The aim of the presented system is simplification and speedup of the daily pathological examination routine. The system combines telepathology with computer-aided diagnostics algorithms. To the best of our knowledge, this is the first approach proposing such a comprehensive method. Our system is designed to accumulate knowledge through a learning process during diagnostics. Our system targets image acquisition and interpretation stages. The image acquisition subsystem solves various problems related to microscopical slide digitization such as biomedical image registration, data representation, and processing. The interpretation subsystem is based on Gabor filter texture features as well as on color features. A support vector machine classifier together with a feature selection is used for computer-aided diagnostics. The system design allows easy adaptation to a wide range of microscopical pathology examinations. The system is easily deployed and scaled. It has a low support cost and can aggregate a wide range of existing hardware. The experimental validation of the system is based on a database of more than three thousand samples. During the experimental evaluation, the system exhibited successful interaction with a pathologist.
In this paper, we address the problem of fully automated decomposition of hyperspectral images for transmission light microscopy. The hyperspectral images are decomposed into spectrally homogeneous compounds. The resulting compounds are described by their spectral characteristics and optical density. We present the multiplicative physical model of image formation in transmission light microscopy, justify reduction of a hyperspectral image decomposition problem to a blind source separation problem, and provide method for hyperspectral restoration of separated compounds. In our approach, dimensionality reduction using principal component analysis (PCA) is followed by a blind source separation (BSS) algorithm. The BSS method is based on sparsifying transformation of observed images and relative Newton optimization procedure. The presented method was verified on hyperspectral images of biological tissues. The method was compared to the existing approach based on nonnegative matrix factorization. Experiments showed that the presented method is faster and better separates the biological compounds from imaging artifacts. The results obtained in this work may be used for improving automatic microscope hardware calibration and computer-aided diagnostics.
In microscopy, regions of interest are usually much smaller than the whole slide area. Various microscopy related medical applications, such as telepathology and computer aided diagnosis, are liable to benefit greatly from microscope auto positioning on previously defined regions of interest. In this paper, we present a method for image-based auto positioning on a microscope slide. The method is based on localization of a microscopic query image using a previously acquired slide map. It uses geometric hashing, a highly efficient technique drawn from the object recognition field. The algorithm exhibits high tolerance to possible variations in visual appearance due to slide rotations, scaling and illumination changes. Experimental results indicate high reliability of the algorithm
The aim of the presented system is simplification of the daily pathological routine of prostatic cancer diagnostics. The system combines telepathology with computer-aided diagnostics algorithms. To the best of our knowledge, this is the first approach proposing such a comprehensive method. Our system is designed to accumulate knowledge in learning process during diagnostics. Our system targets image acquisition and interpretation stages. The image acquisition subsystem solves various problems related to microscopical slide digitization like biomedical image registration, data representation, and processing. The interpretation subsystem bases on Gabor filter texture features as well as on color features. A support vector machine classifier together with feature selection is used for computer-aided diagnostics. The experimental validation of the system bases on a database of more than three thousand samples.During the experimental evaluation, the system exhibited successful interaction with a pathologist.
In microscopy, regions of interest are usually much smaller than the whole slide area. Various microscopy related medical applications are liable to benefit greatly from microscope auto positioning in previously defined regions of interest. In this paper we present a method for image-based auto positioning on a microscope slide. The method is based on localization of a microscopic query image using a previously acquired slide map. It uses geometric hashing, a highly efficient technique drawn from the object recognition field. The algorithm exhibits high tolerance to possible variations in visual appearance due to slide rotations, scaling and illumination changes. Experimental results indicate high reliability of the algorithm.
We call a set of vectors z[k] 2 R m jointly sparse, when for the most of them all m components are simultaneously [close to] zero. When recovering this set from [indirect] noisy observations using variational approach, joint sparsity prior can be expressed via convex penalty term P k kz[k]k2. In this work we explore joint sparsity in the context of blind source separation problem X = AS +», where mixing matrix A is and sources S are unknown. We suppose S to have sparse representation coefficients C in some given signal frame (dictionary) ': S = C'. In this case, the mixtures’ coefficients AC are jointly sparse, therefore we can recover them robustly without knowledge of mixing matrix A, just using joint sparsity prior and solving a convex optimization problem. Another use of joint sparsity comes when the sources themselves are not scalars, but vectors (for example, color images: three RGB color layers usually have spatial similarities, therefore their [wavelet-type] representations are jointly sparse.) Our simulations show efficiency of the presented approach.
In this paper we discuss the use of clustering techniques to enhance the user experience and thus the success of collaborative tagging services. We show that clustering techniques can improve the user experience of current tagging services. We first describe current limitations of tagging services, second, we give an overview of existing approaches. We then describe the algorithms we used for tag clustering and give experimental results. Finally, we explore the use of several techniques to identify semantically related tags.
This paper presents an automatic system for morphological screening of the bladder cells. This system is intended to increase efficiency of the subsequent fluorescence in situ hybridization examination by limiting the number of suspicious cells. The system works in two major phases. The first phase is slide scanning. The second stage includes cells detection and morphological analysis. Both stages refine their results using supervised classification algorithm. The developed method was tested on nine microscopical slides, containing more than 12000 manually labeled cells. The results provided by the system were compared to the ground truth labeled by a human expert.
