Max-AUC feature selection in computer-aided detection of polyps in CT colonography

Predictive Model Based on Texture Analysis of Noncontrast Cardiac Magnetic Resonance Images for the Prognostic Evaluation of Cardiac Amyloidosis

OBJECTIVES

We aimed to develop a predictive model based on textural features of noncontrast cardiac magnetic resonance (CMR) imaging for risk stratification toward adverse events in patients with cardiac amyloidosis (CA).

METHODS

A cohort of 78 patients with CA was grouped into training (n = 54) and validation (n = 24) sets at a ratio of 7:3. A total of 275 textural features were extracted from the CMR images. MaZda and a support vector machine (SVM) were used for feature selection and model construction. An SVM model incorporating radiological and textural features was built to predict endpoint events by evaluating the area under the curve.

RESULTS

In the entire cohort, 52 patients experienced major adverse cardiovascular events and 26 patients did not. By combining 2 radiological features and 8 texture features, extracted from cine and T2-weighted imaging images, the SVM model achieved area under the curves of the receiver operating characteristic and precision-recall curves of 0.930 and 0.962 in the training cohort and that of 0.867 and 0.941 in the validated cohort, respectively. The Kaplan-Meier curve of this SVM model criterion significantly stratified the CA outcomes (log-rank test, P  < 0.0001).

CONCLUSIONS

The SVM model based on radiological and textural features derived from noncontrast CMR images can be a reliable biomarker for adverse events prognostication in patients with CA.

Machine learning-based CT texture analysis in the differentiation of testicular masses

Purpose

To evaluate the ability of texture features for distinguishing between benign and malignant testicular masses, and furthermore, for identifying primary testicular lymphoma in malignant tumors and identifying seminoma in testicular germ cell tumors, respectively.

Methods

We retrospectively collected 77 patients with an abdominal and pelvic enhanced computed tomography (CT) examination and a histopathologically confirmed testicular mass from a single center. The ROI of each mass was split into two parts by the largest cross-sectional slice and deemed to be two samples. After all processing steps, three-dimensional texture features were extracted from unenhanced and contrast-enhanced CT images. Excellent reproducibility of texture features was defined as intra-class correlation coefficient ≥0.8 (ICC ≥0.8). All the groups were balanced via the synthetic minority over-sampling technique (SMOTE) method. Dimension reduction was based on pearson correlation coefficient (PCC). Before model building, minimum-redundancy maximum-relevance (mRMR) selection and recursive feature elimination (RFE) were used for further feature selection. At last, three ML classifiers with the highest cross validation with 5-fold were selected: autoencoder (AE), support vector machine(SVM), linear discriminant analysis (LAD). Logistics regression (LR) and LR-LASSO were also constructed to compare with the ML classifiers.

Results

985 texture features with ICC ≥0.8 were extracted for further feature selection process. With the highest AUC of 0.946 (P <0.01), logistics regression was proved to be the best model for the identification of benign or malignant testicular masses. Besides, LR also had the best performance in identifying primary testicular lymphoma in malignant testicular tumors and in identifying seminoma in testicular germ cell tumors, with the AUC of 0.982 (P <0.01) and 0.928 (P <0.01), respectively.

Conclusion

Until now, this is the first study that applied CT texture analysis (CTTA) to assess the heterogeneity of testicular tumors. LR model based on CTTA might be a promising non-invasive tool for the diagnosis and differentiation of testicular masses. The accurate diagnosis of testicular masses would assist urologists in correct preoperative and perioperative decision making.

Diffusion-Weighted Imaging Prior to Percutaneous Sclerotherapy of Venous Malformations—Proof of Concept Study for Prediction of Clinical Outcome

Prediction of response to percutaneous sclerotherapy in patients with venous malformations (VM) is currently not possible with baseline clinical or imaging characteristics. This prospective single-center study aimed to predict treatment outcome of percutaneous sclerotherapy as measured by quality of life (QoL) by using radiomic analysis of diffusion-weighted (dw) magnetic resonance imaging (MRI) before and after first percutaneous sclerotherapy. In all patients (n = 16) pre-interventional (PRE-) and delta (DELTA-) radiomic features (RF) were extracted from dw-MRI before and after first percutaneous sclerotherapy with ethanol gel or polidocanol foam, while QoL was assessed using the Toronto Extremity Salvage Score (TESS) and the 36-Item Short Form Survey (SF-36) health questionnaire. For selecting features that allow differentiation of clinical response, a stepwise dimension reduction was performed. Logistic regression models were fitted and selected PRE-/DELTA-RF were tested for their predictive value. QoL improved significantly after percutaneous sclerotherapy. While no common baseline patient characteristics were able to predict response to percutaneous sclerotherapy, the radiomics signature of VMs (independent PRE/DELTA-RF) revealed high potential for the prediction of clinical response after percutaneous sclerotherapy. This proof-of-concept study provides first evidence on the potential predictive value of (delta) radiomic analysis from diffusion-weighted MRI for Quality-of-Life outcome after percutaneous sclerotherapy in patients with venous malformations.

A Radiomic MRI based Nomogram for Prediction of Heart Failure with Preserved Ejection Fraction in Systemic Lupus Erythematosus Patients: Insights From a Three-Center Prospective Study

BACKGROUND

Myocardial T1 and extracellular volume (ECV) fraction values have important roles in the prognostication of heart failure with preserved ejection fraction (HFpEF). However, the traditional mean quantification of intensity levels is not sufficient.

PURPOSE

To evaluate a T1 map-based radiomic nomogram as a long-term prognosticator for HFpEF in systemic lupus erythematosus (SLE) patients.

STUDY TYPE

Prospective.

POPULATION

A total of 115 SLE patients and 50 age- and gender-matched controls.

FIELD STRENGTH/SEQUENCE

A 3.0 T scanner; cine imaging, precontrast and post-contrast T1 mapping and T2 mapping sequences.

ASSESSMENT

A radiomic nomogram was developed based on precontrast T1 mapping. Three independent readers assessed and compared the ECV value and the value of the radiomic nomogram for predicting HFpEF in SLE patients.

STATISTICAL TEST

Cox proportional hazard models, Youden index for determining cut-off values for high HFpEF risk vs. low HFpEF risk classification, Kaplan-Meier analysis, intraclass correlation (ICC), and Uno C statistic test.

RESULTS

During a median follow-up of 27 (interquartile range, 19-37) months, 31 SLE patients developed HFpEF. Patients with elevated ECV (≥31%) and a higher output (≥42.7) from the radiomic feature "S_33_sum average" of the precontrast T1 map had a significantly higher risk of developing HFpEF than those who had lower ECV (<31%) and an output <42.7. Patients with a higher "S_33_sum average" value on precontrast T1 map had a significantly increased risk for HFpEF (hazard ratio, 1.363, 95% CI, 1.130-1.645), after adjusting for covariates including ECV and LVEF. Finally, "S_33_sum average" from precontrast T1 mapping had modest but significantly incremental prognostic value over the mean ECV value (Uno C statistic comparing models, 0.860 vs. 0.835).

DATA CONCLUSION

The precontrast T1 map-based radiomic nomogram, as a measure of diffuse myocardial fibrosis was associated with HFpEF and provided modest prognostic value for predicting HFpEF in SLE patients.

EVIDENCE LEVEL

1 TECHNICAL EFFICACY: Stage 2.

The Relationship between Stress Levels Measured by a Questionnaire and the Data Obtained by Smart Glasses and Finger Pulse Oximeters among Polish Dental Students

Stress is a physical, mental, or emotional response to a change and is a significant problem in modern society. In addition to questionnaires, levels of stress may be assessed by monitoring physiological signals, such as via photoplethysmogram (PPG), electroencephalogram (EEG), electrocardiogram (ECG), electrodermal activity (EDA), facial expressions, and head and body movements. In our study, we attempted to find the relationship between the perceived stress level and physiological signals, such as heart rate (HR), head movements, and electrooculographic (EOG) signals. The perceived stress level was acquired by self-assessment questionnaires in which the participants marked their stress level before, during, and after performing a task. The heart rate was acquired with a finger pulse oximeter and the head movements (linear acceleration and angular velocity) and electrooculographic signals were recorded with JINS MEME ES_R smart glasses (JINS Holdings, Inc., Tokyo, Japan). We observed significant differences between the perceived stress level, heart rate, the power of linear acceleration, angular velocity, and EOG signals before performing the task and during the task. However, except for HR, these signals were poorly correlated with the perceived stress level acquired during the task.

Evaluation of 68Ga-PSMA-11 PET-MRI in Patients with Advanced Prostate Cancer Receiving 177Lu-PSMA-617 Therapy: A Radiomics Analysis

¹⁷⁷Lutetium PSMA-617 (Lu-PSMA) therapy in patients with metastatic castration resistant prostate cancer (mCRPC) has gained visibility through the ongoing phase III trial. The data on prediction of therapy outcome and survival out of pretherapeutic imaging parameters is still sparse. In this study, the predictive and prognostic value of radiomic features from ⁶⁸Ga-PSMA-11 PET-MRI are analyzed. In total, 21 patients with mCRPC underwent ⁶⁸Ga-PSMA-11 PET-MRI before Lu-PSMA therapy. The PET-positive tumor volume was defined and transferred to whole-body T2-, T1- and contrast-enhanced T1-weighted MRI-sequences. The radiomic features from PET and MRI sequences were extracted by using a freely available software package. For selecting features that allow differentiation of biochemical response (PSA decrease > 50%), a stepwise dimension reduction was performed. Logistic regression models were fitted, and selected features were tested for their prognostic value (overall survival) in all patients. Eight patients achieved biochemical response after Lu-PSMA therapy. Ten independent radiomic features differentiated well between responders and non-responders. The logistic regression model, including the feature interquartile range from T2-weighted images, revealed the highest accuracy (AUC = 0.83) for the prediction of biochemical response after Lu-PSMA therapy. Within the final model, patients with a biochemical response (p = 0.003) and higher T2 interquartile range values in pre-therapeutic imaging (p = 0.038) survived significantly longer. This proof-of-concept study provides first evidence on a potential predictive and prognostic value of radiomic analysis of pretherapeutic ⁶⁸Ga-PSMA-11 PET-MRI before Lu-PSMA therapy.

A quantitative validation of segmented colon in virtual colonoscopy using image moments

Background

Evaluation of segmented colon is one of the challenges in Computed Tomography Colonography (CTC). The objective of the study was to measure the segmented colon accurately using image processing techniques.

Methods

This was a retrospective study, and the Institutional Ethical clearance was obtained for the secondary dataset. The technique was tested on 85 CTC dataset. The CTC dataset of 100–120 kVp, 100 mA, and ST (Slice Thickness) of 1.25 and 2.5 mm were used for empirical testing. The initial results of the work appear in the conference proceedings. Post colon segmentation, three distance measurement techniques, and one volumetric overlap computation were applied in Euclidian space in which the distances were measured on MPR views of the segmented and unsegmented colons and the volumetric overlap calculation between these two volumes.

Results

The key finding was that the measurements on both the segmented and the unsegmented volumes remain same without much difference noticed. This was statistically proved. The results were validated quantitatively on 2D MPR images. An accuracy of 95.265±0.4551% was achieved through volumetric overlap computation. Through pairedt−test, at α=5%, statistical values were p=0.6769, and t=0.4169 which infer that there was no much significant difference.

Conclusion

The combination of different validation techniques was applied to check the robustness of colon segmentation method, and good results were achieved with this approach. Through quantitative validation, the results were accepted at α=5%.

Label Self-Advised Support Vector Machine (LSA-SVM)-Automated Classification of Foot Drop Rehabilitation Case Study

Stroke represents a major health problem in our society. One of the effects of stroke is foot drop. Foot drop (FD) is a weakness that occurs in specific muscles in the ankle and foot such as the anterior tibialis, gastrocnemius, plantaris and soleus muscles. Foot flexion and extension are normally generated by lower motor neurons (LMN). The affected muscles impact the ankle and foot in both downward and upward motions. One possible solution for FD is to investigate the movement based on the bio signal (myoelectric signal) of the muscles. Bio signal control systems like electromyography (EMG) are used for rehabilitation devices that include foot drop. One of these systems is function electrical stimulation (FES). This paper proposes new methods and algorithms to develop the performance of myoelectric pattern recognition (M-PR), to improve automated rehabilitation devices, to test these methodologies in offline and real-time experimental datasets. Label classifying is a predictive data mining application with multiple applications in the world, including automatic labeling of resources such as videos, music, images and texts. We combine the label classification method with the self-advised support vector machine (SA-SVM) to create an adapted and altered label classification method, named the label self-advised support vector machine (LSA-SVM). For the experimental data, we collected data from foot drop patients using the sEMG device, in the Metro Rehabilitation Hospital in Sydney, Australia using Ethical Approval (UTS HREC NO. ETH15-0152). The experimental results for the EMG dataset and benchmark datasets exhibit its benefits. Furthermore, the experimental results on UCI datasets indicate that LSA-SVM achieves the best performance when working together with SA-SVM and SVM. This paper describes the state-of-the-art procedures for M-PR and studies all the conceivable structures.

Cardiac MRI and Texture Analysis of Myocardial T1 and T2 Maps in Myocarditis with Acute versus Chronic Symptoms of Heart Failure

BackgroundThe establishment of a timely and correct diagnosis in heart failure-like myocarditis remains one of the most challenging in clinical cardiology.PurposeTo assess the diagnostic potential of texture analysis in heart failure-like myocarditis with comparison to endomyocardial biopsy (EMB) as the reference standard.Materials and MethodsSeventy-one study participants from the Magnetic Resonance Imaging in Myocarditis (MyoRacer) trial (ClinicalTrials.gov registration no. NCT02177630) with clinical suspicion for myocarditis and symptoms of heart failure were prospectively included (from August 2012 to May 2015) in the study. Participants underwent biventricular EMB and cardiac MRI at 1.5 T, including native T1 and T2 mapping and standard Lake Louise criteria. Texture analysis was applied on T1 and T2 maps by using an open-source software. Stepwise dimension reduction was performed for selecting features enabling the diagnosis of myocarditis. Diagnostic performance was assessed from the area under the curve (AUC) from receiver operating characteristic analyses with 10-fold cross validation.ResultsIn participants with acute heart failure-like myocarditis (n = 31; mean age, 47 years ± 17; 10 women), the texture feature GrayLevelNonUniformity from T2 maps (T2_GLNU) showed diagnostic performance similar to that of mean myocardial T2 time (AUC, 0.69 for both). The combination of mean T2 time and T2_GLNU had the highest AUC (0.76; 95% confidence interval [CI]: 0.43, 0.95), with sensitivity of 81% (25 of 31) and specificity of 71% (22 of 31). In patients with chronic heart failure-like myocarditis (n = 40; mean age, 48 years ± 13; 12 women), the histogram feature T2_kurtosis demonstrated superior diagnostic performance compared to that of all other single parameters (AUC, 0.81; 95% CI: 0.66, 0.96). The combination of the two texture features, T2_kurtosis and the GrayLevelNonUniformity from T1, had the highest diagnostic performance (AUC, 0.85; 95% CI: 0.57, 0.90; sensitivity, 90% [36 of 40]; and specificity, 72% [29 of 40]).ConclusionIn this proof-of-concept study, texture analysis applied on cardiac MRI T1 and T2 mapping delivers quantitative imaging parameters for the diagnosis of acute or chronic heart failure-like myocarditis and might be superior to Lake Louise criteria or averaged myocardial T1 or T2 values.© RSNA, 2019Online supplemental material is available for this article.See also the editorial by de Roos in this issue.

Machine learning for computer-aided polyp detection using wavelets and content-based image

The continuous growing of machine learning techniques, their capabilities improvements and the availability of data being continuously collected, recorded and updated, can enhance diagnosis stages by making it faster and more accurate than human diagnosis. In lower endoscopies procedures, most of the diagnosis relies on the capabilities and expertise of the physician. During medical training, physicians can be benefited from the assistance of algorithms able to automatically detect polyps, thus enhancing their diagnosis. In this paper, we propose a machine learning approach trained to detect polyps in lower endoscopies recordings with high accuracy and sensitivity, previously processed using wavelet transform for feature extraction. The propose system is validated using available datasets. From a set of 1132 images, our system showed a 97.9% of accuracy in diagnosing polyps, around 10% more efficient than other approaches using techniques with a low computational requirement previously published. In addition, the false positive rate was 0.03. This encouraging result can be also extended to other diagnosis.

Clear Cell Renal Cell Carcinoma: Machine Learning-Based Quantitative Computed Tomography Texture Analysis for Prediction of Fuhrman Nuclear Grade

OBJECTIVE

To evaluate the performance of quantitative computed tomography (CT) texture analysis using different machine learning (ML) classifiers for discriminating low and high nuclear grade clear cell renal cell carcinomas (cc-RCCs).

MATERIALS AND METHODS

This retrospective study included 53 patients with pathologically proven 54 cc-RCCs (31 low-grade [grade 1 or 2]; 23 high-grade [grade 3 or 4]). In one patient, two synchronous cc-RCCs were included in the analysis. Mean age was 57.5 years. Thirty-four (64.1%) patients were male and 19 were female (35.9%). Mean tumour size based on the maximum diameter was 57.4 mm (range, 16-145 mm). Forty patients underwent radical nephrectomy and 13 underwent partial nephrectomy. Following pre-processing steps, two-dimensional CT texture features were extracted using portal-phase contrast-enhanced CT. Reproducibility of texture features was assessed with the intra-class correlation coefficient (ICC). Nested cross-validation with a wrapper-based algorithm was used in feature selection and model optimisation. The ML classifiers were support vector machine (SVM), multilayer perceptron (MLP, a sort of neural network), naïve Bayes, k-nearest neighbours, and random forest. The performance of the classifiers was compared by certain metrics.

RESULTS

Among 279 texture features, 241 features with an ICC equal to or higher than 0.80 (excellent reproducibility) were included in the further feature selection process. The best model was created using SVM. The selected subset of features for SVM included five co-occurrence matrix (ICC range, 0.885-0.998), three run-length matrix (ICC range, 0.889-0.992), one gradient (ICC = 0.998), and four Haar wavelet features (ICC range, 0.941-0.997). The overall accuracy, sensitivity (for detecting high-grade cc-RCCs), specificity (for detecting high-grade cc-RCCs), and overall area under the curve of the best model were 85.1%, 91.3%, 80.6%, and 0.860, respectively.

CONCLUSIONS

The ML-based CT texture analysis can be a useful and promising non-invasive method for prediction of low and high Fuhrman nuclear grade cc-RCCs.

KEY POINTS

• Based on the percutaneous biopsy literature, ML-based CT texture analysis has a comparable predictive performance with percutaneous biopsy. • Highest predictive performance was obtained with use of the SVM. • SVM correctly classified 85.1% of cc-RCCs in terms of nuclear grade, with an AUC of 0.860.

Cardiac MRI Texture Analysis of T1 and T2 Maps in Patients with Infarctlike Acute Myocarditis

Purpose To assess the diagnostic potential of texture analysis applied to T1 and T2 maps obtained with cardiac MRI for the diagnosis of acute infarctlike myocarditis. Materials and Methods This prospective study from August 2012 to May 2015 included 39 participants (overall mean age ± standard deviation, 34.7 years ± 12.2 [range, 18-63 years]; mean age of women, 46.1 years ± 10.8 [range, 24-63 years]; mean age of men, 29.8 years ± 9.2 [range, 18-56 years]) from the Magnetic Resonance Imaging in Myocarditis (MyoRacer) trial with clinical suspicion of acute myocarditis and infarctlike presentation. Participants underwent biventricular endomyocardial biopsy, cardiac catheterization, and cardiac MRI at 1.5 T, in which native T1 and T2 mapping as well as Lake Louise criteria (LLC) were assessed. Texture analysis was applied on T1 and T2 maps by using a freely available software package. Stepwise dimension reduction and texture feature selection was performed for selecting features enabling the diagnosis of myocarditis by using endomyocardial biopsy as the reference standard. Results Endomyocardial biopsy confirmed the diagnosis of acute myocarditis in 26 patients, whereas 13 participants had no signs of acute inflammation. Mean T1 and T2 values and LLC showed a low diagnostic performance, with area under the curve in receiver operating curve analyses as follows: 0.65 (95% confidence interval [CI]: 0.45, 0.85) for T1, 0.67 (95% CI: 0.49, 0.85) for T2, and 0.62 (95% CI: 0.42, 0.79) for LLC. Combining the texture features T2 run-length nonuniformity and gray-level nonuniformity resulted in higher diagnostic performance with an area under the curve of 0.88 (95% CI: 0.73, 1.00) (P < .001) and a sensitivity and specificity of 89% [95% CI: 81%, 93%] and 92% [95% CI: 77%, 93%], respectively. Conclusion Texture analysis of T2 maps shows high sensitivity and specificity for the diagnosis of acute infarctlike myocarditis. © RSNA, 2018 Online supplemental material is available for this article.

Codebook-based electrooculography data analysis towards cognitive activity recognition

With the advancement in mobile/wearable technology, people started to use a variety of sensing devices to track their daily activities as well as health and fitness conditions in order to improve the quality of life. This work addresses an idea of eye movement analysis, which due to the strong correlation with cognitive tasks can be successfully utilized in activity recognition. Eye movements are recorded using an electrooculographic (EOG) system built into the frames of glasses, which can be worn more unobtrusively and comfortably than other devices. Since the obtained information is low-level sensor data expressed as a sequence representing values in constant intervals (100 Hz), the cognitive activity recognition problem is formulated as sequence classification. However, it is unclear what kind of features are useful for accurate cognitive activity recognition. Thus, a machine learning algorithm like a codebook approach is applied, which instead of focusing on feature engineering is using a distribution of characteristic subsequences (codewords) to describe sequences of recorded EOG data, where the codewords are obtained by clustering a large number of subsequences. Further, statistical analysis of the codeword distribution results in discovering features which are characteristic to a certain activity class. Experimental results demonstrate good accuracy of the codebook-based cognitive activity recognition reflecting the effective usage of the codewords.

Texture analysis and machine learning of non-contrast T1-weighted MR images in patients with hypertrophic cardiomyopathy-Preliminary results

PURPOSE

To test in a first proof-of-concept study whether texture analysis (TA) allows for the detection of myocardial tissue alterations in hypertrophic cardiomyopathy (HCM) on non-contrast T1-weighted cardiac magnetic resonance (CMR) images using machine learning based approaches.

METHODS

This retrospective, IRB-approved study included 32 patients with known HCM. Thirty patients with normal CMR served as controls. Regions-of-interest for TA encompassing the left ventricle were drawn on short-axis non-contrast T1-weighted images using a freely available software package. Step-wise dimension reduction and texture feature selection was performed for selecting features enabling the detection of myocardial tissue alterations in HCM patients on non-contrast T1-weighted CMR images.

RESULTS

Comparing HCM patients and controls, four texture features were identified showing significant differences between groups (Grey-level Non-uniformity [GLevNonU]: 74 ± 17 vs. 38 ± 9, p < .001; Energy of wavelet coefficients in low-frequency sub-bands [WavEnLL]: 58 ± 5 vs. 48 ± 10, p < .001; Fraction: 0.70 ± 0.07 vs. 0.78 ± 0.05, p < .001; Sum Average: 16.6 ± 0.4 vs. 17.0 ± 0.5, p = .007). A model containing the single parameter GLevNonU proved to be the best for differentiating between HCM patients and controls with a sensitivity/specificity of 91%/93%. A cut-off of GLevNonU ≥46 allowed for distinguishing HCM patients from controls with a sensitivity/specificity of 94%/90%. Even in patients without late gadolinium enhancement (LGE), the defined cut-off led to a differentiation of LGE- patients from healthy controls with 100% sensitivity and 90% specificity.

CONCLUSIONS

TA on non-contrast T1-weighted images allows for the detection of myocardial tissue alterations in the setting of HCM with excellent accuracy, delivering potential novel parameters for a non-contrast assessment of myocardial texture alterations.

Influence Analysis for the Area Under the Receiver Operating Characteristic Curve

Classification measures play essential roles in the assessment and construction of classifiers. Hence, determining how to prevent these measures from being affected by individual observations has become an important problem. In this paper, we propose several indexes based on the influence function and the concept of local influence to identify influential observations that affect the estimate of the area under the receiver operating characteristic curve (AUC), an important and commonly used measure. Cumulative lift charts are also used to equipoise the disagreements among the proposed indexes. Both the AUC indexes and the graphical tools only rely on the classification scores, and both are applicable to classifiers that can produce real-valued classification scores. A real data set is used for illustration.

Subacute and Chronic Left Ventricular Myocardial Scar: Accuracy of Texture Analysis on Nonenhanced Cine MR Images

Purpose To test whether texture analysis (TA) allows for the diagnosis of subacute and chronic myocardial infarction (MI) on noncontrast material-enhanced cine cardiac magnetic resonance (MR) images. Materials and Methods In this retrospective, institutional review board-approved study, 120 patients who underwent cardiac MR imaging and showed large transmural (volume of enhancement on late gadolinium enhancement [LGE] images >20%, n = 72) or small (enhanced volume ≤20%, n = 48) subacute or chronic ischemic scars were included. Sixty patients with normal cardiac MR imaging findings served as control subjects. Regions of interest for TA encompassing the left ventricle were drawn by two blinded, independent readers on cine images in end systole by using a freely available software package. Stepwise dimension reduction and texture feature selection based on reproducibility, machine learning, and correlation analyses were performed for selecting features, enabling the diagnosis of MI on nonenhanced cine MR images by using LGE imaging as the standard of reference. Results Five independent texture features allowed for differentiation between ischemic scar and normal myocardium on cine MR images in both subgroups: Teta1, Perc.01, Variance, WavEnHH.s-3, and S(5,5)SumEntrp (in patients with large MI: all P values < .001; in patients with small MI: Teta1 and Perc.01, P < .001; Variance, P = .026; WavEnHH.s-3, P = .007; S[5,5]SumEntrp, P = .045). Multiple logistic regression models revealed that combining the features Teta1 and Perc.01 resulted in the highest accuracy for diagnosing large and small MI on cine MR images, with an area under the curve of 0.93 and 0.92, respectively. Conclusion This proof-of-concept study indicates that TA of nonenhanced cine MR images allows for the diagnosis of subacute and chronic MI with high accuracy. ^© RSNA, 2017 Online supplemental material is available for this article.

Measurement of smaller colon polyp in CT colonography images using morphological image processing

PURPOSE

Automated measurement of the size and shape of colon polyps is one of the challenges in Computed tomography colonography (CTC). The objective of this retrospective study was to improve the sensitivity and specificity of smaller polyp measurement in CTC using image processing techniques.

METHODS

A domain knowledge-based method has been implemented with hybrid method of colon segmentation, morphological image processing operators for detecting the colonic structures, and the decision-making system for delineating the smaller polyp-based on a priori knowledge.

RESULTS

The method was applied on 45 CTC dataset. The key finding was that the smaller polyps were accurately measured. In addition to 6-9 mm range, polyps of even <5 mm were also detected. The results were validated qualitatively and quantitatively using both 2D MPR and 3D view. Implementation was done on a high-performance computer with parallel processing. It takes [Formula: see text] min for measuring the smaller polyp in a dataset of 500 CTC images. With this method, [Formula: see text] and [Formula: see text] were achieved.

CONCLUSIONS

The domain-based approach with morphological image processing has given good results. The smaller polyps were measured accurately which helps in making right clinical decisions. Qualitatively and quantitatively the results were acceptable when compared to the ground truth at [Formula: see text].

Texture analysis in radiology: Does the emperor have no clothes?

Texture analysis is more and more frequently used in radiology research. Is this a new technology, and if not, what has changed? Is texture analysis the great diagnostic and prognostic tool we have been searching for in radiology? This commentary answers these questions and places texture analysis into its proper perspective.

A CAD of fully automated colonic polyp detection for contrasted and non-contrasted CT scans

PURPOSE

Computer-aided detection (CAD) systems are developed to help radiologists detect colonic polyps over CT scans. It is possible to reduce the detection time and increase the detection accuracy rates by using CAD systems. In this paper, we aimed to develop a fully integrated CAD system for automated detection of polyps that yields a high polyp detection rate with a reasonable number of false positives.

METHODS

The proposed CAD system is a multistage implementation whose main components are: automatic colon segmentation, candidate detection, feature extraction and classification. The first element of the algorithm includes a discrete segmentation for both air and fluid regions. Colon-air regions were determined based on adaptive thresholding, and the volume/length measure was used to detect air regions. To extract the colon-fluid regions, a rule-based connectivity test was used to detect the regions belong to the colon. Potential polyp candidates were detected based on the 3D Laplacian of Gaussian filter. The geometrical features were used to reduce false-positive detections. A 2D projection image was generated to extract discriminative features as the inputs of an artificial neural network classifier.

RESULTS

Our CAD system performs at 100% sensitivity for polyps larger than 9 mm, 95.83% sensitivity for polyps 6-10 mm and 85.71% sensitivity for polyps smaller than 6 mm with 5.3 false positives per dataset. Also, clinically relevant polyps ([Formula: see text]6 mm) were identified with 96.67% sensitivity at 1.12 FP/dataset.

CONCLUSIONS

To the best of our knowledge, the novel polyp candidate detection system which determines polyp candidates with LoG filters is one of the main contributions. We also propose a new 2D projection image calculation scheme to determine the distinctive features. We believe that our CAD system is highly effective for assisting radiologist interpreting CT.

Quantitative radiology: automated measurement of polyp volume in computed tomography colonography using Hessian matrix-based shape extraction and volume growing

BACKGROUND

Current measurement of the single longest dimension of a polyp is subjective and has variations among radiologists. Our purpose was to develop a computerized measurement of polyp volume in computed tomography colonography (CTC).

METHODS

We developed a 3D automated scheme for measuring polyp volume at CTC. Our scheme consisted of segmentation of colon wall to confine polyp segmentation to the colon wall, extraction of a highly polyp-like seed region based on the Hessian matrix, a 3D volume growing technique under the minimum surface expansion criterion for segmentation of polyps, and sub-voxel refinement and surface smoothing for obtaining a smooth polyp surface. Our database consisted of 30 polyp views (15 polyps) in CTC scans from 13 patients. Each patient was scanned in the supine and prone positions. Polyp sizes measured in optical colonoscopy (OC) ranged from 6-18 mm with a mean of 10 mm. A radiologist outlined polyps in each slice and calculated volumes by summation of volumes in each slice. The measurement study was repeated 3 times at least 1 week apart for minimizing a memory effect bias. We used the mean volume of the three studies as "gold standard".

RESULTS

Our measurement scheme yielded a mean polyp volume of 0.38 cc (range, 0.15-1.24 cc), whereas a mean "gold standard" manual volume was 0.40 cc (range, 0.15-1.08 cc). The "gold-standard" manual and computer volumetric reached excellent agreement (intra-class correlation coefficient =0.80), with no statistically significant difference [P (F≤f) =0.42].

CONCLUSIONS

We developed an automated scheme for measuring polyp volume at CTC based on Hessian matrix-based shape extraction and volume growing. Polyp volumes obtained by our automated scheme agreed excellently with "gold standard" manual volumes. Our fully automated scheme can efficiently provide accurate polyp volumes for radiologists; thus, it would help radiologists improve the accuracy and efficiency of polyp volume measurements in CTC.

Feature selection in classification of eye movements using electrooculography for activity recognition

Activity recognition is needed in different requisition, for example, reconnaissance system, patient monitoring, and human-computer interfaces. Feature selection plays an important role in activity recognition, data mining, and machine learning. In selecting subset of features, an efficient evolutionary algorithm Differential Evolution (DE), a very efficient optimizer, is used for finding informative features from eye movements using electrooculography (EOG). Many researchers use EOG signals in human-computer interactions with various computational intelligence methods to analyze eye movements. The proposed system involves analysis of EOG signals using clearness based features, minimum redundancy maximum relevance features, and Differential Evolution based features. This work concentrates more on the feature selection algorithm based on DE in order to improve the classification for faultless activity recognition.