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Colonic polyps appear like elliptical protrusions on the inner wall of the colon. Curvature based features for colonic polyp detection have proved to be successful in several computer-aided diagnostic CT colonography (CTC) systems. Some simple thresholds are set for those features for creating initial polyp candidates, sophisticated classification scheme are then applied on these polyp candidates to reduce false positives. There are two objective functions, the number of missed polyps and false positive rate, that need to be minimized when setting those thresholds. These two objectives conflict and it is usually difficult to optimize them both by a gradient search. In this paper, we utilized a multiobjective evolutionary method, the Strength Pareto Evolutionary Algorithm (SPEA2), 1 to optimize those thresholds. SPEA2 incorporates the concept of Pareto dominance and applies genetic techniques to evolve individual solutions to the Pareto front. The SPEA2 algorithm was applied to colon CT images from 27 patients each having a prone and a supine scan. There are 40 colonoscopically confirmed polyps resulting in 72 positive detections in CTC reading. The results obtained by SPEA2 were compared with those obtained by our old system, where an appropriate value was set for each of those thresholds by a histogram examination method. If we keep the sensitivity the same as that of our old system, the SPEA2 algorithm reduced false positive rate by 76.4% from average false positive 55.6 to 13.3 per data set. If the false positive rate is kept the same for both systems, SPEA2 increased the sensitivity by 13.1% from 53 to 61 among 72 ground truth detections.

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Sleep spindles are brief bursts of brain activity in the sigma frequency range (11–16 Hz) measured by electroencephalography (EEG) mostly during non-rapid eye movement (NREM) stage 2 sleep. These oscillations are of great biological and clinical interests because they potentially play an important role in identifying and characterizing the processes of various neurological disorders. Conventionally, sleep spindles are identified by expert sleep clinicians via visual inspection of EEG signals. The process is laborious and the results are inconsistent among different experts. To resolve the problem, numerous computerized methods have been developed to automate the process of sleep spindle identification. Still, the performance of these automated sleep spindle detection methods varies inconsistently from study to study. There are two reasons: (1) the lack of common benchmark databases, and (2) the lack of commonly accepted evaluation metrics. In this study, we focus on tackling the second problem by proposing to evaluate the performance of a spindle detector in a multi-objective optimization context and hypothesize that using the resultant Pareto fronts for deriving evaluation metrics will improve automatic sleep spindle detection. We use a popular multi-objective evolutionary algorithm (MOEA), the Strength Pareto Evolutionary Algorithm (SPEA2), to optimize six existing frequency-based sleep spindle detection algorithms. They include three Fourier, one continuous wavelet transform (CWT), and two Hilbert-Huang transform (HHT) based algorithms. We also explore three hybrid approaches. Trained and tested on open-access DREAMS and MASS databases, two new hybrid methods of combining Fourier with HHT algorithms show significant performance improvement with F1-scores of 0.726–0.737.

ObjectivesTo investigate the feasibility of manual segmentation by users of different backgrounds in a previously developed multifeature computer-aided diagnosis (CADx) system to classify melanocytic and non-melanocytic skin lesions based on conventional digital photographic images.MethodsIn total, 347 conventional photographs of melanocytic and non-melanocytic skin lesions were retrospectively reviewed, and manually segmented by two groups of physicians, dermatologists and general practitioners, as well as by an automated segmentation software program, JSEG. The performance of CADx based on inputs from these two groups of physicians and that of the JSEG program was compared using feature agreement analysis.ResultsThe estimated area under the receiver operating characteristic curve for classification of benign or malignant skin lesions based were comparable on individual segmentation by the gold standard (0.893, 95% CI 0.856 to 0.930), dermatologists (0.886, 95% CI 0.863 to 0.908), general practitioners (0.883, 95% CI 0.864 to 0.903) and JSEG (0.856, 95% CI 0.812 to 0.899). The agreement in the malignancy probability scores among the physicians was excellent (intraclass correlation coefficient: 0.91). By selecting an optimal cut-off value of malignancy probability score, the sensitivity and specificity were 80.07% and 81.47% for dermatologists and 79.90% and 80.20% for general practitioners.ConclusionsThis study suggests that manual segmentation by general practitioners is feasible in the described CADx system for classifying benign and malignant skin lesions.