RESUMEN
Feature detection is used in many computer vision applications such as image segmentation, object recognition, and image retrieval. For these applications, robustness with respect to shadows, shading, and specularities is desired. Features based on derivatives of photometric invariants, which we will call full invariants, provide the desired robustness. However, because computation of photometric invariants involves nonlinear transformations, these features are Instable and, therefore, impractical for many applications. We propose a new class of derivatives which we refer to as quasi-invariants. These quasi-invariants are derivatives which share with full photometric invariants the property that they are insensitive for certain photometric edges, such as shadows or specular edges, but without the inherent instabilities of full photometric invariants. Experiments show that the quasi-invariant derivatives are less sensitive to noise and introduce less edge displacement than full invariant derivatives. Moreover, quasi-invariants significantly outperform the full invariant derivatives in terms of discriminative power.
Asunto(s)
Algoritmos , Inteligencia Artificial , Interpretación de Imagen Asistida por Computador/métodos , Almacenamiento y Recuperación de la Información/métodos , Reconocimiento de Normas Patrones Automatizadas/métodos , Fotometría/métodos , Aumento de la Imagen/métodos , Reproducibilidad de los Resultados , Sensibilidad y EspecificidadRESUMEN
BACKGROUND: A critical step in automatic microscopy is focusing. This report describes a robust and fast autofocus approach useful for a wide range of microscopic modalities and preparations. METHODS: The focus curve is measured over the complete focal range, reducing the chance that the best focus position is determined by dust or optical artifacts. Convolution with the derivative of a Gaussian smoothing function reduces the effect of noise on the focus curve. The influence of mechanical tolerance is accounted for. RESULTS: The method is shown to be robust in fluorescence, bright-field and phase contrast microscopy, in fixed and living cells, as well as in fixed tissue. The algorithm was able to focus accurately within 2 or 3 s, even under extremely noisy and low contrast imaging conditions. CONCLUSIONS: The proposed method is generally applicable in light microscopy, whenever the image information content is sufficient. The reliability of the autofocus method allows for unattended operation on a large scale.
Asunto(s)
Microscopía/métodos , Animales , Automatización , Caenorhabditis elegans , Estudios de Evaluación como Asunto , Humanos , Miocardio/citología , Neuronas/citología , Control de Calidad , RatasRESUMEN
BACKGROUND: Characterization of tissues can be based on the topographical relationship between the cells. Such characterization should be insensitive to distortions intrinsic to the acquisition of biological preparation. In this paper, a method for the robust segmentation of tissues based on the spatial distribution of cells is proposed. MATERIALS AND METHODS: The neighborhood of each cell in the tissue is modeled by the distances to the surrounding cells. Comparison with an example or prototype neighborhood reveals topographical similarity between tissue and prototype. Processing of all cells in the tissue extracts the regions with tissue architecture similar to the given example. RESULTS: Comparison with other topographical-segmentation methods shows that the proposed method is better suited for partitioning tissue architecture. As an example, the quantification of the structural integrity in rat hippocampi after ischemia is demonstrated. In contrast to other methods, the algorithm correlates well with expert evaluation. CONCLUSIONS: The present method reduces the nonbiological variation in the analysis of tissue sections and thus improves confidence in the result. The method can be applied to any field where regular patterns have to be detected, as long as the directional distribution of neighbors may be neglected.