Human chromosome classification using competitive support vector machine teams

Classification of chromosome is a challenging task and requires very precise autonomous classifier. This paper proposes to employ competing support vector machines (SVMs) placed in a grid. Each agent in cells of the grid is responsible to distinguish two classes. Overall output is determined by simple majority voting of SVMs. Relying same principle as the work by Palalic and Can [17], we compared the results obtained where the algorithms delivers better accuracy

Application of Ensemble Machines of Neural Networks to Chromosome Classification

This work presents approaches to the automatic classification of metaphase chromosomes using several perceptron neural network techniques on neural networks function as committee machines. To represent the banding patterns, only chromosome gray level profiles are exploited. The other inputs to the ensemble machines of the network are the chromosome size and centromeric index. It is shown that, without much effort, the classification performances of the four networks are found to be similar to the ones of a well-developed parametric classifier. Four parallel networks trained for the four different aspects of the data set, the gray level profile vector, Fourier coefficients of gray level profiles, 3D data of chromosome length – centromeric index – total gray levels, and 4D data obtained by the addition of average gray levels. Then the classification results of differently trained neural networks (i.e., experts), are combined by the use of a genuine ensemble-averaging to produce an overall output by the combiner. We discuss the flexibility of the classifier developed, its potential for development, and how it may be improved to suit the current needs in karyotyping

ENERGY MINIMIZATION FOR IDENTIFICATION OF BANDING PATTERN IN CHROMOSOMES USING OPTIMIZED GRAPH CUT ALGORITHM

Intensity inhomogeneity is a significant cause in reducing the accuracy of image segmentation. This paper proposes an algorithm for identification of bands in chromosomes using graph cut segmentation that uses global and local image statistics. The global energy is an estimate of the intensity distribution of the image and background and local energy provide the information related with neighboring pixels that eliminates the impact of intensity inhomogeneities. Efficient energy minimization helps in better pixel labeling and this is done by optimized Graph cut process. The shape prior of the band at each location of the image is considered with shape probability energy functions. The experimental results demonstrate that the approach is robust and efficient in detecting the band information in chromosomes to a larger extent

Digital image analysis of mitotic chromosomes

Změny v počtu a ve struktuře chromozomů jsou příčinou řady vážných onemocnění. K odhalení chromozomálních změn slouží cytogenetická vyšetření, která nejčastěji vedou k sestavení karyotypu. Pro účely cytogenetických analýz se chromozomy vizualizují pomocí vhodných metod a nejčastěji se následně sestavují do karyotypu. Protože ruční stanovení karyotypu je časově i finančně náročné, vyvíjí se přístupy k automatickému karyotypování pomocí počítačového softwaru. Automatické karyotypovací systémy klasifikují chromozomy do tříd na základě identifikačních znaků, specifických pro každý chromozom. Automatickou klasifikaci však nejvíce limituje přítomnost překrývající se a silně ohnutých chromozomů, přítomných v téměř každé mitóze. Přesnost a spolehlivost karyotypovacích systémů stále závisí na zásahu uživatele. Cílem vývoje nových přístupů k automatickému karyotypování je tedy zejména překonání výše zmíněných problémů a dále vývoj takových klasifikačních metod, které umožňí klasifikaci chromozomů do párů bez lidské kontroly.Changes in chromosome number and structure may cause serious diseases. Cytogenetic tests leadin to set of karyotype are done for detecting these abnormalities. Chromosomes are visualised with proper methods and karyotype is made up most often. Manual karyotyping is time-consuming and expensive task. Because of this, researchers have been developing automated karyotyping systems. Karyotyping systems classify chromosomes into classes based on their characteristic features. Overlapping and bent chromosomes are limitations for automatic classification since they ocur at almost every mitosis. Accuracy and reliability of karyotyping systems still depend on the human intervention. Overcoming of these problems and development of fully automated system is the aim of modern approaches.

Chromosome classification and speech recognition using inferred Markov networks with empirical landmarks.

by Law Hon Man.Thesis (M.Phil.)--Chinese University of Hong Kong, 1993.Includes bibliographical references (leaves 67-70).Chapter 1 --- Introduction --- p.1Chapter 2 --- Automated Chromosome Classification --- p.4Chapter 2.1 --- Procedures in Chromosome Classification --- p.6Chapter 2.2 --- Sample Preparation --- p.7Chapter 2.3 --- Low Level Processing and Measurement --- p.9Chapter 2.4 --- Feature Extraction --- p.11Chapter 2.5 --- Classification --- p.15Chapter 3 --- Inference of Markov Networks by Dynamic Programming --- p.17Chapter 3.1 --- Markov Networks --- p.18Chapter 3.2 --- String-to-String Correction --- p.19Chapter 3.3 --- String-to-Network Alignment --- p.21Chapter 3.4 --- Forced Landmarks in String-to-Network Alignment --- p.31Chapter 4 --- Landmark Finding in Markov Networks --- p.34Chapter 4.1 --- Landmark Finding without a priori Knowledge --- p.34Chapter 4.2 --- Chromosome Profile Processing --- p.37Chapter 4.3 --- Analysis of Chromosome Networks --- p.39Chapter 4.4 --- Classification Results --- p.45Chapter 5 --- Speech Recognition using Inferred Markov Networks --- p.48Chapter 5.1 --- Linear Predictive Analysis --- p.48Chapter 5.2 --- TIMIT Speech Database --- p.50Chapter 5.3 --- Feature Extraction --- p.51Chapter 5.4 --- Empirical Landmarks in Speech Networks --- p.52Chapter 5.5 --- Classification Results --- p.55Chapter 6 --- Conclusion --- p.57Chapter 6.1 --- Suggested Improvements --- p.57Chapter 6.2 --- Concluding remarks --- p.61Appendix A --- p.63Reference --- p.6

Reconhecimento de padrões usando indexação recursiva

Tese (Doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico

Computational Strategies for Object Recognition

This article reviews the available methods forautomated identification of objects in digital images. The techniques are classified into groups according to the nature of the computational strategy used. Four classes are proposed: (1) the s~mplest strategies, which work on data appropriate for feature vector classification, (2) methods that match models to symbolic data structures for situations involving reliable data and complex models, (3) approaches that fit models to the photometry and are appropriate for noisy data and simple models, and (4) combinations of these strategies, which must be adopted in complex situations Representative examples of various methods are summarized, and the classes of strategies are evaluated with respect to their appropriateness for particular applications

Human chromosome classification based on local band descriptors

In this paper several new techniques for automated chromosome analysis are described: one for piecewise-linear chromosome stretching and projection, two for accurately localizing the centromere and one for two-dimensional local band pattern description. A classification procedure is described that is based upon local band descriptors. Classification results obtained with this method are compared with results obtained with the global band description method (WDD functions). Data sets from two different laboratories are used to investigate the influence of the preparation. Results show the suitability of the local description method in its ability to visualize the image processing technique at the level of the chromosome image