases identified opportunities for further simplification and improvement in computational speed.

CONCLUSIONS

The initial studies show the proposed registration method to successfully register CBCT images of the inflated and deflated lung. Accuracy appears sufficient to localize the target and adjacent critical anatomy within ∼1-2 mm and guide localization under conditions in which the target cannot be discerned directly in CBCT (e.g., subtle, nonsolid tumors). The ability to directly localize tumors in the operating room could provide a valuable addition to the VATS arsenal, obviate the cost, logistics, and morbidity of preoperative tagging, and improve patient safety. Future work includes in vivo testing, optimization of workflow, and integration with a CBCT image guidance system.

Automated pulmonary nodule detection based on three-dimensional shape-based feature descriptor

Computer-aided detection (CAD) can help radiologists to detect pulmonary nodules at an early stage. In pulmonary nodule CAD systems, feature extraction is very important for describing the characteristics of nodule candidates. In this paper, we propose a novel three-dimensional shape-based feature descriptor to detect pulmonary nodules in CT scans. After lung volume segmentation, nodule candidates are detected using multi-scale dot enhancement filtering in the segmented lung volume. Next, we extract feature descriptors from the detected nodule candidates, and these are refined using an iterative wall elimination method. Finally, a support vector machine-based classifier is trained to classify nodules and non-nodules. The performance of the proposed system is evaluated on Lung Image Database Consortium data. The proposed method significantly reduces the number of false positives in nodule candidates. This method achieves 97.5% sensitivity, with only 6.76 false positives per scan.

Quantitative computed tomography imaging of interstitial lung diseases

PURPOSE

High-resolution chest computed tomography (HRCT) is essential in the characterization of interstitial lung disease. The HRCT features of some diseases can be diagnostic. Longitudinal monitoring with HRCT can assess progression of interstitial lung disease; however, subtle changes in the volume and character of abnormalities can be difficult to assess. Accuracy of diagnosis can be dependent on expertise and experience of the radiologist, pathologist, or clinician. Quantitative analysis of thoracic HRCT has the potential to determine the extent of disease reproducibly, classify the types of abnormalities, and automate the diagnostic process.

MATERIALS AND METHODS

Novel software that utilizes histogram signatures to characterize pulmonary parenchyma was used to analyze chest HRCT data, including retrospective processing of clinical CT scans and research data from the Lung Tissue Research Consortium. Additional information including physiological, pathologic, and semiquantitative radiologist assessment was available to allow comparison of quantitative results, with visual estimates of the disease, physiological parameters, and measures of disease outcome.

RESULTS

Quantitative analysis results were provided in regional volumetric quantities for statistical analysis and a graphical representation. These results suggest that quantitative HRCT analysis can serve as a biomarker with physiological, pathologic, and prognostic significance.

CONCLUSIONS

It is likely that quantitative analysis of HRCT can be used in clinical practice as a means to aid in identifying a probable diagnosis, stratifying prognosis in early disease, and consistently determining progression of the disease or response to therapy. Further optimization of quantitative techniques and longitudinal analysis of well-characterized subjects would be helpful in validating these methods.

PET-CT based automated lung nodule detection

An automatic method is presented in order to detect lung nodules in PET-CT studies. Using the foreground and background mean ratio independently in every nodule, we can detect the region of the nodules properly. The size and intensity of the lesions do not affect the result of the algorithm, although size constraints are present in the final classification step. The CT image is also used to classify the found lesions built on lung segmentation. We also deal with those cases when nearby and similar nodules are merged into one by a split-up post-processing step. With our method the time of the localization can be decreased from more than one hour to maximum five minutes. The method had been implemented and validated on real clinical cases in Interview Fusion clinical evaluation software (Mediso). Results indicate that our approach is very effective in detecting lung nodules and can be a valuable aid for physicians working in the daily routine of oncology.

Automated iterative neutrosophic lung segmentation for image analysis in thoracic computed tomography

PURPOSE

Lung segmentation is a fundamental step in many image analysis applications for lung diseases and abnormalities in thoracic computed tomography (CT). The authors have previously developed a lung segmentation method based on expectation-maximization (EM) analysis and morphological operations (EMM) for our computer-aided detection (CAD) system for pulmonary embolism (PE) in CT pulmonary angiography (CTPA). However, due to the large variations in pathology that may be present in thoracic CT images, it is difficult to extract the lung regions accurately, especially when the lung parenchyma contains extensive lung diseases. The purpose of this study is to develop a new method that can provide accurate lung segmentation, including those affected by lung diseases.

METHODS

An iterative neutrosophic lung segmentation (INLS) method was developed to improve the EMM segmentation utilizing the anatomic features of the ribs and lungs. The initial lung regions (ILRs) were extracted using our previously developed EMM method, in which the ribs were extracted using 3D hierarchical EM segmentation and the ribcage was constructed using morphological operations. Based on the anatomic features of ribs and lungs, the initial EMM segmentation was refined using INLS to obtain the final lung regions. In the INLS method, the anatomic features were mapped into a neutrosophic domain, and the neutrosophic operation was performed iteratively to refine the ILRs. With IRB approval, 5 and 58 CTPA scans were collected retrospectively and used as training and test sets, of which 2 and 34 cases had lung diseases, respectively. The lung regions manually outlined by an experienced thoracic radiologist were used as reference standard for performance evaluation of the automated lung segmentation. The percentage overlap area (POA), the Hausdorff distance (Hdist), and the average distance (AvgDist) of the lung boundaries relative to the reference standard were used as performance metrics.

RESULTS

The proposed method achieved larger POAs and smaller distance errors than the EMM method. For the 58 test cases, the average POA, Hdist, and AvgDist were improved from 85.4±18.4%, 22.6±29.4 mm, and 3.5±5.4 mm using EMM to 91.2±6.7%, 16.0±11.3 mm, and 2.5±1.0 mm using INLS, respectively. The improvements were statistically significant (p<0.05). To evaluate the accuracy of the INLS method in the identification of the lung boundaries affected by lung diseases, the authors separately analyzed the performance of the proposed method on the cases with versus without the lung diseases. The results showed that the cases without lung diseases were segmented more accurately than the cases with lung diseases by both the EMM and the INLS methods, but the INLS method achieved better performance than the EMM method in both cases.

CONCLUSIONS

The new INLS method utilizing the anatomic features of the rib and lung significantly improved the accuracy of lung segmentation, especially for the cases affected by lung diseases. Improvement in lung segmentation will facilitate many image analysis tasks and CAD applications for lung diseases and abnormalities in thoracic CT, including automated PE detection.

Automated and robust PERCIST-based thresholding framework for whole body PET-CT studies

Positron emission tomography (PET) is unique for quantitatively assessing treatment response before marked morphological changes are detectable by Computed Tomography (CT). PET response criterion (PERCIST) is a widely accepted approach of assessing metabolic response of malignant lesions by using Standardized uptake value (SUV) normalized by lean body mass (LBM) with a volume of interest (VOI) reference defined in the right lobe of liver. However, the operator-dependent delineation of VOI reference is a time consuming and subjective task. Although the VOI reference can be estimated from the co-aligned CT, the low-dose CT data in PET-CT poses challenge in liver segmentation. In this study, we propose a fully automatic framework to calculate the PERCIST-based thresholding for whole-body PET-CT studies. The framework consists of multi-atlas registration and voxel classification for CT data to segment liver structure and delineate the VOI reference, which is then mapped to the PET data to derive the value of SUVLBM thresholding for PET to select regions of high metabolism. We evaluated our framework with 28 clinical studies diagnosed with lung cancer or lymphoma, and demonstrated both reliability and efficiency in depicting lesions using PERCIST thresholding.

Automated aorta segmentation in low-dose chest CT images

PURPOSE

Abnormalities of aortic surface and aortic diameter can be related to cardiovascular disease and aortic aneurysm. Computer-based aortic segmentation and measurement may aid physicians in related disease diagnosis. This paper presents a fully automated algorithm for aorta segmentation in low-dose non-contrast CT images.

METHODS

The original non-contrast CT scan images as well as their pre-computed anatomy label maps are used to locate the aorta and identify its surface. First a seed point is located inside the aortic lumen. Then, a cylindrical model is progressively fitted to the 3D image space to track the aorta centerline. Finally, the aortic surface is located based on image intensity information. This algorithm has been trained and tested on 359 low-dose non-contrast CT images from VIA-ELCAP and LIDC public image databases. Twenty images were used for training to obtain the optimal set of parameters, while the remaining images were used for testing. The segmentation result has been evaluated both qualitatively and quantitatively. Sixty representative testing images were used to establish a partial ground truth by manual marking on several axial image slices.

RESULTS

Compared to ground truth marking, the segmentation result had a mean Dice Similarity Coefficient of 0.933 (maximum 0.963 and minimum 0.907). The average boundary distance between manual segmentation and automatic segmentation was 1.39 mm with a maximum of 1.79 mm and a minimum of 0.83 mm.

CONCLUSION

Both qualitative and quantitative evaluations have shown that the presented algorithm is able to accurately segment the aorta in low-dose non-contrast CT images.

Individual nodule tracking in micro-CT images of a longitudinal lung cancer mouse model

We present and evaluate an automatic and quantitative method for the complex task of characterizing individual nodule volumetric progression in a longitudinal mouse model of lung cancer. Fourteen A/J mice received an intraperitoneal injection of urethane. Respiratory-gated micro-CT images of the lungs were acquired at 8, 22, and 37 weeks after injection. A radiologist identified a total of 196, 585 and 636 nodules, respectively. The three micro-CT image volumes from every animal were then registered and the nodules automatically matched with an average accuracy of 99.5%. All nodules detected at week 8 were tracked all the way to week 37, and volumetrically segmented to measure their growth and doubling rates. 92.5% of all nodules were correctly segmented, ranging from the earliest stage to advanced stage, where nodule segmentation becomes more challenging due to complex anatomy and nodule overlap. Volume segmentation was validated using a foam lung phantom with embedded polyethylene microspheres. We also correlated growth rates with nodule phenotypes based on histology, to conclude that the growth rate of malignant tumors is significantly higher than that of benign lesions. In conclusion, we present a turnkey solution that combines longitudinal imaging with nodule matching and volumetric nodule segmentation resulting in a powerful tool for preclinical research.

Genetic ancestry and the relationship of cigarette smoking to lung function and per cent emphysema in four race/ethnic groups: a cross-sectional study

BACKGROUND

Cigarette smoking is the major cause of chronic obstructive pulmonary disease and emphysema. Recent studies suggest that susceptibility to cigarette smoke may vary by race/ethnicity; however, they were generally small and relied on self-reported race/ethnicity.

OBJECTIVE

To test the hypothesis that relationships of smoking to lung function and per cent emphysema differ by genetic ancestry and self-reported race/ethnicity among Caucasians, African-Americans, Hispanics and Chinese-Americans.

DESIGN

Cross-sectional population-based study of adults age 45-84 years in the USA.

MEASUREMENTS

Principal components of genetic ancestry and continental ancestry estimated from one million genome-wide single nucleotide polymorphisms; pack-years of smoking; spirometry measured for 3344 participants; and per cent emphysema on computed tomography for 8224 participants.

RESULTS

The prevalence of ever-smoking was: Caucasians, 57.6%; African-Americans, 56.4%; Hispanics, 46.7%; and Chinese-Americans, 26.8%. Every 10 pack-years was associated with -0.73% (95% CI -0.90% to -0.56%) decrement in the forced expiratory volume in 1 s to forced vital capacity (FEV1 to FVC) and a 0.23% (95% CI 0.08% to 0.38%) increase in per cent emphysema. There was no evidence that relationships of pack-years to the FEV1 to FVC, airflow obstruction and per cent emphysema varied by genetic ancestry (all p>0.10), self-reported race/ethnicity (all p>0.10) or, among African-Americans, African ancestry. There were small differences in relationships of pack-years to the FEV1 among male Chinese-Americans and to the FEV1 to FVC ratio with African and Native American ancestry among male Hispanics only.

CONCLUSIONS

In this large cohort, there was little to no evidence that the associations of smoking to lung function and per cent emphysema differed by genetic ancestry or self-reported race/ethnicity.

Effects of lung inflation on airway heterogeneity during histaminergic bronchoconstriction

Lung inflation has been shown to dilate airways by altering the mechanical equilibrium between opposing airway and parenchymal forces. However, it is not known how heterogeneously such dilation occurs throughout the airway tree. In six anesthetized dogs, we measured the diameters of five to six central airway segments using high-resolution computed tomography, along with respiratory input impedance (Zrs) during generalized aerosol histamine challenge, and local histamine challenge in which the agonist was instilled directly onto the epithelia of the imaged central airways. Airway diameters and Zrs were measured at 12 and 25 cmH2O. The Zrs spectra were fitted with a model that incorporated continuous distributions of airway resistances. Airway heterogeneity was quantified using the coefficient of variation for predefined airway distribution functions. Significant reductions in average central airway diameter were observed at 12 cmH2O for both aerosolized and local challenges, along with significant increases upon inflation to 25 cmH2O. No significant differences were observed for the coefficient of variation of airway diameters under any condition. Significant increases in effective airway resistance as measured by Zrs were observed only for the aerosolized challenge at 12 cmH2O, which was completely reversed upon inflation. We conclude that the lung periphery may be the most dominant contributor to increases in airway resistance and tissue elastance during bronchoconstriction induced by aerosolized histamine. However, isolated constriction of only a few central airway segments may also affect tissue stiffness via interdependence with their surrounding parenchyma.

3D TRACHEOBRONCHIAL AIRWAY TREE SEGMENTATION FROM THORAX CT IMAGES

Segmentation of the lung structures is an important operation in the medical analysis. This paper is proposing a region growing algorithm for airway segmentation. The proposed method for the airway tree segmentation works fully in 3D and performs the measurements in the original gray-scale volume for increased accuracy and efficiency. This algorithm uses region growing and morphological operators. The airway segmentation algorithm is intended to serve qualitative and quantitative purposes, and additional three descriptors are being used for evaluation of the airway segmentation. The proposed method was evaluated using the database of 15 patients who underwent lung CT scans, with varying image quality and anatomical changes. Overlap measure is used to show the difference between measured volumes from the established gold standard and the proposed method. The student t-test and Pearson test showed high correlation of the results with the gold standard. Overall, the test results were satisfactory since accurate segmentation was achieved in 95% of the patients.

MDCT-based quantification of porcine pulmonary arterial morphometry and self-similarity of arterial branching geometry

The pig is frequently used as an experimental model for studies of the pulmonary circulation, yet the branching and dimensional geometry of the porcine pulmonary vasculature remains poorly defined. The purposes of this study are to improve the geometric definition of the porcine pulmonary arteries and to determine whether the arterial tree exhibits self-similarity in its branching geometry. Five animals were imaged using thin slice spiral computed tomography in the prone posture during airway inflation pressure at 25 cmH2O. The luminal diameter and distance from the inlet of the left and right pulmonary arteries were measured along the left and right main arterial pathway in each lung of each animal. A further six minor pathways were measured in a single animal. The similarity in the rate of reduction of diameter with distance of all minor pathways and the two main pathways, along with similarity in the number of branches arising along the pathways, supports self-similarity in the arterial tree. The rate of reduction in diameter with distance from the inlet was not significantly different among the five animals (P > 0.48) when normalized for main pulmonary artery diameter and total main artery pathlength, which supports intersubject similarity. Other metrics to quantify the tree geometry are strikingly similar to those from airways of other quadrupeds, with the exception of a significantly larger length to diameter ratio, which is more appropriate for the vascular tree. A simplifying self-similar model for the porcine pulmonary arteries is proposed to capture the important geometric features of the arterial tree.

An efficient optimization framework for multi-region segmentation based on Lagrangian duality

We introduce a multi-region model for simultaneous segmentation of medical images. In contrast to many other models, geometric constraints such as inclusion and exclusion between the regions are enforced, which makes it possible to correctly segment different regions even if the intensity distributions are identical. We efficiently optimize the model using a combination of graph cuts and Lagrangian duality which is faster and more memory efficient than current state of the art. As the method is based on global optimization techniques, the resulting segmentations are independent of initialization. We apply our framework to the segmentation of the left and right ventricles, myocardium and the left ventricular papillary muscles in magnetic resonance imaging and to lung segmentation in full-body X-ray computed tomography. We evaluate our approach on a publicly available benchmark with competitive results.

Ventilatory and cardiac responses to pulmonary embolism: consequences for gas exchange and blood pressure

Acute thromboembolic pulmonary embolism (PE) is a life threatening condition that can lead to pulmonary hypertension and right ventricular dysfunction or failure. There is typically an increase in ventilation rate and cardiac output as a response to PE prior to cardiac failure, which is at least in part due to systemic hypoxemia. Here we assess the response of the lungs to changes in these parameters using anatomically-based computational models of pulmonary perfusion, ventilation and gas exchange. We show that increases in ventilation and cardiac output improve overall gas exchange in PE. However, this comes at the cost of an increased pulmonary blood pressure, which may contribute to pulmonary hypertension as a result of PE.

Computer-aided diagnosis systems for lung cancer: challenges and methodologies

This paper overviews one of the most important, interesting, and challenging problems in oncology, the problem of lung cancer diagnosis. Developing an effective computer-aided diagnosis (CAD) system for lung cancer is of great clinical importance and can increase the patient's chance of survival. For this reason, CAD systems for lung cancer have been investigated in a huge number of research studies. A typical CAD system for lung cancer diagnosis is composed of four main processing steps: segmentation of the lung fields, detection of nodules inside the lung fields, segmentation of the detected nodules, and diagnosis of the nodules as benign or malignant. This paper overviews the current state-of-the-art techniques that have been developed to implement each of these CAD processing steps. For each technique, various aspects of technical issues, implemented methodologies, training and testing databases, and validation methods, as well as achieved performances, are described. In addition, the paper addresses several challenges that researchers face in each implementation step and outlines the strengths and drawbacks of the existing approaches for lung cancer CAD systems.

The association of systemic microvascular changes with lung function and lung density: a cross-sectional study

Smoking causes endothelial dysfunction and systemic microvascular disease with resultant end-organ damage in the kidneys, eyes and heart. Little is known about microvascular changes in smoking-related lung disease. We tested if microvascular changes in the retina, kidneys and heart were associated with obstructive spirometry and low lung density on computed tomography. The Multi-Ethnic Study of Atherosclerosis recruited participants age 45-84 years without clinical cardiovascular disease. Measures of microvascular function included retinal arteriolar and venular caliber, urine albumin-to-creatinine ratio and, in a subset, myocardial blood flow on magnetic resonance imaging. Spirometry was measured following ATS/ERS guidelines. Low attenuation areas (LAA) were measured on lung fields of cardiac computed tomograms. Regression models adjusted for pulmonary and cardiac risk factors, medications and body size. Among 3,397 participants, retinal venular caliber was inversely associated with forced expiratory volume in one second (FEV(1)) (P<0.001) and FEV(1)/forced vital capacity (FVC) ratio (P = 0.04). Albumin-to-creatinine ratio was inversely associated with FEV(1) (P = 0.002) but not FEV(1)/FVC. Myocardial blood flow (n = 126) was associated with lower FEV(1) (P = 0.02), lower FEV(1)/FVC (P = 0.001) and greater percentage LAA (P = 0.04). Associations were of greater magnitude among smokers. Low lung function was associated with microvascular changes in the retina, kidneys and heart, and low lung density was associated with impaired myocardial microvascular perfusion. These cross-sectional results suggest that microvascular damage with end-organ dysfunction in all circulations may pertain to the lung, that lung dysfunction may contribute to systemic microvascular disease, or that there may be a shared predisposition.

Improving Intensity-Based Lung CT Registration Accuracy Utilizing Vascular Information

Accurate pulmonary image registration is a challenging problem when the lungs have a deformation with large distance. In this work, we present a nonrigid volumetric registration algorithm to track lung motion between a pair of intrasubject CT images acquired at different inflation levels and introduce a new vesselness similarity cost that improves intensity-only registration. Volumetric CT datasets from six human subjects were used in this study. The performance of four intensity-only registration algorithms was compared with and without adding the vesselness similarity cost function. Matching accuracy was evaluated using landmarks, vessel tree, and fissure planes. The Jacobian determinant of the transformation was used to reveal the deformation pattern of local parenchymal tissue. The average matching error for intensity-only registration methods was on the order of 1 mm at landmarks and 1.5 mm on fissure planes. After adding the vesselness preserving cost function, the landmark and fissure positioning errors decreased approximately by 25% and 30%, respectively. The vesselness cost function effectively helped improve the registration accuracy in regions near thoracic cage and near the diaphragm for all the intensity-only registration algorithms tested and also helped produce more consistent and more reliable patterns of regional tissue deformation.

Quantifying the margin sharpness of lesions on radiological images for content-based image retrieval

PURPOSE

To develop a method to quantify the margin sharpness of lesions on CT and to evaluate it in simulations and CT scans of liver and lung lesions.

METHODS

The authors computed two attributes of margin sharpness: the intensity difference between a lesion and its surroundings, and the sharpness of the intensity transition across the lesion boundary. These two attributes were extracted from sigmoid curves fitted along lines automatically drawn orthogonal to the lesion margin. The authors then represented the margin characteristics for each lesion by a feature vector containing histograms of these parameters. The authors created 100 simulated CT scans of lesions over a range of intensity difference and margin sharpness, and used the concordance correlation between the known parameter and the corresponding computed feature as a measure of performance. The authors also evaluated their method in 79 liver lesions (44 patients: 23 M, 21 F, mean age 61) and 58 lung nodules (57 patients: 24 M, 33 F, mean age 66). The methodology presented takes into consideration the boundary of the liver and lung during feature extraction in clinical images to ensure that the margin feature do not get contaminated by anatomy other than the normal organ surrounding the lesions. For evaluation in these clinical images, the authors created subjective independent reference standards for pairwise margin sharpness similarity in the liver and lung cohorts, and compared rank orderings of similarity used using our sharpness feature to that expected from the reference standards using mean normalized discounted cumulative gain (NDCG) over all query images. In addition, the authors compared their proposed feature with two existing techniques for lesion margin characterization using the simulated and clinical datasets. The authors also evaluated the robustness of their features against variations in delineation of the lesion margin by simulating five types of deformations of the lesion margin. Equivalence across deformations was assessed using Schuirmann's paired two one-sided tests.

RESULTS

In simulated images, the concordance correlation between measured gradient and actual gradient was 0.994. The mean (s.d.) and standard deviation NDCG score for the retrieval of K images, K = 5, 10, and 15, were 84% (8%), 85% (7%), and 85% (7%) for CT images containing liver lesions, and 82% (7%), 84% (6%), and 85% (4%) for CT images containing lung nodules, respectively. The authors' proposed method outperformed the two existing margin characterization methods in average NDCG scores over all K, by 1.5% and 3% in datasets containing liver lesion, and 4.5% and 5% in datasets containing lung nodules. Equivalence testing showed that the authors' feature is more robust across all margin deformations (p < 0.05) than the two existing methods for margin sharpness characterization in both simulated and clinical datasets.

CONCLUSIONS

The authors have described a new image feature to quantify the margin sharpness of lesions. It has strong correlation with known margin sharpness in simulated images and in clinical CT images containing liver lesions and lung nodules. This image feature has excellent performance for retrieving images with similar margin characteristics, suggesting potential utility, in conjunction with other lesion features, for content-based image retrieval applications.

Quantification of lung damage in an elastase-induced mouse model of emphysema

Objective. To define the sensitivity of microcomputed tomography- (micro-CT-) derived descriptors for the quantification of lung damage caused by elastase instillation. Materials and Methods. The lungs of 30 elastase treated and 30 control A/J mice were analyzed 1, 6, 12, and 24 hours and 7 and 17 days after elastase instillation using (i) breath-hold-gated micro-CT, (ii) pulmonary function tests (PFTs), (iii) RT-PCR for RNA cytokine expression, and (iv) histomorphometry. For the latter, an automatic, parallel software toolset was implemented that computes the airspace enlargement descriptors: mean linear intercept (L(m)) and weighted means of airspace diameters (D(0), D(1), and D(2)). A Support Vector Classifier was trained and tested based on three nonhistological descriptors using D(2) as ground truth. Results. D(2) detected statistically significant differences (P < 0.01) between the groups at all time points. Furthermore, D(2) at 1 hour (24 hours) was significantly lower (P < 0.01) than D(2) at 24 hours (7 days). The classifier trained on the micro-CT-derived descriptors achieves an area under the curve (AUC) of 0.95 well above the others (PFTS AUC = 0.71; cytokine AUC = 0.88). Conclusion. Micro-CT-derived descriptors are more sensitive than the other methods compared, to detect in vivo early signs of the disease.

Emphysema quantification by low-dose CT: potential impact of adaptive iterative dose reduction using 3D processing

OBJECTIVE

The purpose of this study is to investigate the effect of a novel reconstruction algorithm, adaptive iterative dose reduction using 3D processing, on emphysema quantification by low-dose CT.

MATERIALS AND METHODS

Twenty-six patients who had undergone standard-dose (150 mAs) and low-dose (25 mAs) CT scans were included in this retrospective study. Emphysema was quantified by several quantitative measures, including emphysema index given by the percentage of lung region with low attenuation (lower than -950 HU), the 15th percentile of lung density, and size distribution of low-attenuation lung regions, on standard-dose CT images reconstructed without adaptive iterative dose reduction using 3D processing and on low-dose CT images reconstructed both without and with adaptive iterative dose reduction using 3D processing. The Bland-Altman analysis was used to assess whether the agreement between emphysema quantifications on low-dose CT and on standard-dose CT was improved by the use of adaptive iterative dose reduction using 3D processing.

RESULTS

For the emphysema index, the mean differences between measurements on low-dose CT and on standard-dose CT were 1.98% without and -0.946% with the use of adaptive iterative dose reduction using 3D processing. For 15th percentile of lung density, the mean differences without and with adaptive iterative dose reduction using 3D processing were -6.67 and 1.28 HU, respectively. For the size distribution of low-attenuation lung regions, the ranges of the mean relative differences without and with adaptive iterative dose reduction using 3D processing were 21.4-85.5% and -14.1% to 11.2%, respectively. For 15th percentile of lung density and the size distribution of low-attenuation lung regions, the agreement was thus improved by the use of adaptive iterative dose reduction using 3D processing.

CONCLUSION

The use of adaptive iterative dose reduction using 3D processing resulted in greater consistency of emphysema quantification by low-dose CT, with quantification by standard-dose CT.

Hierarchical patch-based sparse representation--a new approach for resolution enhancement of 4D-CT lung data

4D-CT plays an important role in lung cancer treatment because of its capability in providing a comprehensive characterization of respiratory motion for high-precision radiation therapy. However, due to the inherent high-dose exposure associated with CT, dense sampling along superior-inferior direction is often not practical, thus resulting in an inter-slice thickness that is much greater than in-plane voxel resolutions. As a consequence, artifacts such as lung vessel discontinuity and partial volume effects are often observed in 4D-CT images, which may mislead dose administration in radiation therapy. In this paper, we present a novel patch-based technique for resolution enhancement of 4D-CT images along the superior-inferior direction. Our working premise is that anatomical information that is missing in one particular phase can be recovered from other phases. Based on this assumption, we employ a hierarchical patch-based sparse representation mechanism to enhance the superior-inferior resolution of 4D-CT by reconstructing additional intermediate CT slices. Specifically, for each spatial location on an intermediate CT slice that we intend to reconstruct, we first agglomerate a dictionary of patches from images of all other phases in the 4D-CT. We then employ a sparse combination of patches from this dictionary, with guidance from neighboring (upper and lower) slices, to reconstruct a series of patches, which we progressively refine in a hierarchical fashion to reconstruct the final intermediate slices with significantly enhanced anatomical details. Our method was extensively evaluated using a public dataset. In all experiments, our method outperforms the conventional linear and cubic-spline interpolation methods in preserving image details and also in suppressing misleading artifacts, indicating that our proposed method can potentially be applied to better image-guided radiation therapy of lung cancer in the future.

Computed tomography-based biomarker provides unique signature for diagnosis of COPD phenotypes and disease progression

Chronic obstructive pulmonary disease (COPD) is increasingly being recognized as a highly heterogeneous disorder, composed of varying pathobiology. Accurate detection of COPD subtypes by image biomarkers are urgently needed to enable individualized treatment thus improving patient outcome. We adapted the Parametric Response Map (PRM), a voxel-wise image analysis technique, for assessing COPD phenotype. We analyzed whole lung CT scans of 194 COPD individuals acquired at inspiration and expiration from the COPDGene Study. PRM identified the extent of functional small airways disease (fSAD) and emphysema as well as provided CT-based evidence that supports the concept that fSAD precedes emphysema with increasing COPD severity. PRM is a versatile imaging biomarker capable of diagnosing disease extent and phenotype, while providing detailed spatial information of disease distribution and location. PRMs ability to differentiate between specific COPD phenotypes will allow for more accurate diagnosis of individual patients complementing standard clinical techniques.

Illustration of the obstacles in computerized lung segmentation using examples

PURPOSE

Automated lung volume segmentation is often a preprocessing step in quantitative lung computed tomography (CT) image analysis. The objective of this study is to identify the obstacles in computerized lung volume segmentation and illustrate those explicitly using real examples. Awareness of these "difficult" cases may be helpful for the development of a robust and consistent lung segmentation algorithm.

METHODS

We collected a large diverse dataset consisting of 2768 chest CT examinations acquired on 2292 subjects from various sources. These examinations cover a wide range of diseases, including lung cancer, chronic obstructive pulmonary disease, human immunodeficiency virus, pulmonary embolism, pneumonia, asthma, and interstitial lung disease (ILD). The CT acquisition protocols, including dose, scanners, and reconstruction kernels, vary significantly. After the application of a "neutral" thresholding-based approach to the collected CT examinations in a batch manner, the failed cases were subjectively identified and classified into different subgroups.

RESULTS

Totally, 121 failed examinations are identified, corresponding to a failure ratio of 4.4%. These failed cases are summarized as 11 different subgroups, which is further classified into 3 broad categories: (1) failure caused by diseases, (2) failure caused by anatomy variability, and (3) failure caused by external factors. The failure percentages in these categories are 62.0%, 32.2%, and 5.8%, respectively.

CONCLUSIONS

The presence of specific lung diseases (e.g., pulmonary nodules, ILD, and pneumonia) is the primary issue in computerized lung segmentation. The segmentation failures caused by external factors and anatomy variety are relatively low but unavoidable in practice. It is desirable to develop robust schemes to handle these issues in a single pass when a large number of CT examinations need to be analyzed.

A comparative study of HRCT image metrics and PFT values for characterization of ILD and COPD

RATIONALE AND OBJECTIVES

The aim of this study was to compare the performance of various image-based metrics computed from thoracic high-resolution computed tomography (HRCT) with data from pulmonary function testing (PFT) in characterizing interstitial lung disease (ILD) and chronic obstructive pulmonary disease (COPD).

MATERIALS AND METHODS

Fourteen patients with ILD and 11 with COPD had undergone both PFT and HRCT within 3 days. For each patient, 93 image-based metrics were computed, and their relationships with the 21 clinically used PFT parameters were analyzed using a minimal-redundancy-maximal-relevance statistical framework. The first 20 features were selected among the total of 114 mixed image metrics and PFT values in the characterization of ILD and COPD.

RESULTS

Among the best-performing 20 features, 14 were image metrics, derived from attenuation histograms and texture descriptions. The highest relevance value computed from PFT parameters was 0.47, and the highest from image metrics was 0.52, given the theoretical bound as [0, 0.69]. The ILD or COPD classifier using the first four features achieved a 1.92% error rate.

CONCLUSIONS

Some image metrics are not only as good discriminators as PFT for the characterization of ILD and COPD but are also not redundant when PFT values are provided. Image metrics of attenuation histogram statistics and texture descriptions may be valuable for further investigation in computer-assisted diagnosis.

Precise segmentation of 3-D magnetic resonance angiography

Accurate automatic extraction of a 3-D cerebrovascular system from images obtained by time-of-flight (TOF) or phase contrast (PC) magnetic resonance angiography (MRA) is a challenging segmentation problem due to the small size objects of interest (blood vessels) in each 2-D MRA slice and complex surrounding anatomical structures (e.g., fat, bones, or gray and white brain matter). We show that due to the multimodal nature of MRA data, blood vessels can be accurately separated from the background in each slice using a voxel-wise classification based on precisely identified probability models of voxel intensities. To identify the models, an empirical marginal probability distribution of intensities is closely approximated with a linear combination of discrete Gaussians (LCDG) with alternate signs, using our previous EM-based techniques for precise linear combination of Gaussian-approximation adapted to deal with the LCDGs. The high accuracy of the proposed approach is experimentally validated on 85 real MRA datasets (50 TOF and 35 PC) as well as on synthetic MRA data for special 3-D geometrical phantoms of known shapes.

A novel segmentation approach for improving diagnostic accuracy of CAD systems for detecting lung cancer from chest computed tomography images

Segmentation of lung tissue is an important and challenging task in any computer aided diagnosis system. The accuracy of the segmentation subsystem determines the performance of the other subsystems in any computer aided diagnosis system based on image analysis. We propose a novel technique for segmentation of lung tissue from computed tomography of the chest. Manual segmentation of lung parenchyma becomes difficult with an enormous volume of images. The goal of this work is to present an automated approach to segmentation of lung parenchyma from the rest of the chest CT image. The approach involves the conventional optimal thresholding technique and operations based on convex edge and centroid properties of the lung region. The segmentation technique proposed in this article can be used to preprocess lung images given to a computer aided diagnosis system for diagnosis of lung disorders. This improves the diagnostic performance of the system. This has been tested by using it in a computer aided diagnosis system that was used for detection of lung cancer from chest computed tomography images. The results obtained show that the lungs can be correctly segmented even in the presence of peripheral pathology bearing regions; pathology bearing regions that could not be detected using a CAD system that applies optimal thresholding could be detected using a CAD system using out proposed approach for segmentation of lungs.