14,275 research outputs found
Experimental Assessment of Color Deconvolution and Color Normalization for Automated Classification of Histology Images Stained with Hematoxylin and Eosin
Histological evaluation plays a major role in cancer diagnosis and treatment. The appearance of H&E-stained images can vary significantly as a consequence of differences in several factors, such as reagents, staining conditions, preparation procedure and image acquisition system. Such potential sources of noise can all have negative effects on computer-assisted classification. To minimize such artefacts and their potentially negative effects several color pre-processing methods have been proposed in the literature—for instance, color augmentation, color constancy, color deconvolution and color transfer. Still, little work has been done to investigate the efficacy of these methods on a quantitative basis. In this paper, we evaluated the effects of color constancy, deconvolution and transfer on automated classification of H&E-stained images representing different types of cancers—specifically breast, prostate, colorectal cancer and malignant lymphoma. Our results indicate that in most cases color pre-processing does not improve the classification accuracy, especially when coupled with color-based image descriptors. Some pre-processing methods, however, can be beneficial when used with some texture-based methods like Gabor filters and Local Binary Patterns
Scene illumination classification based on histogram quartering of CIE-Y component
Despite the rapidly expanding research into various aspects of illumination estimation
methods, there are limited number of studies addressing illumination classification for
different purposes. The increasing demand for color constancy process, wide application
of it and high dependency of color constancy to illumination estimation makes this
research topic challenging. Definitely, an accurate estimation of illumination in the
image will provide a better platform for doing correction and finally will lead in better
color constancy performance. The main purpose of any illumination estimation
algorithm from any type and class is to estimate an accurate number as illumination. In
scene illumination estimation dealing with large range of illumination and small
variation of it is critical. Those algorithms which performed estimation carrying out lots
of calculation that leads in expensive methods in terms of computing resources. There
are several technical limitations in estimating an accurate number as illumination. In
addition using light temperature in all previous studies leads to have complicated and
computationally expensive methods. On the other hand classification is appropriate for
applications like photography when most of the images have been captured in a small set
of illuminants like scene illuminant. This study aims to develop a framework of image
illumination classifier that is capable of classifying images under different illumination
levels with an acceptable accuracy. The method will be tested on real scene images
captured with illumination level is measured. This method is a combination of physic
based methods and data driven (statistical) methods that categorize the images based on
statistical features extracted from illumination histogram of image. The result of
categorization will be validated using inherent illumination data of scene. Applying the
improving algorithm for characterizing histograms (histogram quartering) handed out
the advantages of high accuracy. A trained neural network which is the parameters are
tuned for this specific application has taken into account in order to sort out the image
into predefined groups. Finally, for performance and accuracy evaluation
misclassification error percentages, Mean Square Error (MSE), regression analysis and response time are used. This developed method finally will result in a high accuracy and
straightforward classification system especially for illumination concept. The results of
this study strongly demonstrate that light intensity with the help of a perfectly tuned
neural network can be used as the light property to establish a scene illumination
classification system
Color Constancy Convolutional Autoencoder
In this paper, we study the importance of pre-training for the generalization
capability in the color constancy problem. We propose two novel approaches
based on convolutional autoencoders: an unsupervised pre-training algorithm
using a fine-tuned encoder and a semi-supervised pre-training algorithm using a
novel composite-loss function. This enables us to solve the data scarcity
problem and achieve competitive, to the state-of-the-art, results while
requiring much fewer parameters on ColorChecker RECommended dataset. We further
study the over-fitting phenomenon on the recently introduced version of
INTEL-TUT Dataset for Camera Invariant Color Constancy Research, which has both
field and non-field scenes acquired by three different camera models.Comment: 6 pages, 1 figure, 3 table
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