Calibration of a desktop scanner and digital image analysis procedure for quantification of a root morphology

Selostus: Tasoskannerin ja digitaalisen kuva-analyysimenetelmän kalibrointi juurten morfologian kvantifioimiseksi

Image Analysis for Evaluating Air Void Parameters of Concrete, HR-396, 1998

This research project investigated the use of image analysis to measure the air void parameters of concrete specimens produced under standard laboratory conditions. The results obtained from the image analysis technique were compared to results obtained from plastic air content tests, Danish air meter tests (also referred to as Air Void Analyzer tests), high-pressure air content tests on hardened concrete, and linear traverse tests (as per ASTM C-457). Hardened concrete specimens were sent to three different laboratories for the linear traverse tests. The samples that were circulated to the three labs consisted of specimens that needed different levels of surface preparation. The first set consisted of approximately 18 specimens that had been sectioned from a 4 in. by 4 in. by 18 in. (10 cm by 10 cm by 46 cm) beam using a saw equipped with a diamond blade. These specimens were subjected to the normal sample preparation techniques that were commonly employed by the three different labs (each lab practiced slightly different specimen preparation techniques). The second set of samples consisted of eight specimens that had been ground and polished at a single laboratory. The companion labs were only supposed to retouch the sample surfaces if they exhibited major flaws. In general, the study indicated that the image analysis test results for entrained air content exhibited good to strong correlation to the average values determined via the linear traverse technique. Specimens ground and polished in a single laboratory and then circulated to the other participating laboratories for the air content determinations exhibited the strongest correlation between the image analysis and linear traverse techniques (coefficient of determination, r-squared = 0.96, for n=8). Specimens ground and polished at each of the individual laboratories exhibited considerably more scatter (coefficient of determination, r-squared = 0.78, for n=16). The image analysis technique tended to produce low estimates of the specific surface of the voids when compared to the results from the linear traverse method. This caused the image analysis spacing factor calculations to produce larger values than those obtained from the linear traverse tests. The image analysis spacing factors were still successful at distinguishing between the frost-prone test specimens and the other (more durable) test specimens that were studied in this research project.

Sample size for measurement of root traits on common bean by image analysis

Evaluation of root traits may be facilitated if they are assessed on samples of the root system. The objective of this work was to determine the sample size of the root system in order to estimate root traits of common bean (Phaseolus vulgaris L.) cultivars by digital image analysis. One plant was grown per pot and harvested at pod setting, with 64 and 16 pots corresponding to two and four cultivars in the first and second experiments, respectively. Root samples were scanned up to the completeness of the root system and the root area and length were estimated. Scanning a root sample demanded 21 minutes, and scanning the entire root system demanded 4 hours and 53 minutes. In the first experiment, root area and length estimated with two samples showed, respectively, a correlation of 0.977 and 0.860, with these traits measured in the entire root. In the second experiment, the correlation was 0.889 and 0.915. The increase in the correlation with more than two samples was negligible. The two samples corresponded to 13.4% and 16.9% of total root mass (excluding taproot and nodules) in the first and second experiments. Taproot stands for a high proportion of root mass and must be deducted on root trait estimations. Samples with nearly 15% of total root mass produce reliable root trait estimates.

Image Analysis for the Characterization of Materials for Highway Construction : Phase I, Progress Report, HR-346, 1992

The major objective of this project is to evaluate image analysis for characterizing air voids in Portland cement contract (PCC) and asphalt concrete (AC) and aggregate gradation in asphalt concrete. Phase 1 of this project has concentrated on evaluation and refinement of sample preparation techniques, evaluation of methods and instruments for conducting image analysis, and finally, analysis and comparison of a select portion of samples. Preliminary results suggest a strong correlation between the results obtained from the linear traverse method and image analysis methods for determining percent air voids in concrete. Preliminary work with asphalt samples has shown that damage caused by a high vacuum of the conventional scanning electron microscope (SEM) may too disruptive. Alternative solutions have been explored, including confocal microscopy and low vacuum electron microscopy. Additionally, a conventional high vacuum SEM operating at a marginal operating vacuum may suffice.

Retinal Image Analysis Using Machine Vision

The topic of this thesis is studying how lesions in retina caused by diabetic retinopathy can be detected from color fundus images by using machine vision methods. Methods for equalizing uneven illumination in fundus images, detecting regions of poor image quality due toinadequate illumination, and recognizing abnormal lesions were developed duringthe work. The developed methods exploit mainly the color information and simpleshape features to detect lesions. In addition, a graphical tool for collecting lesion data was developed. The tool was used by an ophthalmologist who marked lesions in the images to help method development and evaluation. The tool is a general purpose one, and thus it is possible to reuse the tool in similar projects.The developed methods were tested with a separate test set of 128 color fundus images. From test results it was calculated how accurately methods classify abnormal funduses as abnormal (sensitivity) and healthy funduses as normal (specificity). The sensitivity values were 92% for hemorrhages, 73% for red small dots (microaneurysms and small hemorrhages), and 77% for exudates (hard and soft exudates). The specificity values were 75% for hemorrhages, 70% for red small dots, and 50% for exudates. Thus, the developed methods detected hemorrhages accurately and microaneurysms and exudates moderately.

Environmental Monitoring using Image Analysis

Tärkeä tehtävä ympäristön tarkkailussa on arvioida ympäristön nykyinen tila ja ihmisen siihen aiheuttamat muutokset sekä analysoida ja etsiä näiden yhtenäiset suhteet. Ympäristön muuttumista voidaan hallita keräämällä ja analysoimalla tietoa. Tässä diplomityössä on tutkittu vesikasvillisuudessa hai vainuja muutoksia käyttäen etäältä hankittua mittausdataa ja kuvan analysointimenetelmiä. Ympäristön tarkkailuun on käytetty Suomen suurimmasta järvestä Saimaasta vuosina 1996 ja 1999 otettuja ilmakuvia. Ensimmäinen kuva-analyysin vaihe on geometrinen korjaus, jonka tarkoituksena on kohdistaa ja suhteuttaa otetut kuvat samaan koordinaattijärjestelmään. Toinen vaihe on kohdistaa vastaavat paikalliset alueet ja tunnistaa kasvillisuuden muuttuminen. Kasvillisuuden tunnistamiseen on käytetty erilaisia lähestymistapoja sisältäen valvottuja ja valvomattomia tunnistustapoja. Tutkimuksessa käytettiin aitoa, kohinoista mittausdataa, minkä perusteella tehdyt kokeet antoivat hyviä tuloksia tutkimuksen onnistumisesta.

Data Envelopment Analysis and non-discretionary inputs : How to select the most suitable model using multi-criteria decision analysis

Within Data Envelopment Analysis, several alternative models allow for an environmental adjustment. The majority of them deliver divergent results. Decision makers face the difficult task of selecting the most suitable model. This study is performed to overcome this difficulty. By doing so, it fills a research gap. First, a two-step web-based survey is conducted. It aims (1) to identify the selection criteria, (2) to prioritize and weight the selection criteria with respect to the goal of selecting the most suitable model and (3) to collect the preferences about which model is preferable to fulfil each selection criterion. Second, Analytic Hierarchy Process is used to quantify the preferences expressed in the survey. Results show that the understandability, the applicability and the acceptability of the alternative models are valid selection criteria. The selection of the most suitable model depends on the preferences of the decision makers with regards to these criteria.

Particle shape and orientation in laser diffraction and static image analysis size distribution analysis of micrometer sized rectangular particles

Laser diffraction (LD) and static image analysis (SIA) of rectangular particles [United States Pharmacopeia, USP30-NF25, General Chapter <776>, Optical Miroscopy.] have been systematically studied. To rule out sample dispersion and particle orientation as the root cause of differences in size distribution profiles, we immobilize powder samples on a glass plate by means of a dry disperser. For a defined region of the glass plate, we measure the diffraction pattern as induced by the dispersed particles, and the 2D dimensions of the individual particles using LD and optical microscopy, respectively. We demonstrate a correlation between LD and SIA, with the scattering intensity of the individual particles as the dominant factor. In theory, the scattering intensity is related to the square of the projected area of both spherical and rectangular particles. In traditional LD the size distribution profile is dominated by the maximum projected area of the particles (A). The diffraction diameters of a rectangular particle with length L and breadth B as measured by the LD instrument approximately correspond to spheres of diameter ØL and ØB respectively. Differences in the scattering intensity between spherical and rectangular particles suggest that the contribution made to the overall LD volume probability distribution by each rectangular particle is proportional to A2/L and A2/B. Accordingly, for rectangular particles the scattering intensity weighted diffraction diameter (SIWDD) explains an overestimation of their shortest dimension and an underestimation of their longest dimension. This study analyzes various samples of particles whose length ranges from approximately 10 to 1000 μm. The correlation we demonstrate between LD and SIA can be used to improve validation of LD methods based on SIA data for a variety of pharmaceutical powders all with a different rectangular particle size and shape.

Analysis of resuspension and sedimentation processes of unaltered fish faeces using image analysis

Postprint (published version)

Characterisation of pigment particles by scanning electron microscope and image analysis programs

Coating and filler pigments have strong influence to the properties of the paper. Filler content can be even over 30 % and pigment content in coating is about 85-95 weight percent. The physical and chemical properties of the pigments are different and the knowledge of these properties is important for optimising of optical and printing properties of the paper. The size and shape of pigment particles can be measured by different analysers which can be based on sedimentation, laser diffraction, changes in electric field etc. In this master's thesis was researched particle properties especially by scanning electron microscope (SEM) and image analysis programs. Research included nine pigments with different particle size and shape. Pigments were analysed by two image analysis programs (INCA Feature and Poikki), Coulter LS230 (laser diffraction) and SediGraph 5100 (sedimentation). The results were compared to perceive the effect of particle shape to the performance of the analysers. Only image analysis programs gave parameters of the particle shape. One part of research was also the sample preparation for SEM. Individual particles should be separated and distinct in ideal sample. Analysing methods gave different results but results from image analysis programs corresponded even to sedimentation or to laser diffraction depending on the particle shape. Detailed analysis of the particle shape required high magnification in SEM, but measured parameters described very well the shape of the particles. Large particles (ecd~1 µm) could be used also in 3D-modelling which enabled the measurement of the thickness of the particles. Scanning electron microscope and image analysis programs were effective and multifunctional tools for particle analyses. Development and experience will devise the usability of analysing method in routine use.

Computational Techniques for Spectral Image Analysis

Multispectral images are becoming more common in the field of remote sensing, computer vision, and industrial applications. Due to the high accuracy of the multispectral information, it can be used as an important quality factor in the inspection of industrial products. Recently, the development on multispectral imaging systems and the computational analysis on the multispectral images have been the focus of a growing interest. In this thesis, three areas of multispectral image analysis are considered. First, a method for analyzing multispectral textured images was developed. The method is based on a spectral cooccurrence matrix, which contains information of the joint distribution of spectral classes in a spectral domain. Next, a procedure for estimating the illumination spectrum of the color images was developed. Proposed method can be used, for example, in color constancy, color correction, and in the content based search from color image databases. Finally, color filters for the optical pattern recognition were designed, and a prototype of a spectral vision system was constructed. The spectral vision system can be used to acquire a low dimensional component image set for the two dimensional spectral image reconstruction. The data obtained by the spectral vision system is small and therefore convenient for storing and transmitting a spectral image.

Improving the oscillating grid method to characterize aquaculture biosolids using a laser beam and image analysis

Quickremovalofbiosolidsinaquaculturefacilities,andspeciallyinrecirculatingaquaculturesystems(RAS),isoneofthemostimportantstepinwastemanagement.Sedimentationdynamicsofbiosolidsinanaquaculturetankwilldeterminetheiraccumulationatthebottomofthetank.

Eye Fundus Image Analysis for Automatic Detection of Diabetic Retinopathy

Diabetes is a rapidly increasing worldwide problem which is characterised by defective metabolism of glucose that causes long-term dysfunction and failure of various organs. The most common complication of diabetes is diabetic retinopathy (DR), which is one of the primary causes of blindness and visual impairment in adults. The rapid increase of diabetes pushes the limits of the current DR screening capabilities for which the digital imaging of the eye fundus (retinal imaging), and automatic or semi-automatic image analysis algorithms provide a potential solution. In this work, the use of colour in the detection of diabetic retinopathy is statistically studied using a supervised algorithm based on one-class classification and Gaussian mixture model estimation. The presented algorithm distinguishes a certain diabetic lesion type from all other possible objects in eye fundus images by only estimating the probability density function of that certain lesion type. For the training and ground truth estimation, the algorithm combines manual annotations of several experts for which the best practices were experimentally selected. By assessing the algorithm’s performance while conducting experiments with the colour space selection, both illuminance and colour correction, and background class information, the use of colour in the detection of diabetic retinopathy was quantitatively evaluated. Another contribution of this work is the benchmarking framework for eye fundus image analysis algorithms needed for the development of the automatic DR detection algorithms. The benchmarking framework provides guidelines on how to construct a benchmarking database that comprises true patient images, ground truth, and an evaluation protocol. The evaluation is based on the standard receiver operating characteristics analysis and it follows the medical practice in the decision making providing protocols for image- and pixel-based evaluations. During the work, two public medical image databases with ground truth were published: DIARETDB0 and DIARETDB1. The framework, DR databases and the final algorithm, are made public in the web to set the baseline results for automatic detection of diabetic retinopathy. Although deviating from the general context of the thesis, a simple and effective optic disc localisation method is presented. The optic disc localisation is discussed, since normal eye fundus structures are fundamental in the characterisation of DR.

Image analysis for assessing broiler breeder behavior response to thermal environment

The research proposes a methodology for assessing broiler breeder response to changes in rearing thermal environment. The continuous video recording of a flock analyzed may offer compelling evidences of thermal comfort, as well as other indications of welfare. An algorithm for classifying specific broiler breeder behavior was developed. Videos were recorded over three boxes where 30 breeders were reared. The boxes were mounted inside an environmental chamber were ambient temperature varied from cold to hot. Digital images were processed based on the number of pixels, according to their light intensity variation and binary contrast allowing a sequence of behaviors related to welfare. The system used the default of x, y coordinates, where x represents the horizontal distance from the top left of the work area to the point P, and y is the vertical distance. The video images were observed, and a grid was developed for identifying the area the birds stayed and the time they spent at that place. The sequence was analyzed frame by frame confronting the data with specific adopted thermal neutral rearing standards. The grid mask overlapped the real bird image. The resulting image allows the visualization of clusters, as birds in flock behave in certain patterns. An algorithm indicating the breeder response to thermal environment was developed.

Evaluation of follicular lymphoid depletion in the Bursa of Fabricius: an alternative methodology using digital image analysis and artificial neural networks

Fifty Bursa of Fabricius (BF) were examined by conventional optical microscopy and digital images were acquired and processed using Matlab® 6.5 software. The Artificial Neuronal Network (ANN) was generated using Neuroshell® Classifier software and the optical and digital data were compared. The ANN was able to make a comparable classification of digital and optical scores. The use of ANN was able to classify correctly the majority of the follicles, reaching sensibility and specificity of 89% and 96%, respectively. When the follicles were scored and grouped in a binary fashion the sensibility increased to 90% and obtained the maximum value for the specificity of 92%. These results demonstrate that the use of digital image analysis and ANN is a useful tool for the pathological classification of the BF lymphoid depletion. In addition it provides objective results that allow measuring the dimension of the error in the diagnosis and classification therefore making comparison between databases feasible.