RESUMO
This paper presents a computer-aided approach for nodule delineation in thyroid ultrasound (US) images. The developed algorithm is based on a novel active contour model, named variable background active contour (VBAC), and incorporates the advantages of the level set region-based active contour without edges (ACWE) model, offering noise robustness and the ability to delineate multiple nodules. Unlike the classic active contour models that are sensitive in the presence of intensity inhomogeneities, the proposed VBAC model considers information of variable background regions. VBAC has been evaluated on synthetic images, as well as on real thyroid US images. From the quantification of the results, two major impacts have been derived: 1) higher average accuracy in the delineation of hypoechoic thyroid nodules, which exceeds 91%; and 2) faster convergence when compared with the ACWE model.
Assuntos
Inteligência Artificial , Interpretação de Imagem Assistida por Computador/métodos , Modelos Biológicos , Reconhecimento Automatizado de Padrão/métodos , Nódulo da Glândula Tireoide/diagnóstico por imagem , Ultrassonografia/métodos , Simulação por Computador , Humanos , Reprodutibilidade dos Testes , Sensibilidade e EspecificidadeRESUMO
Today 95% of all gastrointestinal carcinomas are believed to arise from adenomas. The early detection of adenomas could prevent their evolution to cancer. A novel system for the support of the detection of adenomas in gastrointestinal video endoscopy is presented. Unlike other systems, it accepts standard low-resolution video input thus requiring less computational resources and facilitating both portability and the potential to be used in telemedicine applications. It combines intelligent processing techniques of SVMs and color-texture analysis methodologies into a sound pattern recognition framework. Concerning the system's accuracy this was measured using ROC analysis and found to exceed 94%.
Assuntos
Adenoma/diagnóstico , Inteligência Artificial , Pólipos do Colo/diagnóstico , Diagnóstico por Computador/instrumentação , Endoscópios Gastrointestinais , Processamento de Imagem Assistida por Computador/métodos , Pólipos/diagnóstico , Neoplasias Gástricas/diagnóstico , Gravação em Vídeo/instrumentação , Tomada de Decisões Assistida por Computador , Sistemas Inteligentes , Humanos , Processamento de Imagem Assistida por Computador/instrumentação , Microcomputadores , SoftwareRESUMO
We present an approach to the detection of tumors in colonoscopic video. It is based on a new color feature extraction scheme to represent the different regions in the frame sequence. This scheme is built on the wavelet decomposition. The features named as color wavelet covariance (CWC) are based on the covariances of second-order textural measures and an optimum subset of them is proposed after the application of a selection algorithm. The proposed approach is supported by a linear discriminant analysis (LDA) procedure for the characterization of the image regions along the video frames. The whole methodology has been applied on real data sets of color colonoscopic videos. The performance in the detection of abnormal colonic regions corresponding to adenomatous polyps has been estimated high, reaching 97% specificity and 90% sensitivity.