Drying kinetic analysis of municipal solid waste using modified page model and pattern search method

This work studied the drying kinetics of the organic fractions of municipal solid waste (MSW) samples with different initial moisture contents and presented a new method for determination of drying kinetic parameters. A series of drying experiments at different temperatures were performed by using a thermogravimetric technique. Based on the modified Page drying model and the general pattern search method, a new drying kinetic method was developed using multiple isothermal drying curves simultaneously. The new method fitted the experimental data more accurately than the traditional method. Drying kinetic behaviors under extrapolated conditions were also predicted and validated. The new method indicated that the drying activation energies for the samples with initial moisture contents of 31.1 and 17.2 % on wet basis were 25.97 and 24.73 kJ mol−1. These results are useful for drying process simulation and industrial dryer design. This new method can be also applied to determine the drying parameters of other materials with high reliability.

Identification of protein coding regions using the modified Gabor-wavelet transform

An important topic in genomic sequence analysis is the identification of protein coding regions. In this context, several coding DNA model-independent methods based on the occurrence of specific patterns of nucleotides at coding regions have been proposed. Nonetheless, these methods have not been completely suitable due to their dependence on an empirically predefined window length required for a local analysis of a DNA region. We introduce a method based on a modified Gabor-wavelet transform (MGWT) for the identification of protein coding regions. This novel transform is tuned to analyze periodic signal components and presents the advantage of being independent of the window length. We compared the performance of the MGWT with other methods by using eukaryote data sets. The results show that MGWT outperforms all assessed model-independent methods with respect to identification accuracy. These results indicate that the source of at least part of the identification errors produced by the previous methods is the fixed working scale. The new method not only avoids this source of errors but also makes a tool available for detailed exploration of the nucleotide occurrence.

Spatial point pattern analysis of gorilla nest sites in the Kagwene Sanctuary, Cameroon: Towards understanding the nesting behavior of a critically endangered subspecies

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Atlas-based segmentation of pathological MR brain images using a model of lesion growth.

We propose a method for brain atlas deformation in the presence of large space-occupying tumors, based on an a priori model of lesion growth that assumes radial expansion of the lesion from its starting point. Our approach involves three steps. First, an affine registration brings the atlas and the patient into global correspondence. Then, the seeding of a synthetic tumor into the brain atlas provides a template for the lesion. The last step is the deformation of the seeded atlas, combining a method derived from optical flow principles and a model of lesion growth. Results show that a good registration is performed and that the method can be applied to automatic segmentation of structures and substructures in brains with gross deformation, with important medical applications in neurosurgery, radiosurgery, and radiotherapy.

Analysis of a finite volume method for a cross-diffusion model in population dynamics

The main goal of this paper is to propose a convergent finite volume method for a reactionâeuro"diffusion system with cross-diffusion. First, we sketch an existence proof for a class of cross-diffusion systems. Then the standard two-point finite volume fluxes are used in combination with a nonlinear positivity-preserving approximation of the cross-diffusion coefficients. Existence and uniqueness of the approximate solution are addressed, and it is also shown that the scheme converges to the corresponding weak solution for the studied model. Furthermore, we provide a stability analysis to study pattern-formation phenomena, and we perform two-dimensional numerical examples which exhibit formation of nonuniform spatial patterns. From the simulations it is also found that experimental rates of convergence are slightly below second order. The convergence proof uses two ingredients of interest for various applications, namely the discrete Sobolev embedding inequalities with general boundary conditions and a space-time $L^1$ compactness argument that mimics the compactness lemma due to Kruzhkov. The proofs of these results are given in the Appendix.

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.

classification and content based retrieval of digital mammograms and placental sonograms

Image processing has been a challenging and multidisciplinary research area since decades with continuing improvements in its various branches especially Medical Imaging. The healthcare industry was very much benefited with the advances in Image Processing techniques for the efficient management of large volumes of clinical data. The popularity and growth of Image Processing field attracts researchers from many disciplines including Computer Science and Medical Science due to its applicability to the real world. In the meantime, Computer Science is becoming an important driving force for the further development of Medical Sciences. The objective of this study is to make use of the basic concepts in Medical Image Processing and develop methods and tools for clinicians’ assistance. This work is motivated from clinical applications of digital mammograms and placental sonograms, and uses real medical images for proposing a method intended to assist radiologists in the diagnostic process. The study consists of two domains of Pattern recognition, Classification and Content Based Retrieval. Mammogram images of breast cancer patients and placental images are used for this study. Cancer is a disaster to human race. The accuracy in characterizing images using simplified user friendly Computer Aided Diagnosis techniques helps radiologists in detecting cancers at an early stage. Breast cancer which accounts for the major cause of cancer death in women can be fully cured if detected at an early stage. Studies relating to placental characteristics and abnormalities are important in foetal monitoring. The diagnostic variability in sonographic examination of placenta can be overlooked by detailed placental texture analysis by focusing on placental grading. The work aims on early breast cancer detection and placental maturity analysis. This dissertation is a stepping stone in combing various application domains of healthcare and technology.

Using appearance and context for outdoor scene object classification

We propose a probabilistic object classifier for outdoor scene analysis as a first step in solving the problem of scene context generation. The method begins with a top-down control, which uses the previously learned models (appearance and absolute location) to obtain an initial pixel-level classification. This information provides us the core of objects, which is used to acquire a more accurate object model. Therefore, their growing by specific active regions allows us to obtain an accurate recognition of known regions. Next, a stage of general segmentation provides the segmentation of unknown regions by a bottom-strategy. Finally, the last stage tries to perform a region fusion of known and unknown segmented objects. The result is both a segmentation of the image and a recognition of each segment as a given object class or as an unknown segmented object. Furthermore, experimental results are shown and evaluated to prove the validity of our proposal

Modeling and Classifying Breast Tissue Density in Mammograms

We present a new approach to model and classify breast parenchymal tissue. Given a mammogram, first, we will discover the distribution of the different tissue densities in an unsupervised manner, and second, we will use this tissue distribution to perform the classification. We achieve this using a classifier based on local descriptors and probabilistic Latent Semantic Analysis (pLSA), a generative model from the statistical text literature. We studied the influence of different descriptors like texture and SIFT features at the classification stage showing that textons outperform SIFT in all cases. Moreover we demonstrate that pLSA automatically extracts meaningful latent aspects generating a compact tissue representation based on their densities, useful for discriminating on mammogram classification. We show the results of tissue classification over the MIAS and DDSM datasets. We compare our method with approaches that classified these same datasets showing a better performance of our proposal

Efficacy of several digital radiographic imaging systems for laboratory determination of endodontic file length

P>AimTo compare the efficacy of different digital radiographic imaging systems for determining the length of endodontic files.MethodologyK-type endodontic files were introduced into the canals of 40 extracted human permanent single-rooted teeth and fixed in place at random lengths. The teeth were radiographed using Digora Optime (R), CygnusRay MPS (R) and CDR Wireless (R) digital imaging systems. Six observers measured every file length in all the images and repeated this procedure in 50% of the image samples, and assigned a score to the level of difficulty found. Analysis of variance for differences between digital systems and Tukey's test were performed. The level of intraobserver agreement was measured by intraclass correlation. The assigned scores were evaluated by Kruskal-Wallis and Dunn's tests.ResultsThe CDR Wireless values did not differ significantly from the actual lengths and the CygnusRay MPS values. The Digora Optime system was significantly different from the others and overestimated the values (P < 0.05). The Digora Optime was significantly easier to use for taking measurements and the CygnusRay MPS the most difficult (P < 0.05). All digital radiographic imaging systems showed excellent agreement with the Intraclass Correlation Coefficient > 0.95.ConclusionsThe three digital radiographic imaging systems were precise. The CDR Wireless system was significantly more accurate in determining endodontic file lengths, and similarly to Digora Optime, was considered the least difficult to use when assessing endodontic file lengths.

Recursive hierarchic segmentation analysis of bone mineral density changes on digital panoramic images

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Biological shape analysis by digital curvature

This paper reports the novel application of digital curvature as a feature for morphological characterization and classification of landmark shapes. By inheriting several unique features of the continuous curvature, the digital curvature provides invariance to translations, rotations, local shape deformations, and is easily made tolerant to scaling. In addition, the bending energy, a global shape feature, can be directly estimated from the curvature values. The application of these features to analyse patterns of cranial morphological geographic differentiation in the rodent species Thrichomys apereoides has led to encouraging results, indicating a close correspondence between the geographical and morphological distributions. (C) 2003 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

Evaluation epidermal p53 immunostaining by digital image analysis

Digital techniques have been developed and validated to assess semiquantitatively immunohistochemical nuclear staining. Currently visual classification is the standard for qualitative nuclear evaluation. Analysis of pixels that represents the immunohistochemical labeling can be more sensitive, reproducible and objective than visual grading. This study compared two semiquantitative techniques of digital image analysis with three techniques of visual analysis imaging to estimate the p53 nuclear immunostaining. Methods: Sixty-three sun-exposed forearm-skin biopsies were photographed and submitted to three visual analyses of images: the qualitative visual evaluation method (0 to 4 +), the percentage of labeled nuclei and HSCORE. Digital image analysis was performed using ImageJ 1.45p; the density of nuclei was scored per ephitelial area (DensNU) and the pixel density was established in marked suprabasal epithelium (DensPSB). Results: Statistical significance was found in: the agreement and correlation among the visual estimates of evaluators, correlation among the median visual score of the evaluators, the HSCORE and the percentage of marked nuclei with the DensNU and DensPSB estimates. DensNU was strongly correlated to the percentage of p53-marked nuclei in the epidermis, and DensPSB with the HSCORE. Conclusion: The parameters presented herein can be applied in routine analysis of immunohistochemical nuclear staining of epidermis. © 2012 John Wiley & Sons A/S.

Representação, acesso, uso e reuso da imagem digital

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)