We present a constrained spectral unmixing method to remove highlight from a single spectral image. In the constrained spectral unmixing method, the constraints have been imposed so that all the fractions of diffuse and highlight reflection sum up to 1 and are positive. As a result, the spectra of the diffuse image are always positive. The spectral power distribution (SPD) of the light source has been used as the pure highlight spectrum. The pure diffuse spectrum of the measured spectrum has been chosen from the set of diffuse spectra. The pure diffuse spectrum has a minimum angle among the angles calculated between spectra from a set of diffuse spectra and the measured spectrum projected onto the subspace orthogonal to the SPD of the light source. The set of diffuse spectra has been collected by an automated target generation program from the diffuse part in the image. Constrained energy minimization in a finite impulse response linear filter has been used to detect the highlight and diffuse parts in the image. Results by constrained spectral unmixing have been compared with results by the orthogonal subspace projection (OSP) method [Proceedings of International Conference on Pattern Recognition (2006), pp. 812-815] and probabilistic principal component analysis (PPCA) [Proceedings of the 4th WSEAS International Conference on Signal Processing, Robotics and Automation (2005), paper 15]. Constrained spectral unmixing outperforms OSP and PPCA in the visual assessment of the diffuse results. The highlight removal method by constrained spectral unmixing is suitable for spectral images.
In this letter, we evaluated the pixel-level and plotlevel tree species classification of Scots Pine, Norway Spruce, and deciduous birch in a boreal forest using 64-band AisaEAGLE II hyperspectral data in a wavelength range of 400-1000 nm. First, band selection was performed using a sparse logistic regressionbased feature selection algorithm with pixel-level and plot-level data in case of balanced and imbalanced training data. This resulted in 8-11 selected hyperspectral bands, depending on the properties of the data used. We evaluated a tree species classification with 8-11 selected hyperspectral bands directly for a least squares support vector machine (LS-SVM)-based pixel-level classification with a relatively small training set size (0.5%-1.5% of the total data) and obtained an accuracy and kappa of around 93.50% and 0.90, respectively. These results are around 0.53%-0.94% points lower than those obtained using all of the hyperspectral bands. Second, one important wavelength region highlight by the selected bands was used to modify the sensor sensitivity configuration in the Leica Airborne Digital Sensor 40 (ADS40) multispectral sensor. Using a simulation model and the hyperspectral data, the modified and standard Leica ADS40 sensor responses were simulated and compared, and the modified system simulated response indicates a 3%-5% point improvement in the pixel-level and plot-level LS-SVM classification accuracy compared with the simulated responses of the standard Leica ADS40 band configuration.
Introduction: Peptic ulcer perforation carries high mortality and morbidity. Boey’s score is shown to be a simple scoring system to help predict morbidity and mortality. This is a prospective observational study to evaluate the applicability of Boey’s score in predicting mortality and morbidity in Nepalese patients.
Methods: This study was conducted in the Dept. of Surgery, Nepal Medical College and Teaching Hospital (NMCTH), Attarkhel, Jorpati between 1st of July 2012 to 30th June 2019 over a period of 7 years. This was a prospective observational study. All patients who underwent laparotomy for suspected peptic ulcer perforation peritonitis were included in the study.
Results: Fourty-seven patients were included in the study. Male patients outnumbered females by a ratio of almost 4:1. Eighteen (38%) patients had Boey’s score of 1, and 7 (15 %) patients had a Boey’s score of 3. Overall postoperative mortality was 7 (15%). Boey’s score predicted morbidity and mortality with a p-value of <0.01. The length of hospital stay was also more in patients with a higher score and it was statistically significant.
Conclusions: Boey’s score is both easy and effective in predicting postoperative morbidity, mortality and length of hospital stay.
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