Lesion detection in demoscopy images with novel density-based and active contour approaches

<p>Abstract</p> <p>Background</p> <p>Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma and other pigmented skin lesions. Automated assessment tools for dermoscopy images have become an important field of research mainly because of inter- and intra-observer variations in human interpretation. One of the most important steps in dermoscopy image analysis is the detection of lesion borders, since many other features, such as asymmetry, border irregularity, and abrupt border cutoff, rely on the boundary of the lesion. </p> <p>Results</p> <p>To automate the process of delineating the lesions, we employed Active Contour Model (ACM) and boundary-driven density-based clustering (BD-DBSCAN) algorithms on 50 dermoscopy images, which also have ground truths to be used for quantitative comparison. We have observed that ACM and BD-DBSCAN have the same border error of 6.6% on all images. To address noisy images, BD-DBSCAN can perform better delineation than ACM. However, when used with optimum parameters, ACM outperforms BD-DBSCAN, since ACM has a higher recall ratio.</p> <p>Conclusion</p> <p>We successfully proposed two new frameworks to delineate suspicious lesions with i) an ACM integrated approach with sharpening and ii) a fast boundary-driven density-based clustering technique. ACM shrinks a curve toward the boundary of the lesion. To guide the evolution, the model employs the exact solution <abbrgrp><abbr bid="B27">27</abbr></abbrgrp> of a specific form of the Geometric Heat Partial Differential Equation <abbrgrp><abbr bid="B28">28</abbr></abbrgrp>. To make ACM advance through noisy images, an improvement of the model’s boundary condition is under consideration. BD-DBSCAN improves regular density-based algorithm to select query points intelligently.</p

A Regional Reduction in Ito and IKACh in the Murine Posterior Left Atrial Myocardium Is Associated with Action Potential Prolongation and Increased Ectopic Activity.

BACKGROUND: The left atrial posterior wall (LAPW) is potentially an important area for the development and maintenance of atrial fibrillation. We assessed whether there are regional electrical differences throughout the murine left atrial myocardium that could underlie regional differences in arrhythmia susceptibility. METHODS: We used high-resolution optical mapping and sharp microelectrode recordings to quantify regional differences in electrical activation and repolarisation within the intact, superfused murine left atrium and quantified regional ion channel mRNA expression by Taqman Low Density Array. We also performed selected cellular electrophysiology experiments to validate regional differences in ion channel function. RESULTS: Spontaneous ectopic activity was observed during sustained 1Hz pacing in 10/19 intact LA and this was abolished following resection of LAPW (0/19 resected LA, P<0.001). The source of the ectopic activity was the LAPW myocardium, distinct from the pulmonary vein sleeve and LAA, determined by optical mapping. Overall, LAPW action potentials (APs) were ca. 40% longer than the LAA and this region displayed more APD heterogeneity. mRNA expression of Kcna4, Kcnj3 and Kcnj5 was lower in the LAPW myocardium than in the LAA. Cardiomyocytes isolated from the LAPW had decreased Ito and a reduced IKACh current density at both positive and negative test potentials. CONCLUSIONS: The murine LAPW myocardium has a different electrical phenotype and ion channel mRNA expression profile compared with other regions of the LA, and this is associated with increased ectopic activity. If similar regional electrical differences are present in the human LA, then the LAPW may be a potential future target for treatment of atrial fibrillation

Colorization and Automated Segmentation of Human T2 MR Brain Images for Characterization of Soft Tissues

Characterization of tissues like brain by using magnetic resonance (MR) images and colorization of the gray scale image has been reported in the literature, along with the advantages and drawbacks. Here, we present two independent methods; (i) a novel colorization method to underscore the variability in brain MR images, indicative of the underlying physical density of bio tissue, (ii) a segmentation method (both hard and soft segmentation) to characterize gray brain MR images. The segmented images are then transformed into color using the above-mentioned colorization method, yielding promising results for manual tracing. Our color transformation incorporates the voxel classification by matching the luminance of voxels of the source MR image and provided color image by measuring the distance between them. The segmentation method is based on single-phase clustering for 2D and 3D image segmentation with a new auto centroid selection method, which divides the image into three distinct regions (gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) using prior anatomical knowledge). Results have been successfully validated on human T2-weighted (T2) brain MR images. The proposed method can be potentially applied to gray-scale images from other imaging modalities, in bringing out additional diagnostic tissue information contained in the colorized image processing approach as described

Automated Three-Dimensional Detection and Shape Classification of Dendritic Spines from Fluorescence Microscopy Images

A fundamental challenge in understanding how dendritic spine morphology controls learning and memory has been quantifying three-dimensional (3D) spine shapes with sufficient precision to distinguish morphologic types, and sufficient throughput for robust statistical analysis. The necessity to analyze large volumetric data sets accurately, efficiently, and in true 3D has been a major bottleneck in deriving reliable relationships between altered neuronal function and changes in spine morphology. We introduce a novel system for automated detection, shape analysis and classification of dendritic spines from laser scanning microscopy (LSM) images that directly addresses these limitations. The system is more accurate, and at least an order of magnitude faster, than existing technologies. By operating fully in 3D the algorithm resolves spines that are undetectable with standard two-dimensional (2D) tools. Adaptive local thresholding, voxel clustering and Rayburst Sampling generate a profile of diameter estimates used to classify spines into morphologic types, while minimizing optical smear and quantization artifacts. The technique opens new horizons on the objective evaluation of spine changes with synaptic plasticity, normal development and aging, and with neurodegenerative disorders that impair cognitive function

Preditores de mudanças nos regimes terapêuticos para o tratamento de Aids em crianças Predictors of changes in drug regimens for treating AIDS in children

Identificar as causas de mudanças de esquemas terapêuticos no tratamento de Aids em crianças contaminadas por transmissão vertical. Estudo caso-controle, sendo o grupo controle constituído de 49 crianças que não efetuaram troca de esquema antirretroviral, e o grupo caso, de 62 crianças que já efetuaram troca de janeiro de 2000 a dezembro de 2005. Foram utilizados dados do prontuário e roteiro de entrevista semi-estruturada. A análise dos dados foi pelo programa SPSS, usado teste qui-quadrado e regressão logística. As principais causas para mudança do tratamento foram: piora virológica (48,4%), imunológica (46,6%) e clínica (35,5%) dos pacientes. O ajuste dos dados através de análise da regressão logística demonstrou que a intolerância medicamentosa foi a variável que mais contribui para a mudança da medicação (OR ajustado:79,94; IC95%:6,28-1034,55) A adesão não foi apontada como responsável pela troca de tratamento antirretroviral, esse fato foi atribuído à organização do serviço e a atuação da equipe interdisciplinar.<br>To identify the causes of changes in therapeutic regimens for treating AIDS in children infected through vertical transmission. This was a case-control study in which the control group consisted of 49 children who had not had any changes to their antiretroviral regimen and the case group consisted of 62 children who had had changes between January 2000 and December 2005. Data from the patients' medical files and a semistructured interview were used. The data analysis was carried out using the SPSS software, and the chi-square test and logistic regression were applied. The main causes of changes in treatment were: increased viral load (48.4%), poor immunological response (46.6%) and clinical worsening (35.5%) of the patients. The adjustment of the data through logistic regression analysis showed that drug intolerance was the variable that contributed most to the change in medication (adjusted OR: 79.94; 95% CI: 6.28 -1034.55). Adherence was not shown to be responsible for changes in the anti-retroviral therapy, and this was attributable to the organization of the service and actions of the interdisciplinary team