Evaluating and Improving Automatic Sleep Spindle Detection by Using Multi-Objective Evolutionary Algorithms.

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.

A robust hair segmentation and removal approach for clinical images of skin lesions.

Artifacts such as hair are major obstacles to automatic segmentation of pigmented skin lesion images for computer-aided diagnosis systems. It is even more challenging to process clinical images taken by a regular digital camera, where the shadows of the skin texture may mimic hair-like curvilinear structures. In this study, we examined the popular DullRazor software with a dataset of 20 clinical images. The software, specifically designed for dermoscopic images, was unable to remove fine hairs or hairs in the shade. Alternatively, we proposed using conventional matched filters to enhance curvilinear structures. The more complicate hair intersection patterns, which were known to generate low matched filtering responses, were recovered by using region growing algorithms from nearby detected hair segments with linear discriminant analysis (LDA) based on a color similarity criterion. The preliminary results indicated the proposed method was able to remove more fine hairs and hairs in the shade, and lower false hair detection rate by 58% (from 0.438 to 0.183) as compared to the DullRazor's approach.

Using standard nonenhanced axial scans for cerebral CT angiography bone elimination: feasibility study.

OBJECTIVE: To investigate the feasibility of using standard nonenhanced axial-mode scans as precontrast scans for bone elimination in cerebral CT angiography (CTA). MATERIALS AND METHODS: A consecutive dataset of 32 patients who had both cerebral nonenhanced CT (NECT) (scanned in axial mode) and subtraction CTA (scanned in helical mode) examinations between April and August 2008 were retrospectively analyzed. For each patient, both axial- and helical-mode, NECT scans were processed by using the matched mask bone elimination (MMBE) method. Bone masks generated from axial- and helical-mode NECT scans were quantitatively compared by using overlapping analyses. The diagnostic quality and noise level of the resultant, maximum intensity projection, images by using 2 different bone masks were visually evaluated by 2 neuroradiologists independently using a 5-point scale (inferior, 1; worse, 2; equivalent, 3; better, 4; superior, 5). The effective doses to patients were estimated by using a dose-length product method. RESULTS: Of the 28 (87.5%) patients without intrascan movements, overlap rates between axial- and helical-mode bone masks ranged from 99.2% to 99.9% (mean, 99.7% +/- 0.2%). The mean diagnostic quality and noise level scores of resultant maximum intensity projection images given by 2 neuroradiologists were 3.0 +/- 0.3 and 2.5 +/- 0.5, respectively. The effective dose to patients with a routine brain CTA examination can be reduced from 1.16 to 0.78 mSv (16 cm, field-of-view) by using the proposed method if standard axial-mode NECT scans of the head are readily available. CONCLUSION: We found that using standard axial-mode NECT scans for bone elimination in helical-mode CTA is feasible. This method can further lower radiation dose without compromising the diagnostic quality.