Statistical methods for tissue array images - algorithmic scoring and co-training

Recent advances in tissue microarray technology have allowed immunohistochemistry to become a powerful medium-to-high throughput analysis tool, particularly for the validation of diagnostic and prognostic biomarkers. However, as study size grows, the manual evaluation of these assays becomes a prohibitive limitation; it vastly reduces throughput and greatly increases variability and expense. We propose an algorithm - Tissue Array Co-Occurrence Matrix Analysis (TACOMA) - for quantifying cellular phenotypes based on textural regularity summarized by local inter-pixel relationships. The algorithm can be easily trained for any staining pattern, is absent of sensitive tuning parameters and has the ability to report salient pixels in an image that contribute to its score. Pathologists' input via informative training patches is an important aspect of the algorithm that allows the training for any specific marker or cell type. With co-training, the error rate of TACOMA can be reduced substantially for a very small training sample (e.g., with size 30). We give theoretical insights into the success of co-training via thinning of the feature set in a high-dimensional setting when there is "sufficient" redundancy among the features. TACOMA is flexible, transparent and provides a scoring process that can be evaluated with clarity and confidence. In a study based on an estrogen receptor (ER) marker, we show that TACOMA is comparable to, or outperforms, pathologists' performance in terms of accuracy and repeatability.Comment: Published in at http://dx.doi.org/10.1214/12-AOAS543 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Multivariate modality inference using Gaussian kernel

The number of modes (also known as modality) of a kernel density estimator (KDE) draws lots of interests and is important in practice. In this paper, we develop an inference framework on the modality of a KDE under multivariate setting using Gaussian kernel. We applied the modal clustering method proposed by [1] for mode hunting. A test statistic and its asymptotic distribution are derived to assess the significance of each mode. The inference procedure is applied on both simulated and real data sets

Off-line Signature Verification Based on Fusion of Grid and Global Features Using Neural Networks

Signature is widely used and developed area of research for personal verification and authentication. In this paper Off-line Signature Verification Based on Fusion of Grid and Global Features Using Neural Networks (SVFGNN) is presented. The global and grid features are fused to generate set of features for the verification of signature. The test signature is compared with data base signatures based on the set of features and match/non match of signatures is decided with the help of Neural Network. The performance analysis is conducted on random, unskilled and skilled signature forgeries along with genuine signatures. It is observed that FAR and FRR results are improved in the proposed method compared to the existing algorithm

Automatic analysis of electronic drawings using neural network

Neural network technique has been found to be a powerful tool in pattern recognition. It captures associations or discovers regularities with a set of patterns, where the types, number of variables or diversity of the data are very great, the relationships between variables are vaguely understood, or the relationships are difficult to describe adequately with conventional approaches. In this dissertation, which is related to the research and the system design aiming at recognizing the digital gate symbols and characters in electronic drawings, we have proposed: (1) A modified Kohonen neural network with a shift-invariant capability in pattern recognition; (2) An effective approach to optimization of the structure of the back-propagation neural network; (3) Candidate searching and pre-processing techniques to facilitate the automatic analysis of the electronic drawings. An analysis and the system performance reveal that when the shift of an image pattern is not large, and the rotation is only by nx90°, (n = 1, 2, and 3), the modified Kohonen neural network is superior to the conventional Kohonen neural network in terms of shift-invariant and limited rotation-invariant capabilities. As a result, the dimensionality of the Kohonen layer can be reduced significantly compared with the conventional ones for the same performance. Moreover, the size of the subsequent neural network, say, back-propagation feed-forward neural network, can be decreased dramatically. There are no known rules for specifying the number of nodes in the hidden layers of a feed-forward neural network. Increasing the size of the hidden layer usually improves the recognition accuracy, while decreasing the size generally improves generalization capability. We determine the optimal size by simulation to attain a balance between the accuracy and generalization. This optimized back-propagation neural network outperforms the conventional ones designed by experience in general. In order to further reduce the computation complexity and save the calculation time spent in neural networks, pre-processing techniques have been developed to remove long circuit lines in the electronic drawings. This made the candidate searching more effective

A novel off-line character recognition: an MLP approach

The purpose of this thesis work is to explore the possibility of efficient man-machine communication through printed documents. An attempt has been made to show the pattern recognition techniques i.e., KNN classifier helpful in recognition of machine printed characters and artificial neural networks may be used to represent and recognize printed English characters of any font and size. In our current work the machine printed document images are scanned by a front end video scanner and are applied to noise removal techniques using smoothing and sharpening filters. The noiseless images are digitized into a bi-level image using Ni-Black proposed binarization technique and proposed adaptive thresholding algorithm using Laplacian sign. Our work is split into three parts. The first part deals with segmentation and thinning. The output of this phase is thinned character image. The second part involves features are extracted from thinned image. The third part deals with KNN classifiers and training of the multilayer perceptron and recognizing characters after the system is trained. Automatic character recognition system promises to hold great future in Automatic office information processing system by integrating with multimedia, like Graphics, image and voice, into a single work station

An expert system for the differential diagnosis of erythemato-squamous diseases

Cataloged from PDF version of article.This paper presents an expert system for differential diagnosis of erythemato-squamous diseases incorporating decisions made by three classification algorithms: nearest neighbor classifier, naive Bayesian classifier and voting feature intervals-5. This tool enables doctors to differentiate six types of erythemato-squamous diseases using clinical and histopathological parameters obtained from a patient. The program also gives explanations for the classifications of each classifier. The patient records are also maintained in a database for further references. (C) 2000 Elsevier Science Ltd. All rights reserved