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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. [Copyright &y& Elsevier]

Abstract: In medical imaging, many applications require visualization and/or analysis of three-dimensional (3D) objects (e.g. organs). At same time, object definition often requires considerable user assistance. In this process, objects are usually defined in an iterative way and their visualization during the process is very important to guide the user’s actions for the next iteration. The usual procedure provides slice visualization during object definition (segmentation) and 3D visualization afterward. In this paper, we propose and evaluate efficient methods to provide 3D visualization during iterative object definition. The methods combine the differential image foresting transform for segmentation with voxel splatting/ray casting for visualization. [Copyright &y& Elsevier]

Objective: The purpose of this study was to identify the presence of periodontal microrganisms in 35 renal transplant patients before the transplant procedure. Study design: At each time point, clinical parameters were recorded and subgingival plaque samples were collected from 4 different sites at days 30 and 90 after surgery. Samples were plated onto selective and nonselective media to determine total colony counts and the presence of putative periodontal pathogens. After transplant surgery, patients received immunosuppressive therapy. Results: Statistical analysis of the microbiologic data showed significant changes between time points. An increase in total counts of microrganisms was observed on day 90 after surgery. As a side effect of cyclosporine, 14 patients developed gingival overgrowth. Beta-hemolytic Streptococcus was more frequently detected in patients who did not present gingival overgrowth 90 days after surgery. Conclusion: Quantitative and qualitative changes of the subgingival microflora can occur 90 days after transplant surgery, while patients are still under immunosupressive drugs. [Copyright &y& Elsevier]

Abstract: The notion of skeleton plays a major role in shape analysis. Some usually desirable characteristics of a skeleton are: centered, thin, homotopic, and sufficient for the reconstruction of the original object. The Euclidean medial axis presents all these characteristics in a continuous framework. In the discrete case, the exact Euclidean medial axis (MA) is also sufficient for reconstruction and centered. It no longer preserves homotopy but it can be combined with a homotopic thinning to generate homotopic skeletons. The thinness of the MA, however, may be discussed. In this paper, we present the definition of the exact Euclidean medial axis in higher resolution, which has the same properties as the MA but with a better thinness characteristic, against the price of rising resolution. We provide and prove an efficient algorithm to compute it. [Copyright &y& Elsevier]

• A method to assess quality on volumetric MR image is proposed. • The method relies on deep CNN to rate the motion artifacts on MR image. • The resulting probability is successfully correlated to the image degradation severity. Motion artifacts on magnetic resonance (MR) images degrade image quality and thus negatively affect clinical and research scanning. Considering the difficulty in preventing patient motion during MR examinations, the identification of motion artifact has attracted significant attention from researchers. We propose an automatic method for the evaluation of motion corrupted images using a deep convolutional neural network (CNN). Deep CNNs has been used widely in image classification tasks. While such methods require a significant amount of annotated training data, a scarce resource in medical imaging, the transfer learning and fine-tuning approaches allow us to use a smaller amount of data. Here we selected four renowned architectures, initially trained on Imagenet contest dataset, to fine-tune. The models were fine-tuned using patches from an annotated dataset composed of 68 T1-weighted volumetric acquisitions from healthy volunteers. For training and validation 48 images were used, while the remaining 20 images were used for testing. Each architecture was fine-tuned for each MR axis, detecting the motion artifact per patches from the three orthogonal MR acquisition axes. The overall average accuracy for the twelve models (three axes for each of four architecture) was 86.3 %. As our goal was to detect fine-grained corruption in the image, we performed an extensive search on lower layers from each of the four architectures, since they filter small regions in the original input. Experiments showed that architectures with fewer layers than the original ones reported the better results for image patches with an overall average accuracy of 90.4 %. The accuracies per architecture were similar so we decided to explore all four architectures performing a result consensus. Also, to determine the probability of motion artifacts presence on the whole acquisition a combination of the three axes were performed. The final architecture consists of an artificial neural network (ANN) classifier combining all models from the four shallower architectures, which overall acquisition-based accuracy was 100.0 %. The proposed method generalization was tested using three different MR data: (1) MR image acquired in epilepsy patients (93 acquisitions); (2) MR image presenting susceptibility artifact (22 acquisitions); and (3) MR image acquired from different scanner vendor (20 acquisitions). The achieved acquisition-based accuracy on generalization tests (1) 90.3 % , (2) 63.6 % , and (3) 75.0 %) suggests that domain adaptation is necessary. Our proposed method can be rapidly applied to large amounts of image data, providing a motion probability p ∈ 0 , 1 per acquisition. This method output can be used as a scale to identify the motion corrupted images from the dataset, thus minimizing the time spent on visual quality control. [ABSTRACT FROM AUTHOR]