Tumor de mama é a neoplasia mais freqüente em cadelas, entretanto, há controvérsias sobre os fatores que influenciam o desenvolvimento do tumor. O objetivo deste trabalho foi estudar o perfil das cadelas com tumor de mama atendidas no Hospital de Clínicas Veterinárias da Universidade Federal do Rio Grande do Sul (HCV-UFRGS). Foram coletados os dados de 85 cadelas apresentando neoplasias mamárias, entre junho de 1999 e maio de 2000. Foram analisados o tempo de evolução dos tumores, neoplasias anteriores, histórico reprodutivo, localização, tamanho dos nódulos, ulcerações, metástases pulmonares e resultados da histopatologia. A partir destes dados, 71,8% foram lesões malignas e, 28,2%, benignas. A maioria dos malignos foi de carcinomas e, dos benignos, adenoma. A idade média foi de 9 anos para as cadelas com tumores benignos e de 9 anos para os malignos. O progestágeno foi associado a um maior número de tumores benignos. As pseudocieses foram relacionadas a tumores de mama malignos. Tanto o uso de progestágenos como as pseudocieses foram relacionados com o aparecimento precoce de neoplasias mamárias em cadelas.

Breast ultrasound images have several attractive properties that make them an interesting tool in breast cancer detection. However, their intrinsic high noise rate and low contrast turn mass detection and segmentation into a challenging task. In this article, a fully automated two-stage breast mass segmentation approach is proposed. In the initial stage, ultrasound images are segmented using support vector machine or discriminant analysis pixel classification with a multiresolution pixel descriptor. The features are extracted using non-linear diffusion, bandpass filtering and scale-variant mean curvature measures. A set of heuristic rules complement the initial segmentation stage, selecting the region of interest in a fully automated manner. In the second segmentation stage, refined segmentation of the area retrieved in the first stage is attempted, using two different techniques. The AdaBoost algorithm uses a descriptor based on scale-variant curvature measures and non-linear diffusion of the original image at lower scales, to improve the spatial accuracy of the ROI. Active contours use the segmentation results from the first stage as initial contours. Results for both proposed segmentation paths were promising, with normalized Dice similarity coefficients of 0.824 for AdaBoost and 0.813 for active contours. Recall rates were 79.6% for AdaBoost and 77.8% for active contours, whereas the precision rate was 89.3% for both methods.

No abstract

Muscle texture analysis in Magnetic Resonance Imaging (MRI) has revealed a good correlation with typical histological changes resulting from neuromuscular disorders. In this research, we assess the effectiveness of several features in describing intramuscular texture alterations in cases of Collagen VI-related myopathy. A T1-weighted Turbo Spin-Echo MRI dataset was used (N subj = 26), consisting of thigh scans from subjects diagnosed with Ullrich Congenital Muscular Dystrophy or Bethlem Myopathy, with different severity levels, as well as healthy subjects. A total of 355 texture features were studied, including attributes derived from the Gray-Level Co-occurrence Matrix, the Run-Length Matrix, Wavelet and Gabor filters. The extracted features were ranked using the Support Vector Machine Recursive Feature Elimination (SVM-RFE) algorithm with Correlation Bias Reduction, prior to cross-validated classification with a Gaussian kernel SVM.