This dissertation provides the foundations of computer-based automated phenotyping methods for analyzing 3D images of mouse embryos. A group-wise registration technique was utilized and optimized and computerized methods were employed for analysis of 3D MRI images of mouse embryos. The assumption that embryo anatomy is highly conserved among genetically identical specimens was verified. The group-wise registration approach was used to align a group of embryos from the 129S1/SvImJ (129Sv) strain as well as a group of C57BL/6J (C57) embryos. Finally, we shed some light on some of the morphological differences between the 129Sv and C57 strains using automated techniques.

This dissertation provides the foundations of computer-based automated phenotyping methods for analyzing 3D images of mouse embryos. A group-wise registration technique was utilized and optimized and computerized methods were employed for analysis of 3D MRI images of mouse embryos. The assumption that embryo anatomy is highly conserved among genetically identical specimens was verified. The group-wise registration approach was used to align a group of embryos from the 129S1/SvImJ (129Sv) strain as well as a group of C57BL/6J (C57) embryos. Finally, we shed some light on some of the morphological differences between the 129Sv and C57 strains using automated techniques.

No description available.

In this study, a new approach for the matching and reconstruction of line features from multispectral stereo aerial images is presented. The advantages of the existing multispectral information in aerial images are fully taken into account all over the steps of pre-processing and edge detection. To accurately describe the straight line segments, a principal component analysis technique is adapted. The initial correspondences between the stereo images are generated using a new pair-wise stereo matching approach which involves a total of seven relational constraints. The final line to line correspondences between the stereo images are established in a precise matching stage in which the final line matches are assigned by means of three novel measures and a final similarity voting scheme. Once the line matches are established, the stereo reconstruction of those matches is performed by an innovative reconstruction approach that manipulates the redundancy inherent in line pair-relations. By this way, the reconstruction of the stereo matches that are observed in a nearly-parallel geometry with the epipolar lines can also be performed accurately. The proposed approach is tested over two different urban test sites with various built-up characteristics, and as a result, very successful and promising stereo line matching and reconstruction performances are reached. Besides, the comparison of the results of the proposed approach with the results of one of the state-of-the-art stereo matching approaches proves the superiority and the potential of proposed approach.

Plankton imaging systems such as SIPPER produce a large quantity of data in the form of plankton images from a variety of classes. A system known as PICES was developed to quickly extract, classify and manage the millions of images produced from a single one-week research cruise. A new fast technique for parameter tuning and feature selection for Support Vector Machines using Wrappers was created. This technique allows for faster feature selection, while at the same time maintaining and sometimes improving classification accuracy. It also gives the user greater flexibility in the management of class contents in existing training libraries. Support vector machines are binary classifiers that can implement multi-class classifiers by creating a classifier for each possible combination of classes or for each class using a one class versus all strategy. Feature selection searches for a single set of features to be used by each of the binary classifiers. This ignores the fact that features that may be good discriminators for two particular classes might not do well for other class combinations. As a result, the feature selection process may not include these features in the common set to be used by all support vector machines. It is shown through experimentation that by selecting features for each binary class combination, overall classification accuracy can be improved and the time required for training a multi-class support vector machine can be reduced. Another benefit of this approach is that significantly less time is required for feature selection when additional classes are added to the training data. This is because the features selected for the existing class combinations are still valid, so that feature selection only needs to be run for the new combination added. This work resulted in a system called PICES, a GUI based user friendly system, which aids in the classification management of over 55 million images of plankton split amongst 180 classes. PICES embodies an improved means of performing Wrapper based feature selection that creates classifiers that train faster and are just as accurate and sometimes more accurate, while reducing the feature selection time.

There are plenty of solutions for the task of multi-class recognition. Unfortunately, these solutions are not always unanimous. Most of them are based on empirical experiments while statistical data features consideration is often omitted. That’s why questions like when and which method should be used, what the reliability of any chosen method is for solving a multi-class recognition task arise. In this dissertation two-stage multi-class decision methods are analyzed. Pair-wise classifiers able to better exploit statistical data features are used in the first stage of such methods. In the second stage a particular fusion rule of the first stage results is used to fuse the first stage results in order to produce the final classification decision. Complexity issues of pair-wise classifiers, training data size and precision of method quality estimation are pointed out in the research. The precision of algorithm highly depends on the data and the number of experiments performed (data permutation, division into training and testing data). It is shown that the declared superiority of some known algorithms is not reliable due to low precision of estimation. A detailed comparison of well known multi-class classification methods is performed and a new pair-wise classifier fusion method based on similar method used in multi-class classifier fusion is presented. The recommendations for multi-class classification task designer are provided. Methods which allow reducing classification... [to full text] / Daugelio klasių atpažinimo uždaviniams spręsti yra sukurta aibė sprendimų ir ne visada vieningų rekomendacijų. Dauguma jų paremta empiriniais bandymais, retai atsižvelgiama į statistines duomenų savybes. Dėl to sprendžiant daugelio klasių klasifikavimo uždavinį kyla klausimų, kurį metodą ir kada geriausia naudoti, koks vieno ar kito metodo patikimumas. Disertacijoje nagrinėjami dviejų pakopų sprendimo priėmimo metodai, kai pirmame etape sudaromi klasifikatoriai poroms (angl. pair-wise), sugebantys geriau išnaudoti klasių tarpusavio statistines savybes, o kitame etape yra atliekamas klasifikatorių poroms rezultatų apjungimas. Tyrime ypatingas dėmesys yra skiriamas klasifikatorių poroms sudėtingumui, mokymo duomenų kiekiui bei algoritmų kokybės įvertinimo tikslumui. Tikslumas labai priklauso nuo duomenų bei atliktų eksperimentų kiekio (duomenų permaišymo klasėse, juos skirstant į mokymo ir testavimo). Parodyta, jog dėl žemo įvertinimo tikslumo kai kurių publikuotų algoritmų deklaruojamas pranašumas prieš žinomus algoritmus nėra patikimas. Darbe atliktas detalus žinomų metodų palyginimas bei pristatytas naujai sukurtas klasifikatorių poroms apjungimo algoritmas, kuris yra paremtas analogišku algoritmu daugelio klasių klasifikatorių rezultatų apjungimui. Pateiktos bendros rekomendacijos, kaip projektuotojui elgtis daugelio klasių atveju. Pasiūlyti metodai, leidžiantys sumažinti klasifikavimo klaidą atliekant klasifikatorių poroms apjungimo koregavimą, kad algoritmas nebūtų... [toliau žr. visą tekstą]

Daugelio klasių atpažinimo uždaviniams spręsti yra sukurta aibė sprendimų ir ne visada vieningų rekomendacijų. Dauguma jų paremta empiriniais bandymais, retai atsižvelgiama į statistines duomenų savybes. Dėl to sprendžiant daugelio klasių klasifikavimo uždavinį kyla klausimų, kurį metodą ir kada geriausia naudoti, koks vieno ar kito metodo patikimumas. Disertacijoje nagrinėjami dviejų pakopų sprendimo priėmimo metodai, kai pirmame etape sudaromi klasifikatoriai poroms (angl. pair-wise), sugebantys geriau išnaudoti klasių tarpusavio statistines savybes, o kitame etape yra atliekamas klasifikatorių poroms rezultatų apjungimas. Tyrime ypatingas dėmesys yra skiriamas klasifikatorių poroms sudėtingumui, mokymo duomenų kiekiui bei algoritmų kokybės įvertinimo tikslumui. Tikslumas labai priklauso nuo duomenų bei atliktų eksperimentų kiekio (duomenų permaišymo klasėse, juos skirstant į mokymo ir testavimo). Parodyta, jog dėl žemo įvertinimo tikslumo kai kurių publikuotų algoritmų deklaruojamas pranašumas prieš žinomus algoritmus nėra patikimas. Darbe atliktas detalus žinomų metodų palyginimas bei pristatytas naujai sukurtas klasifikatorių poroms apjungimo algoritmas, kuris yra paremtas analogišku algoritmu daugelio klasių klasifikatorių rezultatų apjungimui. Pateiktos bendros rekomendacijos, kaip projektuotojui elgtis daugelio klasių atveju. Pasiūlyti metodai, leidžiantys sumažinti klasifikavimo klaidą atliekant klasifikatorių poroms apjungimo koregavimą, kad algoritmas nebūtų... [toliau žr. visą tekstą] / There are plenty of solutions for the task of multi-class recognition. Unfortunately, these solutions are not always unanimous. Most of them are based on empirical experiments while statistical data features consideration is often omitted. That’s why questions like when and which method should be used, what the reliability of any chosen method is for solving a multi-class recognition task arise. In this dissertation two-stage multi-class decision methods are analyzed. Pair-wise classifiers able to better exploit statistical data features are used in the first stage of such methods. In the second stage a particular fusion rule of the first stage results is used to fuse the first stage results in order to produce the final classification decision. Complexity issues of pair-wise classifiers, training data size and precision of method quality estimation are pointed out in the research. The precision of algorithm highly depends on the data and the number of experiments performed (data permutation, division into training and testing data). It is shown that the declared superiority of some known algorithms is not reliable due to low precision of estimation. A detailed comparison of well known multi-class classification methods is performed and a new pair-wise classifier fusion method based on similar method used in multi-class classifier fusion is presented. The recommendations for multi-class classification task designer are provided. Methods which allow reducing classification... [to full text]

Therapeutic response assessment is a key component in adaptive image-guided radiotherapy. Conventional anatomic measures of response offer little information about the spatial distribution of tumor change. Recently developed voxel-wise response assessment methods operating on functional and biological imaging are better capable of evaluating the heterogeneity of response within the tumor, and thus may yield greater sensitivity than conventional approaches. However, voxel-wise analyses are limited by local registration uncertainties inherent to longitudinal imaging of tumors with changing morphology. A multi-resolution local histogram (LH) moment-based measure of voxel similarity was developed for the purpose of assessing the strength of correspondence between voxels of serial tumor images. This measure was first benchmarked through a series of experiments designed to establish robustness to image intensity variation and sensitivity to alterations in tissue structure through application of simulated deformations. The LH similarity method was subsequently developed as a means of mapping the spatial extent of structural change in tumors through the incorporation of an estimate of image complexity. The change maps were applied to a voxel-wise analysis of diffusion-weighted magnetic resonance imaging of patients with glioblastoma, acquired pre- and post-chemoradiotherapy. The sensitivity of the voxel-wise analysis in differentiating responding/stable patients from non-responding/progressing patients was improved by stratifying the analysis voxels according to regions of interest (ROI) based on the LH similarity-based estimate of tumor change. Meaningful correspondence relationships between evaluated voxels are essential for accurate image-based quantification of tumor structure and function with voxel-wise analysis techniques. The LH similarity methods developed here can robustly evaluate the quality of spatial and temporal voxel correspondence relationships and provide an automated tool for ROI selection and voxel change stratification. It is readily extendable to the analysis of the wide array of anatomic, functional and biological imaging currently used to characterize tumors, guide therapy and assess response.

Therapeutic response assessment is a key component in adaptive image-guided radiotherapy. Conventional anatomic measures of response offer little information about the spatial distribution of tumor change. Recently developed voxel-wise response assessment methods operating on functional and biological imaging are better capable of evaluating the heterogeneity of response within the tumor, and thus may yield greater sensitivity than conventional approaches. However, voxel-wise analyses are limited by local registration uncertainties inherent to longitudinal imaging of tumors with changing morphology. A multi-resolution local histogram (LH) moment-based measure of voxel similarity was developed for the purpose of assessing the strength of correspondence between voxels of serial tumor images. This measure was first benchmarked through a series of experiments designed to establish robustness to image intensity variation and sensitivity to alterations in tissue structure through application of simulated deformations. The LH similarity method was subsequently developed as a means of mapping the spatial extent of structural change in tumors through the incorporation of an estimate of image complexity. The change maps were applied to a voxel-wise analysis of diffusion-weighted magnetic resonance imaging of patients with glioblastoma, acquired pre- and post-chemoradiotherapy. The sensitivity of the voxel-wise analysis in differentiating responding/stable patients from non-responding/progressing patients was improved by stratifying the analysis voxels according to regions of interest (ROI) based on the LH similarity-based estimate of tumor change. Meaningful correspondence relationships between evaluated voxels are essential for accurate image-based quantification of tumor structure and function with voxel-wise analysis techniques. The LH similarity methods developed here can robustly evaluate the quality of spatial and temporal voxel correspondence relationships and provide an automated tool for ROI selection and voxel change stratification. It is readily extendable to the analysis of the wide array of anatomic, functional and biological imaging currently used to characterize tumors, guide therapy and assess response.

In Kenya, 69 percent of populations depend on biomass fuels to meet their cooking needs. The incomplete combustion of biomass is performed in insufficiently ventilated settings thus exposing human health and environment to toxic pollution. To date, women and children in resource limited settings have been most vulnerable to severe respiratory infections. The promotion of the Improved Biomass Cookstoves has become a popular issue in policy, research and practice. Community Based Organizations have however faced serious challenges in increasing sustainable use of the cookstoves among users. This qualitative study investigates the role of Community Based Organizations in promoting the Improved Biomass Cookstoves through women’s empowerment initiatives. It applies focus group interviews, questionnaire and formal interviews. 11 interview forms were administered to 30 women, 2 key informants and 3 implementing Community Based Organization representatives in Dunga wetland community. A combination of previous research and this empirical study show that economic resources, small diameter stove designs as well as insufficient policy support hinder effective stove programmes. While there is general adoption, women lack sufficient income and ownership of land which is a significant resource to empowerment. Women informal financial networks are instrumental in driving behavioral change towards clean cooking in the community. Sufficient gender sensitive policy intervention and support can provide effective channels to ensure sustainable incomes of women, livelihoods and stove use.