Marco António Garcia de Carvalho

Morphological segmentation of yeast by image analysis

Abstract An image analysis method has been developed to segment yeast cells. Yeasts belong to the taxonomic group fungi and have been used on fuel and food industry, for example. The method is capable of segmenting yeast cells based on Watershed Transform and space-scale analysis of the Tree of Critical Lakes. We analise hierarchical, geometric and gray-scale properties of the Tree of Critical Lakes . We show experimental results for one group of yeast images obtained from the School ofFood Engineering at Unicamp, Brazil. Comparison shows that the proposed method provides cells with area 10% lower than traditional approach. Moreover, this approach preserves the cells contour, an important feature because of the performance of bioreactors and other chemical processes are greatly influenced by their morphological character.

Segmentation of images of yeast cells by scale-space analysis

We present an hierarchical approach to segment images of yeast cells based on watershed and space-scale analysis. Yeasts belong to an important fungi class and the performance of bioreactors and other chemical processes are greatly influenced by their morphological character. The method proposed is capable of segmenting yeast cells based on the analysis of survival time, shape and gray-scale features of the Tree of Critical Lakes. We show experimental results for one group of yeast images obtained from the School of Food Engineering at Unicamp, Brazil. Preliminary comparison shows that the proposed method provides cells with area 10% lower than traditional approach, as well as its contours preserved.

Image segmentation using quadtree-based similarity graph and normalized cut

The graph cuts in image segmentation have been widely used in recent years because it regards the problem of image partitioning as a graph partitioning issue, a well-known problem in graph theory. The normalized cut approach uses spectral graph properties of the image representative graph to bipartite it into two or more balanced subgraphs, achieving in some cases good results when applying this approach to image segmentation. In this work, we discuss the normalized cut approach and propose a Quadtree based similarity graph as the input graph in order to segment images. This representation allow us to reduce the cardinality of the similarity graph. Comparisons to the results obtained by other graph similarity representation were also done in sampled images.

Effect of luting agent on the load to failure and accelerated-fatigue resistance of lithium disilicate laminate veneers

OBJECTIVE The aim of this study was to investigate the influence of the luting agent on the application of laminate veneers (LVs) in an accelerated fatigue and load-to-failure test after thermo-cyclic aging. METHODS Sound maxillary central incisors (N=40) were randomly divided into four groups to receive LVs (Li2Si2O5) that were adhesively bonded: Group CEMF: Adhesive cement (Variolink Esthetic LC), fatigue test; Group CEMLF: Adhesive cement, load-to-failure test; Group COMF: Resin composite (Enamel HFO), fatigue test; Group COMLF: Resin composite, load-to-failure test. The specimens were thermo-mechanically aged (1.2×106 cycles at 1.7Hz/50N, 8000 cycles 5-55°C) and then subjected to either accelerated fatigue (5Hz, 25N increasing after each 500 cycles) or load to failure (1mm/min). Failure types were classified and data analyzed using chi-square, Kaplan Meier survival, Log Rank (Mantel-Cox) and independent-samples t-test. RESULTS After thermo-mechanical aging, fracture resistance (p<0.000) was higher in the composite groups. Kaplan Meier survival rates showed significant difference (p<0.001) between the composite (mean load: 1165N; mean cycles: 22.595) and the cement groups (mean load: 762.5N; mean cycles: 14.569). The same differences were observed in the load to failure test (cement M=629.4N, SD±212.82 and composite M=927.59N, SD±261.06); t (18)=-2.80, p=0.01. Failure types were observed as fractures and chipping in group CEMF, all other groups were predominantly adhesive failures between the luting agent and the laminate veneer. SIGNIFICANCE The delivery of laminate veneers using a direct restorative composite rather than a resin cement resulted in significantly less chipping and fractures, higher fracture strength in both accelerated fatigue and load-to-failure.

Image segmentation through combined methods: Watershed transform, unsupervised distance learning and Normalized Cut

Research on image processing has shown that combining segmentation methods may lead to a solid approach to extract semantic information from different sort of images. Within this context, the Normalized Cut (NCut) is usually used as a final partitioning tool for graphs modeled in some chosen method. This work explores the Watershed Transform as a modeling tool, using different criteria of the hierarchical Watershed to convert an image into an adjacency graph. The Watershed is combined with an unsupervised distance learning step that redistributes the graph weights and redefines the Similarity matrix, before the final segmentation step using NCut. Adopting the Berkeley Segmentation Data Set and Benchmark as a background, our goal is to compare the results obtained for this method with previous work to validate its performance.

Combining Hierarchical Structures on Graphs and Normalized Cut for Image Segmentation

Extensive studies have been accomplished for image segmentation. The segmentation algorithms are commonly categorized according to the image characteristics borders and regions (Gonzales & Woods, 2000). In the first one, the image is divided based on its discontinuities, i.e., the places where abrupt intensity changes occur. Regarding to the region segmentation, this happenwhen there are similarities of color or texture, for example, between neighboring pixels. In spite of these categories, the problem is to find a good partitioning of an image among several possible to be achieved.

Intelligent Digital Built Heritage Models: An Approach from Image Processing and Building Information Modelling Technology.

Conservation and maintenance of historic buildings have exceptional requirements and need a detailed diagnosis and an accurate as-is documentation. This paper reports the use of Unmanned Aerial Vehicle (UAV) imagery to create an Intelligent Digital Built Heritage Model (IDBHM) based on Building Information Modeling (BIM) technology. Our work outlines a model-driven approach based on UAV data acquisition, photogrammetry, post-processing and segmentation of point clouds to promote partial automation of BIM modeling process. The methodology proposed was applied to a historical building facade located in Brazil. A qualitative and quantitative assessment of the proposed segmentation method was undertaken through the comparison between segmented clusters and as-designed documents, also as between point clouds and ground control points. An accurate and detailed parametric IDBHM was created from high-resolution Dense Surface Model (DSM). This Model can improve conservation and rehabilitation works. The results demonstrate that the proposed approach yields good results in terms of effectiveness in the clusters segmentation, compared to the as-designed

Quantitative image analysis of acoustic tomography in woods

The development of acoustic techniques for wood analysis through tomography has enabled the generation of images by means of nondestructive techniques. These images allow for the evaluation of the internal condition of wood trunks. This type of evaluation provides valuable information since the internal defects (e.g. holes) in the wood are difficult to identify—especially in its early stages of development. Whereas there is a substantial body of work that aims to improve these images by applying new interpolation and inspection techniques, the assessment of these techniques has traditionally been carried out via a bare visual analysis or inspection of the real wood trunk. In this work, an approach is proposed to quantitatively assess interpolation methods regarding their ability to correctly detect faults in the wood. This approach is based on a confusion matrix that allows for the computation of accuracy, reliability and recall. An experiment is presented using images from the cross-section of wood trunks generated by two interpolation methods applied for internal-hole detection: (1) an interpolation method using surrounding points and (2) the Ellipse Based Spatial Interpolation. The results demonstrated the effectiveness of the approach in quantitatively assessing and comparing these methods.

Segmenting High-quality Digital Images of Stomata using the Wavelet Spot Detection and the Watershed Transform.

Stomata are cells mostly found in plant leaves, stems and other organs. They are responsible for controlling the gas exchange process, i.e. the plant absorbs air and water vapor is released through transpiration. Therefore, stomata characteristics such as size and shape are important parameters to be taken into account. In this paper, we present a method (aiming at improved efficiency) to detect and count stomata based on the analysis of the multi-scale properties of the Wavelet, including a spot detection task working in the CIELab colorspace. We also segmented stomata images using the Watershed Transform, assigning each spot initially detected as a marker. Experiments with real and high-quality images were conducted and divided in two phases. In the first, the results were compared to both manual enumeration and another recent method existing in the literature, considering the same dataset. In the second, the segmented results were compared to a gold standard provided by a specialist using the F-Measure. The experimental results demonstrate that the proposed method results in better effectiveness for both stomata detection and segmentation.

Adding GLCM Texture Analysis to a Combined Watershed Transform and Graph Cut Model for Image Segmentation

Texture analysis is an important step in pattern recognition, image processing and computer vision systems. This work proposes an unsupervised approach to segment digital images combining the Watershed Transform and Normalized Cut in graphs (NCut) using texture information obtained from the Gray-Level Co-occurrence Matrix (GLCM). We corroborate the enhancement of image segmentation by means of the addition of texture analysis through several experiments carried out using the BSDS500 Berkeley dataset. For example, an improvement of 7% and 12% was found in relation to the Combined Watershed+NCut and Quadtree techniques, respectively. The overall performance of the proposed approach was indicated by the F-Measure through comparisons against other important segmentation methods.