Study to assess the importance of errors introduced by applying NOAA 6 and NOAA 7 AVHRR data as an estimator of vegetative vigor: Feasibility study of data normalization

The use of NOAA AVHRR data to map and monitor vegetation types and conditions in near real-time can be enhanced by using a portion of each GAC image that is larger than the central 25% now considered. Enlargement of the cloud free image data set can permit development of a series of algorithms for correcting imagery for ground reflectance and for atmospheric scattering anisotropy within certain accuracy limits. Empirical correction algorithms used to normalize digital radiance or VIN data must contain factors for growth stage and for instrument spectral response. While it is not possible to correct for random fluctuations in target radiance, it is possible to estimate the necessary radiance difference between targets in order to provide target discrimination and quantification within predetermined limits of accuracy. A major difficulty lies in the lack of documentation of preprocessing algorithms used on AVHRR digital data

Quantification Method for In-Vitro Tissue Culture Plants Morphology using Object Tracking and Digital Image Analysis

Manual measurement of morphology variables on in-vitro stored plants usually cause either physical damage or microorganism infection such that further monitoring of their in-vitro performance is precluded. This study adapted computer vision technology by which it is possible to conduct such measurement without physical contact or destructive test. Moreover, by applying object tracking and pattern recognition technique in the algorithm, the system could provide automatic and real time analysis. It was shown that this quantification method reach 80.2% and 87.9% in the measurement of leaf area and chlorophyll intensity. Intensity histogram and Fourier spectrum found to be the best feature for leaf recognition and interpolation usage to adjust pixel amount over the camera distance provide better estimation on leaf area

Objective quantification of fluorescence intensity on the corneal surface using a modified slit-lamp technique.

ObjectivesTo improve the digital quantification of fluorescence intensity of sodium fluorescein instilled on corneal surface by modifying a slit lamp hardware and performing computerized processing of captured digital images.MethodsThe optics of a slit lamp were modified to remove corneal Purkinje reflection and to expand the illuminated area on the cornea, followed by postexperiment image processing to minimize the influence of uneven illumination. To demonstrate the feasibility and reliability of this new technique, we applied it to objective grading of corneal staining with sodium fluorescein. The results of computerized grading were compared with the results obtained using standard subjective grading of corneal staining. Objective digital grades, staining area, and staining pixel with manually and automatically defined threshold (SP-M and SP-A) were calculated for both original and processed images. Standard subjective grades of the original images were performed by 13 trained observers using National Eye Institute (NEI), Efron, and CCLRU grading scales. A series of linear regression analyses were performed to investigate the correlation between objective and subjective grades.ResultsDigital grades of the captured images were correlated significantly with subjective grades. After minimization of the artifact caused by the nonuniform illumination, correlations between digital and subjective grading were mostly strengthened. In some cases, digital grading of corneal staining was more sensitive than subjective grading methods when differentiating subtle differences of corneal staining.ConclusionsModifications performed on commercial slit-lamp hardware and the proposed digital image-processing technique have improved the quality of captured images for semiautomated quantification of fluorescein intensity on the cornea

Automatic plankton quantification using deep features

The study of marine plankton data is vital to monitor the health of the world’s oceans. In recent decades, automatic plankton recognition systems have proved useful to address the vast amount of data collected by specially engineered in situ digital imaging systems. At the beginning, these systems were developed and put into operation using traditional automatic classification techniques, which were fed with handdesigned local image descriptors (such as Fourier features), obtaining quite successful results. In the past few years, there have been many advances in the computer vision community with the rebirth of neural networks. In this paper, we leverage how descriptors computed using Convolutional Neural Networks (CNNs) trained with out-of-domain data are useful to replace hand-designed descriptors in the task of estimating the prevalence of each plankton class in a water sample. To achieve this goal, we have designed a broad set of experiments that show how effective these deep features are when working in combination with state-of-the-art quantification algorithms

Quantifying tumour-infiltrating lymphocyte subsets : a practical immuno-histochemical method

Background: Efficient histological quantification of tumour-infiltrating T and B lymphocyte (TIL) subsets in archival tissues would greatly facilitate investigations of the role of TIL in human cancer biology. We sought to develop such a method. Methods: Ten ×40 digital images of 4 μ sections of 16 ductal invasive breast carcinomas immunostained for CD3, CD4, CD8, and CD20 were acquired (a total of 640 images). The number of pixels in each image matching a partition of Lab colour space corresponding to immunostained cells were counted using the ‘Color range’ and ‘Histogram’ tools in Adobe Photoshop 7. These pixel counts were converted to cell counts per mm2 using a calibration factor derived from one, two, three or all 10 images of each case/antibody combination. Results: Variations in the number of labelled pixels per immunostained cell made individual calibration for each case/antibody combination necessary. Calibration based on two fields containing the most labelled pixels gave a cell count minimally higher (+ 5.3%) than the count based on 10-field calibration, with 95% confidence limits − 14.7 to + 25.3%. As TIL density could vary up to 100-fold between cases, this accuracy and precision are acceptable. Conclusion: The methodology described offers sufficient accuracy, precision and efficiency to quantify the density of TIL sub-populations in breast cancer using commonly available software, and could be adapted to batch processing of image files

New methods for automatic quantification of microstructural features using digital image processing

Thermal and mechanical processes alter the microstructure of materials, which determines their mechanical properties. This makes reliable microstructural analysis important to the design and manufacture of components. However, the analysis of complex microstructures, such as Ti6Al4V, is difficult and typically requires expert materials scientists to manually identify and measure microstructural features. This process is often slow, labour intensive and suffers from poor repeatability. This paper overcomes these challenges by proposing a new set of automated techniques for 2D microstructural analysis. Digital image processing algorithms are developed to isolate individual microstructural features, such as grains and alpha lath colonies. A segmentation of the image is produced, where regions represent grains and colonies, from which morphological features such as; grain size, volume fraction of globular alpha grains and alpha colony size can be measured. The proposed measurement techniques are shown to obtain similar results to existing manual methods while drastically improving speed and repeatability. The benefits of the proposed approach when measuring complex microstructures are demonstrated by comparing it with existing analysis software. Using a few parameter changes, the proposed techniques are effective on a variety of microstructure types and both SEM and optical microscopy image

Image informatics strategies for deciphering neuronal network connectivity

Brain function relies on an intricate network of highly dynamic neuronal connections that rewires dramatically under the impulse of various external cues and pathological conditions. Among the neuronal structures that show morphologi- cal plasticity are neurites, synapses, dendritic spines and even nuclei. This structural remodelling is directly connected with functional changes such as intercellular com- munication and the associated calcium-bursting behaviour. In vitro cultured neu- ronal networks are valuable models for studying these morpho-functional changes. Owing to the automation and standardisation of both image acquisition and image analysis, it has become possible to extract statistically relevant readout from such networks. Here, we focus on the current state-of-the-art in image informatics that enables quantitative microscopic interrogation of neuronal networks. We describe the major correlates of neuronal connectivity and present workflows for analysing them. Finally, we provide an outlook on the challenges that remain to be addressed, and discuss how imaging algorithms can be extended beyond in vitro imaging studies