Computer-Aided Diagnostic System for Early Detection of Acute Renal Transplant Rejection Using Diffusion-Weighted MRI.

OBJECTIVE: Early diagnosis of acute renal transplant rejection (ARTR) is critical for accurate treatment. Although the current gold standard, diagnostic technique is renal biopsy, it is not preferred due to its invasiveness, long recovery time (1-2 weeks), and potential for complications, e.g., bleeding and/or infection. METHODS: This paper presents a computer-aided diagnostic (CAD) system for early ARTR detection using (3D + b-value) diffusion-weighted (DW) magnetic resonance imaging (MRI) data. The CAD process starts from kidney tissue segmentation with an evolving geometric (level-set-based) deformable model. The evolution is guided by a voxel-wise stochastic speed function, which follows from a joint kidney-background Markov-Gibbs random field model accounting for an adaptive kidney shape prior and on-going kidney-background visual appearances. A B-spline-based three-dimensional data alignment is employed to handle local deviations due to breathing and heart beating. Then, empirical cumulative distribution functions of apparent diffusion coefficients of the segmented DW-MRI at different b-values are collected as discriminatory transplant status features. Finally, a deep-learning-based classifier with stacked nonnegative constrained autoencoders is employed to distinguish between rejected and nonrejected renal transplants. RESULTS: In our initial "leave-one-subject-out" experiment on 100 subjects, [Formula: see text] of the subjects were correctly classified. The subsequent four-fold and ten-fold cross-validations gave the average accuracy of [Formula: see text] and [Formula: see text], respectively. CONCLUSION: These results demonstrate the promise of this new CAD system to reliably diagnose renal transplant rejection. SIGNIFICANCE: The technology presented here can significantly impact the quality of care of renal transplant patients since it has the potential to replace the gold standard in kidney diagnosis, biopsy.

Alzheimer's disease diagnostics by a 3D deeply supervised adaptable convolutional network.

Early diagnosis is playing an important role in preventing progress of the Alzheimer's disease (AD). This paper proposes to improve the prediction of AD with a deep 3D Convolutional Neural Network (3D-CNN), which can show generic features capturing AD biomarkers extracted from brain images, adapt to different domain datasets, and accurately classify subjects with improved fine-tuning method. The 3D-CNN is built upon a convolutional autoencoder, which is pre-trained to capture anatomical shape variations in structural brain MRI scans for source domain. Fully connected upper layers of the 3D-CNN are then fine-tuned for each task-specific AD classification in target domain. In this paper, deep supervision algorithm is used to improve the performance of already proposed 3D Adaptive CNN. Experiments on the ADNI MRI dataset without skull-stripping preprocessing have shown that the proposed 3D Deeply Supervised Adaptable CNN outperforms several proposed approaches, including 3D-CNN model, other CNN-based methods and conventional classifiers by accuracy and robustness. Abilities of the proposed network to generalize the features learnt and adapt to other domains have been validated on the CADDementia dataset.

A fast stochastic framework for automatic MR brain images segmentation.

This paper introduces a new framework for the segmentation of different brain structures (white matter, gray matter, and cerebrospinal fluid) from 3D MR brain images at different life stages. The proposed segmentation framework is based on a shape prior built using a subset of co-aligned training images that is adapted during the segmentation process based on first- and second-order visual appearance characteristics of MR images. These characteristics are described using voxel-wise image intensities and their spatial interaction features. To more accurately model the empirical grey level distribution of the brain signals, we use a linear combination of discrete Gaussians (LCDG) model having positive and negative components. To accurately account for the large inhomogeneity in infant MRIs, a higher-order Markov-Gibbs Random Field (MGRF) spatial interaction model that integrates third- and fourth- order families with a traditional second-order model is proposed. The proposed approach was tested and evaluated on 102 3D MR brain scans using three metrics: the Dice coefficient, the 95-percentile modified Hausdorff distance, and the absolute brain volume difference. Experimental results show better segmentation of MR brain images compared to current open source segmentation tools.

3D Kidney Segmentation from Abdominal Images Using Spatial-Appearance Models.

Kidney segmentation is an essential step in developing any noninvasive computer-assisted diagnostic system for renal function assessment. This paper introduces an automated framework for 3D kidney segmentation from dynamic computed tomography (CT) images that integrates discriminative features from the current and prior CT appearances into a random forest classification approach. To account for CT images' inhomogeneities, we employ discriminate features that are extracted from a higher-order spatial model and an adaptive shape model in addition to the first-order CT appearance. To model the interactions between CT data voxels, we employed a higher-order spatial model, which adds the triple and quad clique families to the traditional pairwise clique family. The kidney shape prior model is built using a set of training CT data and is updated during segmentation using not only region labels but also voxels' appearances in neighboring spatial voxel locations. Our framework performance has been evaluated on in vivo dynamic CT data collected from 20 subjects and comprises multiple 3D scans acquired before and after contrast medium administration. Quantitative evaluation between manually and automatically segmented kidney contours using Dice similarity, percentage volume differences, and 95th-percentile bidirectional Hausdorff distances confirms the high accuracy of our approach.

A comprehensive non-invasive framework for diagnosing prostate cancer.

Early detection of prostate cancer increases chances of patients' survival. Our automated non-invasive system for computer-aided diagnosis (CAD) of prostate cancer segments the prostate on diffusion-weighted magnetic resonance images (DW-MRI) acquired at different b-values, estimates its apparent diffusion coefficients (ADC), and classifies their descriptors - empirical cumulative distribution functions (CDF) - with a trained deep learning network. To segment the prostate, an evolving geometric (level-set-based) deformable model is guided by a speed function depending on intensity attributes extracted from the DW-MRI with nonnegative matrix factorization (NMF). For a more robust evolution, the attributes are fused with a probabilistic shape prior and estimated spatial dependencies between prostate voxels. To preserve continuity, the ADCs of the segmented prostate volume at different b-values are normalized and refined using a generalized Gauss-Markov random field image model. The CDFs of the refined ADCs at different b-values are considered global water diffusion features and used to distinguish between benign and malignant prostates. A deep learning network of stacked non-negativity-constrained auto-encoders (SNCAE) is trained to classify the benign or malignant prostates on the basis of the constructed CDFs. Our experiments on 53 clinical DW-MRI data sets resulted in 92.3% accuracy, 83.3% sensitivity, and 100% specificity, indicating that the proposed CAD system could be used as a reliable non-invasive diagnostic tool.

Accurate Lungs Segmentation on CT Chest Images by Adaptive Appearance-Guided Shape Modeling.

To accurately segment pathological and healthy lungs for reliable computer-aided disease diagnostics, a stack of chest CT scans is modeled as a sample of a spatially inhomogeneous joint 3D Markov-Gibbs random field (MGRF) of voxel-wise lung and chest CT image signals (intensities). The proposed learnable MGRF integrates two visual appearance sub-models with an adaptive lung shape submodel. The first-order appearance submodel accounts for both the original CT image and its Gaussian scale space (GSS) filtered version to specify local and global signal properties, respectively. Each empirical marginal probability distribution of signals is closely approximated with a linear combination of discrete Gaussians (LCDG), containing two positive dominant and multiple sign-alternate subordinate DGs. The approximation is separated into two LCDGs to describe individually the lungs and their background, i.e., all other chest tissues. The second-order appearance submodel quantifies conditional pairwise intensity dependencies in the nearest voxel 26-neighborhood in both the original and GSS-filtered images. The shape submodel is built for a set of training data and is adapted during segmentation using both the lung and chest appearances. The accuracy of the proposed segmentation framework is quantitatively assessed using two public databases (ISBI VESSEL12 challenge and MICCAI LOLA11 challenge) and our own database with, respectively, 20, 55, and 30 CT images of various lung pathologies acquired with different scanners and protocols. Quantitative assessment of our framework in terms of Dice similarity coefficients, 95-percentile bidirectional Hausdorff distances, and percentage volume differences confirms the high accuracy of our model on both our database (98.4±1.0%, 2.2±1.0 mm, 0.42±0.10%) and the VESSEL12 database (99.0±0.5%, 2.1±1.6 mm, 0.39±0.20%), respectively. Similarly, the accuracy of our approach is further verified via a blind evaluation by the organizers of the LOLA11 competition, where an average overlap of 98.0% with the expert's segmentation is yielded on all 55 subjects with our framework being ranked first among all the state-of-the-art techniques compared.

A computer-aided diagnostic system for detecting diabetic retinopathy in optical coherence tomography images.

PURPOSE: Detection (diagnosis) of diabetic retinopathy (DR) in optical coherence tomography (OCT) images for patients with type 2 diabetes, but almost clinically normal retina appearances. METHODS: The proposed computer-aided diagnostic (CAD) system detects the DR in three steps: (a) localizing and segmenting 12 distinct retinal layers on the OCT image; (b) deriving features of the segmented layers, and (c) learning most discriminative features and classifying each subject as normal or diabetic. To localise and segment the retinal layers, signals (intensities) of the OCT image are described with a joint Markov-Gibbs random field (MGRF) model of intensities and shape descriptors. Each segmented layer is characterized with cumulative probability distribution functions (CDF) of its locally extracted features, such as reflectivity, curvature, and thickness. A multistage deep fusion classification network (DFCN) with a stack of non-negativity-constrained autoencoders (NCAE) is trained to select the most discriminative retinal layers' features and use their CDFs for detecting the DR. A training atlas was built using the OCT scans for 12 normal subjects and their maps of layers hand-drawn by retina experts. RESULTS: Preliminary experiments on 52 clinical OCT scans (26 normal and 26 with early-stage DR, balanced between 40-79 yr old males and females; 40 training and 12 test subjects) gave the DR detection accuracy, sensitivity, and specificity of 92%; 83%, and 100%, respectively. The 100% accuracy, sensitivity, and specificity have been obtained in the leave-one-out cross-validation test for all the 52 subjects. CONCLUSION: Both the quantitative and visual assessments confirmed the high accuracy of the proposed computer-assisted diagnostic system for early DR detection using the OCT retinal images.

Studying Autism Spectrum Disorder with Structural and Diffusion Magnetic Resonance Imaging: A Survey.

Magnetic resonance imaging (MRI) modalities have emerged as powerful means that facilitate non-invasive clinical diagnostics of various diseases and abnormalities since their inception in the 1980s. Multiple MRI modalities, such as different types of the sMRI and DTI, have been employed to investigate facets of ASD in order to better understand this complex syndrome. This paper reviews recent applications of structural magnetic resonance imaging (sMRI) and diffusion tensor imaging (DTI), to study autism spectrum disorder (ASD). Main reported findings are sometimes contradictory due to different age ranges, hardware protocols, population types, numbers of participants, and image analysis parameters. The primary anatomical structures, such as amygdalae, cerebrum, and cerebellum, associated with clinical-pathological correlates of ASD are highlighted through successive life stages, from infancy to adulthood. This survey demonstrates the absence of consistent pathology in the brains of autistic children and lack of research investigations in patients under 2 years of age in the literature. The known publications also emphasize advances in data acquisition and analysis, as well as significance of multimodal approaches that combine resting-state, task-evoked, and sMRI measures. Initial results obtained with the sMRI and DTI show good promise toward the early and non-invasive ASD diagnostics.

Lightweight Adaptation of Classifiers to Users and Contexts: Trends of the Emerging Domain.

Intelligent computer applications need to adapt their behaviour to contexts and users, but conventional classifier adaptation methods require long data collection and/or training times. Therefore classifier adaptation is often performed as follows: at design time application developers define typical usage contexts and provide reasoning models for each of these contexts, and then at runtime an appropriate model is selected from available ones. Typically, definition of usage contexts and reasoning models heavily relies on domain knowledge. However, in practice many applications are used in so diverse situations that no developer can predict them all and collect for each situation adequate training and test databases. Such applications have to adapt to a new user or unknown context at runtime just from interaction with the user, preferably in fairly lightweight ways, that is, requiring limited user effort to collect training data and limited time of performing the adaptation. This paper analyses adaptation trends in several emerging domains and outlines promising ideas, proposed for making multimodal classifiers user-specific and context-specific without significant user efforts, detailed domain knowledge, and/or complete retraining of the classifiers. Based on this analysis, this paper identifies important application characteristics and presents guidelines to consider these characteristics in adaptation design.

Intramyocardial strain estimation from cardiac cine MRI.

PURPOSE: Functional strain is one of the important clinical indicators for the quantification of heart performance and the early detection of cardiovascular diseases, and functional strain parameters are used to aid therapeutic decisions and follow-up evaluations after cardiac surgery. A comprehensive framework for deriving functional strain parameters at the endocardium, epicardium, and mid-wall of the left ventricle (LV) from conventional cine MRI data was developed and tested. METHODS: Cine data were collected using short TR-/TE-balanced steady-state free precession acquisitions on a 1.5T Siemens Espree scanner. The LV wall borders are segmented using a level set-based deformable model guided by a stochastic force derived from a second-order Markov-Gibbs random field model that accounts for the object shape and appearance features. Then, the mid-wall of the segmented LV is determined based on estimating the centerline between the endocardium and epicardium of the LV. Finally, a geometrical Laplace-based method is proposed to track corresponding points on successive myocardial contours throughout the cardiac cycle in order to characterize the strain evolutions. The method was tested using simulated phantom images with predefined point locations of the LV wall throughout the cardiac cycle. The method was tested on 30 in vivo datasets to evaluate the feasibility of the proposed framework to index functional strain parameters. RESULTS: The cine MRI-based model agreed with the ground truth for functional metrics to within 0.30 % for indexing the peak systolic strain change and 0.29 % (per unit time) for indexing systolic and diastolic strain rates. The method was feasible for in vivo extraction of functional strain parameters. CONCLUSION: Strain indexes of the endocardium, mid-wall, and epicardium can be derived from routine cine images using automated techniques, thereby improving the utility of cine MRI data for characterization of myocardial function. Unlike traditional texture-based tracking, the proposed geometrical method showed the ability to track the LV wall points throughout the cardiac cycle, thus permitting more accurate strain estimation.

Models and methods for analyzing DCE-MRI: a review.

PURPOSE: To present a review of most commonly used techniques to analyze dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), discusses their strengths and weaknesses, and outlines recent clinical applications of findings from these approaches. METHODS: DCE-MRI allows for noninvasive quantitative analysis of contrast agent (CA) transient in soft tissues. Thus, it is an important and well-established tool to reveal microvasculature and perfusion in various clinical applications. In the last three decades, a host of nonparametric and parametric models and methods have been developed in order to quantify the CA's perfusion into tissue and estimate perfusion-related parameters (indexes) from signal- or concentration-time curves. These indexes are widely used in various clinical applications for the detection, characterization, and therapy monitoring of different diseases. RESULTS: Promising theoretical findings and experimental results for the reviewed models and techniques in a variety of clinical applications suggest that DCE-MRI is a clinically relevant imaging modality, which can be used for early diagnosis of different diseases, such as breast and prostate cancer, renal rejection, and liver tumors. CONCLUSIONS: Both nonparametric and parametric approaches for DCE-MRI analysis possess the ability to quantify tissue perfusion.

Fully automated framework for the analysis of myocardial first-pass perfusion MR images.

PURPOSE: To develop an automated framework for accurate analysis of myocardial perfusion using first-pass magnetic resonance imaging. METHODS: The proposed framework consists of four processing stages. First, in order to account for heart deformations due to respiratory motion and heart contraction, a two-step registration methodology is proposed, which has the ability to account for the global and local motions of the heart. The methodology involves an affine-based registration followed by a local B-splines alignment to maximize a new similarity function based on the first- and second-order normalized mutual information. Then the myocardium is segmented using a level-set function, its evolution being constrained by three features, namely, a weighted shape prior, a pixelwise mixed object/background image intensity distribution, and an energy of a second-order binary Markov-Gibbs random field spatial model. At the third stage, residual segmentation errors and imperfection of image alignment are reduced by employing a Laplace-based registration refinement step that provides accurate pixel-on-pixel matches on all segmented frames to generate accurate parametric perfusion maps. Finally, physiology is characterized by pixel-by-pixel mapping of empirical indexes (peak signal intensity, time-to-peak, initial upslope, and the average signal change of the slowly varying agent delivery phase), based on contrast agent dynamics. RESULTS: The authors tested our framework on 24 perfusion data sets from 8 patients with ischemic damage who are undergoing a novel myoregeneration therapy. The performance of the processing steps of our framework is evaluated using both synthetic and in-vivo data. First, our registration methodology is evaluated using realistic synthetic phantoms and a distance-based error metric, and an improvement of registration is documented using the proposed similarity measure (P-value ≤10(-4)). Second, evaluation of our segmentation using the Dice similarity coefficient, documented an average of 0.910 ± 0.037 compared to two other segmentation methods that achieved average values of 0.862 ± 0.045 and 0.844 ± 0.047. Also, the receiver operating characteristic (ROC) analysis of our multifeature segmentation yielded an area under the ROC curve of 0.92, while segmentation based intensity alone showed low performance (an area of 0.69). Moreover, our framework indicated the ability, using empirical perfusion indexes, to reveal regional perfusion improvements with therapy and transmural perfusion differences across the myocardial wall. CONCLUSIONS: By quantitative and visual assessment, our framework documented the ability to characterize regional and transmural perfusion, thereby it augmenting the ability to assess follow-up treatment for patients undergoing myoregeneration therapy. This is afforded by our framework being able to handle both global and local deformations of the heart, segment accurately the myocardial wall, and provide accurate pixel-on-pixel matches of registered perfusion images.

Magnetic resonance imaging findings for dyslexia: a review.

Developmental dyslexia is a brain disorder that is associated with a disability to read, which affects both the behavior and the learning abilities of children. Recent advances in MRI techniques have enabled imaging of different brain structures and correlating the results to clinical findings. The goal of this paper is to cover these imaging studies in order to provide a better understanding of dyslexia and its associated brain abnormalities. In addition, this survey covers the noninvasive MRI-based diagnostics methods that can offer early detection of dyslexia. We focus on three MRI techniques: structural MRI, functional MRI, and diffusion tensor imaging. Structural MRI reveals dyslexia-associated volumetric and shape-based abnormalities in different brain structures (e.g., reduced grey matter volumes, decreased cerebral white matter gyrifications, increased corpus callosum size, and abnormal asymmetry of the cerebellum and planum temporale structures). Functional MRI reports abnormal activation patterns in dyslexia during reading operations (e.g., aggregated studies observed under-activations in the left hemisphere fusiform and supramarginal. gyri and over-activation in the left cerebellum in dyslexic subjects compared with controls). Finally, diffusion tensor imaging reveals abnormal orientations in areas within the white matter micro-structures of dyslexic brains (e.g., aggregated studies reported a reduction of the fraction anisotropy values in bilateral areas within the white matter). Herein, we will discuss all of these MRI findings focusing on various aspects of implemented methodologies, testing databases, as well as the reported findings. Finally, the paper addresses the correlation between the MRI findings in the literature, various aspects of research challenges, and future trends in this active research field.

Local Wavelet-Based Filtering of Electromyographic Signals to Eliminate the Electrocardiographic-Induced Artifacts in Patients with Spinal Cord Injury.

Surface Electromyography (EMG) is a standard method used in clinical practice and research to assess motor function in order to help with the diagnosis of neuromuscular pathology in human and animal models. EMG recorded from trunk muscles involved in the activity of breathing can be used as a direct measure of respiratory motor function in patients with spinal cord injury (SCI) or other disorders associated with motor control deficits. However, EMG potentials recorded from these muscles are often contaminated with heart-induced electrocardiographic (ECG) signals. Elimination of these artifacts plays a critical role in the precise measure of the respiratory muscle electrical activity. This study was undertaken to find an optimal approach to eliminate the ECG artifacts from EMG recordings. Conventional global filtering can be used to decrease the ECG-induced artifact. However, this method can alter the EMG signal and changes physiologically relevant information. We hypothesize that, unlike global filtering, localized removal of ECG artifacts will not change the original EMG signals. We develop an approach to remove the ECG artifacts without altering the amplitude and frequency components of the EMG signal by using an externally recorded ECG signal as a mask to locate areas of the ECG spikes within EMG data. These segments containing ECG spikes were decomposed into 128 sub-wavelets by a custom-scaled Morlet Wavelet Transform. The ECG-related sub-wavelets at the ECG spike location were removed and a de-noised EMG signal was reconstructed. Validity of the proposed method was proven using mathematical simulated synthetic signals and EMG obtained from SCI patients. We compare the Root-mean Square Error and the Relative Change in Variance between this method, global, notch and adaptive filters. The results show that the localized wavelet-based filtering has the benefit of not introducing error in the native EMG signal and accurately removing ECG artifacts from EMG signals.

Myocardial borders segmentation from cine MR images using bidirectional coupled parametric deformable models.

PURPOSE: The authors propose 3D (2D + time) novel, fast, robust, bidirectional coupled parametric deformable models that are capable of segmenting left ventricle (LV) wall borders using first- and second-order visual appearance features. The authors examine the effect of the proposed segmentation method on the estimation of global cardiac performance indexes. METHODS: First-order visual appearance of the cine cardiac magnetic resonance (CMR) signals (inside and outside the boundary of the deformable model) is modeled with an adaptive linear combination of discrete Gaussians (LCDG). Second-order visual appearance of the LV wall is accurately modeled with a translational and rotation-invariant second-order Markov-Gibbs random field (MGRF). The LCDG parameters are estimated using our previously proposed modification of the EM algorithm, and the potentials of rotationally invariant MGRF are computed analytically. RESULTS: The authors tested the proposed segmentation approach on 15 cine CMR data sets using the Dice similarity coefficient (DSC) and the average distance (AD) between the ground truth and automated segmentation contours. The authors documented an average DSC value of 0.926 ± 0.022 and an average AD value of 2.16 ± 0.60 mm compared to two other level set methods that achieve an average DSC values of 0.904 ± 0.033 and 0.885 ± 0.02; and an average AD values of 2.86 ± 1.35 mm and 5.72 ± 4.70 mm, respectively. CONCLUSIONS: The proposed segmentation approach demonstrated superior performance over other methods. Specifically, the comparative results on the publicly available MICCAI 2009 Cardiac MR Left Ventricle Segmentation database documented superior performance of the proposed approach over published methods. Additionally, the high accuracy of our segmentation approach leads to accurate estimation of the global performance indexes, as evidenced by the Bland-Altman analyses of the end-systolic volume (ESV), end-diastolic volume (EDV), and the ejection fraction (EF) ratio.

Dynamic contrast-enhanced MRI-based early detection of acute renal transplant rejection.

A novel framework for the classification of acute rejection versus nonrejection status of renal transplants from 2-D dynamic contrast-enhanced magnetic resonance imaging is proposed. The framework consists of four steps. First, kidney objects are segmented from adjacent structures with a level set deformable boundary guided by a stochastic speed function that accounts for a fourth-order Markov-Gibbs random field model of the kidney/background shape and appearance. Second, a Laplace-based nonrigid registration approach is used to account for local deformations caused by physiological effects. Namely, the target kidney object is deformed over closed, equispaced contours (iso-contours) to closely match the reference object. Next, the cortex is segmented as it is the functional kidney unit that is most affected by rejection. To characterize rejection, perfusion is estimated from contrast agent kinetics using empirical indexes, namely, the transient phase indexes (peak signal intensity, time-to-peak, and initial up-slope), and a steady-phase index defined as the average signal change during the slowly varying tissue phase of agent transit. We used a kn-nearest neighbor classifier to distinguish between acute rejection and nonrejection. Performance of our method was evaluated using the receiver operating characteristics (ROC). Experimental results in 50 subjects, using a combinatoric kn-classifier, correctly classified 92% of training subjects, 100% of the test subjects, and yielded an area under the ROC curve that approached the ideal value. Our proposed framework thus holds promise as a reliable noninvasive diagnostic tool.

Automatic Detection of 2D and 3D Lung Nodules in Chest Spiral CT Scans.

Automatic detection of lung nodules is an important problem in computer analysis of chest radiographs. In this paper, we propose a novel algorithm for isolating lung abnormalities (nodules) from spiral chest low-dose CT (LDCT) scans. The proposed algorithm consists of three main steps. The first step isolates the lung nodules, arteries, veins, bronchi, and bronchioles from the surrounding anatomical structures. The second step detects lung nodules using deformable 3D and 2D templates describing typical geometry and gray-level distribution within the nodules of the same type. The detection combines the normalized cross-correlation template matching and a genetic optimization algorithm. The final step eliminates the false positive nodules (FPNs) using three features that robustly define the true lung nodules. Experiments with 200 CT data sets show that the proposed approach provided comparable results with respect to the experts.

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.