Optical Method for Estimating the Chlorophyll Contents in Plant Leaves.

This work introduces a new vision-based approach for estimating chlorophyll contents in a plant leaf using reflectance and transmittance as base parameters. Images of the top and underside of the leaf are captured. To estimate the base parameters (reflectance/transmittance), a novel optical arrangement is proposed. The chlorophyll content is then estimated by using linear regression where the inputs are the reflectance and transmittance of the leaf. Performance of the proposed method for chlorophyll content estimation was compared with a spectrophotometer and a Soil Plant Analysis Development (SPAD) meter. Chlorophyll content estimation was realized for Lactuca sativa L., Azadirachta indica, Canavalia ensiforme, and Lycopersicon esculentum. Experimental results showed that-in terms of accuracy and processing speed-the proposed algorithm outperformed many of the previous vision-based approach methods that have used SPAD as a reference device. On the other hand, the accuracy reached is 91% for crops such as Azadirachta indica, where the chlorophyll value was obtained using the spectrophotometer. Additionally, it was possible to achieve an estimation of the chlorophyll content in the leaf every 200 ms with a low-cost camera and a simple optical arrangement. This non-destructive method increased accuracy in the chlorophyll content estimation by using an optical arrangement that yielded both the reflectance and transmittance information, while the required hardware is cheap.

A sensor-data-based denoising framework for hyperspectral images.

Many denoising approaches extend image processing to a hyperspectral cube structure, but do not take into account a sensor model nor the format of the recording. We propose a denoising framework for hyperspectral images that uses sensor data to convert an acquisition to a representation facilitating the noise-estimation, namely the photon-corrected image. This photon corrected image format accounts for the most common noise contributions and is spatially proportional to spectral radiance values. The subsequent denoising is based on an extended variational denoising model, which is suited for a Poisson distributed noise. A spatially and spectrally adaptive total variation regularisation term accounts the structural proposition of a hyperspectral image cube. We evaluate the approach on a synthetic dataset that guarantees a noise-free ground truth, and the best results are achieved when the dark current is taken into account.

Automatic evaluation of the Valsalva sinuses from cine-MRI.

OBJECT: Although, there is no global consensus on their measurement, magnetic resonance imaging (MRI) appears to be particularly attractive for the study of the sinuses of Valsalva (SV). The purpose of this study was to automatically evaluate the SV from cine-MRI using a standardized method. MATERIALS AND METHODS: An automatic method based on mathematical morphology was elaborated to segment the aortic root from cross-sectional cine-MRI, and to detect relevant points, such as the commissures, the cusps and the centre of the SV. The distances between these points allow a metric evaluation of the SV. Our method was tested on synthesized data and 41 patient data sets and radii calculations were compared with manual processing. RESULTS: On the patient data sets, there are excellent correlation and concordance between manual and automatic measurements for images at diastole (r=0.97; y=0.97x+0.57; P<10(-5); mean of differences=0.2 mm; standard deviation of differences=2.0 mm) and at systole (r=0.96; y=0.96x+1.2; P<10(-5); mean of differences<0.1 mm; standard deviation of differences=2.4 mm). CONCLUSION: Our automatic method provides reliable morphometric evaluation of the SV. Measures of distances between relevant points allow a precise evaluation of each cusp of the SV. This robust evaluation can be helpful in the follow-up of patients with aortic root diseases.

Automatic measurement of the sinus of Valsalva by image analysis.

BACKGROUND AND OBJECTIVES: Despite the importance of the morphology of the sinus of Valsalva in the behavior of heart valves and the proper irrigation of coronary arteries, the study of these sinuses from medical imaging is still limited to manual radii measurements. This paper aims to present an automatic method to measure the sinuses of Valsalva on medical images, more specifically on cine MRI and Xray CT. METHODS: This paper introduces an enhanced method to automatically localize and extract each sinus of Valsalva edge and its relevant points. Compared to classical active contours, this new image approach enhances the edge extraction of the Sinus of Valsalva. Our process not only allows image segmentation but also a complex study of the considered region including morphological classification, metrological characterization, valve tracking and 2D modeling. RESULTS: The method was successfully used on single or multiplane cine MRI and aortic CT angiographies. The localization is robust and the proposed edge extractor is more efficient than the state-of-the-art methods (average success rate for MRI examinations=84% ± 24%, average success rate for CT examinations=89% ± 11%). Moreover, deduced measurements are close to manual ones. CONCLUSIONS: The software produces accurate measurements of the sinuses of Valsalva. The robustness and the reproducibility of results will help for a better understanding of sinus of Valsalva pathologies and constitutes a first step to the design of complex prostheses adapted to each patient.