Jamshid Dehmeshki

Shape-Based Computer-Aided Detection of Lung Nodules in Thoracic CT Images

In this paper, a new computer tomography (CT) lung nodule computer-aided detection (CAD) method is proposed for detecting both solid nodules and ground-glass opacity (GGO) nodules (part solid and nonsolid). This method consists of several steps. First, the lung region is segmented from the CT data using a fuzzy thresholding method. Then, the volumetric shape index map, which is based on local Gaussian and mean curvatures, and the ldquodotrdquo map, which is based on the eigenvalues of a Hessian matrix, are calculated for each voxel within the lungs to enhance objects of a specific shape with high spherical elements (such as nodule objects). The combination of the shape index (local shape information) and ldquodotrdquo features (local intensity dispersion information) provides a good structure descriptor for the initial nodule candidates generation. Antigeometric diffusion, which diffuses across the image edges, is used as a preprocessing step. The smoothness of image edges enables the accurate calculation of voxel-based geometric features. Adaptive thresholding and modified expectation-maximization methods are employed to segment potential nodule objects. Rule-based filtering is first used to remove easily dismissible nonnodule objects. This is followed by a weighted support vector machine (SVM) classification to further reduce the number of false positive (FP) objects. The proposed method has been trained and validated on a clinical dataset of 108 thoracic CT scans using a wide range of tube dose levels that contain 220 nodules (185 solid nodules and 35 GGO nodules) determined by a ground truth reading process. The data were randomly split into training and testing datasets. The experimental results using the independent dataset indicate an average detection rate of 90.2%, with approximately 8.2 FP/scan. Some challenging nodules such as nonspherical nodules and low-contrast part-solid and nonsolid nodules were identified, while most tissues such as blood vessels were excluded. The methods high detection rate, fast computation, and applicability to different imaging conditions and nodule types shows much promise for clinical applications.

Segmentation of Pulmonary Nodules in Thoracic CT Scans: A Region Growing Approach

This paper presents an efficient algorithm for segmenting different types of pulmonary nodules including high and low contrast nodules, nodules with vasculature attachment, and nodules in the close vicinity of the lung wall or diaphragm. The algorithm performs an adaptive sphericity oriented contrast region growing on the fuzzy connectivity map of the object of interest. This region growing is operated within a volumetric mask which is created by first applying a local adaptive segmentation algorithm that identifies foreground and background regions within a certain window size. The foreground objects are then filled to remove any holes, and a spatial connectivity map is generated to create a 3-D mask. The mask is then enlarged to contain the background while excluding unwanted foreground regions. Apart from generating a confined search volume, the mask is also used to estimate the parameters for the subsequent region growing, as well as for repositioning the seed point in order to ensure reproducibility. The method was run on 815 pulmonary nodules. By using randomly placed seed points, the approach was shown to be fully reproducible. As for acceptability, the segmentation results were visually inspected by a qualified radiologist to search for any gross misssegmentation. 84% of the first results of the segmentation were accepted by the radiologist while for the remaining 16% nodules, alternative segmentation solutions that were provided by the method were selected.

The normal appearing grey matter in primary progressive multiple sclerosis: a magnetisation transfer imaging study

Abstract.Background: In 10–15 % of patients with multiple sclerosis (MS), the clinical course is characterized by slow progression in disability without relapses (primary progressive (PP) MS). The mechanism of disability in this form of MS is poorly understood. Using magnetization transfer ratio (MTR) imaging, we investigated normal appearing white matter (NAWM) and normal appearing grey matter (NAGM) in PPMS and explored the relationship of MTR measures with disability. Methods: Thirty patients with PPMS and 30 age matched controls had spin echo based MTR imaging to study lesions and normal appearing tissues. The brain was segmented into NAWM and NAGM using SPM99 with lesions segmented using a semiautomated local thresholding technique. A 75 % probability threshold for classification of NAWM and NAGM was used to diminish partial volume effects. From normalized histograms of MTR intensity values, six MTR parameters were measured. Mean lesion MTR and T2 lesion volume were also measured. Disability was assessed using Kurtzkes expanded disability status scale (EDSS). Results: Compared with controls, patients exhibited a significant reduction in mean NAWM (p = 0.001) and NAGM (p = 0.004) MTR. Spearmans rank correlation of EDSS with the six MTR parameters in NAWM and NAGM, mean lesion MTR, and T2 lesion volume, was only significant with mean NAGM MTR (r = −0.41, p = 0.02), the 25th percentile of NAGM MTR intensity (r = −0.37, p = 0.05), and T2 lesion volume (r = 0.39, p = 0.04). Multiple regression analysis of the relationship between EDSS and 4 MR parameters representing each tissue type (mean NAWM MTR, mean NAGM MTR, mean lesion MTR, T2 lesion volume) showed that the association of EDSS with mean NAGM MTR remained significant. Conclusions: There appear to be significant abnormalities in the NAGM in PP MS. Further investigation of the pathological basis and functional significance of grey matter abnormality in PPMS is warranted.

Automated detection of lung nodules in CT images using shape-based genetic algorithm.

A shape-based genetic algorithm template-matching (GATM) method is proposed for the detection of nodules with spherical elements. A spherical-oriented convolution-based filtering scheme is used as a pre-processing step for enhancement. To define the fitness function for GATM, a 3D geometric shape feature is calculated at each voxel and then combined into a global nodule intensity distribution. Lung nodule phantom images are used as reference images for template matching. The proposed method has been validated on a clinical dataset of 70 thoracic CT scans (involving 16,800 CT slices) that contains 178 nodules as a gold standard. A total of 160 nodules were correctly detected by the proposed method and resulted in a detection rate of about 90%, with the number of false positives at approximately 14.6/scan (0.06/slice). The high-detection performance of the method suggested promising potential for clinical applications.

Normal-appearing brain tissue MTR histograms in clinically isolated syndromes suggestive of MS

Abstract—Segmented normal-appearing brain tissue (NABT) was investigated in 40 patients with a recent onset and 13 patients with a remote onset of a clinically isolated syndrome (CIS) using magnetization transfer ratio (MTR) histograms. Abnormalities were present in patients with a high risk for MS (recent onset and T2-weighted lesions present) and in those with a low risk for relapse (recent onset without T2-weighted lesions). Similar mild NABT abnormality was present with CIS and no further disease activity 14 years later. NABT MTR abnormality in CIS may indicate susceptibility to demyelination but not to disease progression.

Computer-Assisted Reader Software Versus Expert Reviewers for Polyp Detection on CT Colonography

OBJECTIVE The purpose of our study was to assess the sensitivity of computer-assisted reader (CAR) software for polyp detection compared with the performance of expert reviewers. MATERIALS AND METHODS A library of colonoscopically validated CT colonography cases were collated and separated into training and test sets according to the time of accrual. Training data sets were annotated in consensus by three expert radiologists who were aware of the colonoscopy report. A subset of 45 training cases containing 100 polyps underwent batch analysis using ColonCAR version 1.2 software to determine the optimum polyp enhancement filter settings for polyp detection. Twenty-five consecutive positive test data sets were subsequently interpreted individually by each expert, who was unaware of the endoscopy report, and before generation of the annotated reference via an unblinded consensus interpretation. ColonCAR version 1.2 software was applied to the test cases, at optimized polyp enhancement filter settings, to determine diagnostic performance. False-positive findings were classified according to importance. RESULTS The 25 test cases contained 32 nondiminutive polyps ranging from 6 to 35 mm in diameter. The ColonCAR version 1.2 software identified 26 (81%) of 32 polyps compared with an average sensitivity of 70% for the expert reviewers. Eleven (92%) of 12 polyps > or = 10 mm were detected by ColonCAR version 1.2. All polyps missed by experts 1 (n = 4) and 2 (n = 3) and 12 (86%) of 14 polyps missed by expert 3 were detected by ColonCAR version 1.2. The median number of false-positive highlights per case was 13, of which 91% were easily dismissed. CONCLUSION ColonCAR version 1.2 is sensitive for polyp detection, with a clinically acceptable false-positive rate. ColonCAR version 1.2 has a synergistic effect to the reviewer alone, and its standalone performance may exceed even that of experts.

Disability in multiple sclerosis is related to normal appearing brain tissue MTR histogram abnormalities

Background: Magnetization transfer ratio (MTR) histogram analysis provides a global measure of disease burden in multiple sclerosis (MS). MTR abnormalities in normal appearing brain tissue (NABT) provide quantitative information on the extent of tissue damage undetected by conventional T2-weighted (T2W) magnetic resonance imaging (MRI). A ims: 1) To compare the MTR histograms from NABT across a broad spectrum of relapse onset MS patients, including relapsing-remitting (RR) MS (including newly diagnosed and benign subgroups) and secondary progressive (SP) MS. 2) To determine the relationship between clinical disability and NA BT MTR histograms. Methods: 2D spin echo magnetization transfer imaging was performed on 70 RRMS and 25 SPMS patients and compared with 63 controls. MTR histograms were acquired for NA BT after extracting lesions and cerebrospinal fluid (C SF). T2W images were used to measure the brain parenchymal fraction (BPF) and T2 lesion load. Results: MS patients had a disease duration ranging from 0.5 to 37 years and an Expanded Disability Status Scale (EDSS) score ranging from 0 to 8.5. There was a significant decrease in NA BT mean MTR (± standard deviation) compared with controls (33.07 pu± 1.06 versus 34.26 pu± 0.47; P < 0.001) with an effect size of 2.56. The reductio n in NA BT mean MTR varied among patient groups from 4.9% for SPMS, 3% for all RRMS, 2.7% for early RRMS and 2.5% for benign MS, compared with controls. NA BT mean MTR correlated significantly with T2 lesion load (r = -0.82) and BPF (r =0.58). EDSS score correlated with NA BT mean MTR (r = -0.43), BPF (r = -0.33) and with T2 lesion load (r =0.59). Multivariate analysis using NA BT MTR peak height, T2 lesion load and BPF combined only accounted for 38% of the variance in the EDSS (r =0.62; P <0.001). Disease duration accounted for an additional 14% of variance in the EDSS (r =0.72; P <0.001). Conclusions: There is evidence of diffuse abnormalities in NA BT in addition to global brain atrophy in relapse onset MS patients, including those with recently diagnosed RRMS and benign MS. The abnormalities are greatest in patients with the more disabling SPMS. A trophy, NA BT and lesion abnormalities are all partly correlated; the processes marked by these MR measures all contribute to disability in MS, providing complementary information relevant to the complex pathological processes that occur in MS.

An Automatic Approach for Learning and Tuning Gaussian Interval Type-2 Fuzzy Membership Functions Applied to Lung CAD Classification System

The potential of type-2 fuzzy sets to manage high levels of uncertainty in the subjective knowledge of experts or of numerical information has focused on control and pattern classification systems in recent years. One of the main challenges in designing a type-2 fuzzy logic system (FLS) is how to estimate the parameters of the type-2 fuzzy membership function (T2MF) and the footprint of uncertainty (FOU) from imperfect and noisy datasets. This paper presents an automatic approach to learn and tune Gaussian interval type-2 membership functions (IT2MFs) with application to multidimensional pattern classification problems. T2MFs and their FOUs are tuned according to the uncertainties in the training dataset by a combination of genetic algorithm (GA) and cross-validation techniques. In our GA-based approach, the structure of the chromosome has fewer genes than other GA methods, and chromosome initialization is more precise. The proposed approach addresses the application of the interval type-2 fuzzy logic system (IT2FLS) for the problem of nodule classification in a lung computer-aided detection system. The designed IT2FLS is compared with its type-1 fuzzy logic system (T1FLS) counterpart. The results demonstrate that the IT2FLS outperforms the T1FLS by more than 30% in terms of classification accuracy.

Automated detection of proliferative diabetic retinopathy using a modified line operator and dual classification

Proliferative diabetic retinopathy (PDR) is a condition that carries a high risk of severe visual impairment. The hallmark of PDR is neovascularisation, the growth of abnormal new vessels. This paper describes an automated method for the detection of new vessels in retinal images. Two vessel segmentation approaches are applied, using the standard line operator and a novel modified line operator. The latter is designed to reduce false responses to non-vessel edges. Both generated binary vessel maps hold vital information which must be processed separately. This is achieved with a dual classification system. Local morphology features are measured from each binary vessel map to produce two separate feature sets. Independent classification is performed for each feature set using a support vector machine (SVM) classifier. The system then combines these individual classification outcomes to produce a final decision. Sensitivity and specificity results using a dataset of 60 images are 0.862 and 0.944 respectively on a per patch basis and 1.00 and 0.90 respectively on a per image basis.

Magnetisation transfer ratio histogram analysis of primary progressive and other multiple sclerosis subgroups

INTRODUCTION Global magnetisation transfer ration (MTR) histogram analysis in the brain offers a method for evaluating pathological change both as a result of lesions and microscopic changes in normal appearing tissues. METHODS 39 controls and 83 MS patients (46 primary progressive, 11 benign, 10 relapsing-remitting, 16 secondary progressive) were studied to explore the relationship of six conventional MTR histogram parameters with MS clinical subgroups and disability. Principal component (PC) analysis, which makes use of all the histogram data, was also used to examine the relationship between the MTR histogram and disability. RESULTS When primary progressive patients were compared to controls, there were abnormalities of average MTR, and MTR at the 25th, 50th and 75th percentile. Disabled relapsing onset patients exhibited abnormalities in the same four parameters. Benign and nondisabled relapsing onset patients exhibited no significant abnormalities. Modest correlations were observed between disability and individual MTR parameters in relapse onset but not primary progressive patients--PC analysis revealed stronger and significant associations with disability in both subgroups. (r=0.40 for primary progressive and r=0.51 for relapsing onset). CONCLUSION A number of MTR parameters are abnormal in primary progressive MS. MTR abnormalities are seen in disabled patients, whether of relapsing or primary progressive onset. The improved correlation with disability obtained by PC analysis suggests a useful role of this method for following clinically relevant pathological changes depicted in the MTR histogram.