Data-Driven Homologue Matching for Chromosome Identification

Karyotyping involves the visualization and classification of chromosomes into standard classes. In normal human metaphase spreads, chromosomes occur in homologous pairs for the autosomal classes 1-22, and X chromosome for females. Many existing approaches for performing automated human chromosome image analysis presuppose cell normalcy, containing 46 chromosomes within a metaphase spread with two chromosomes per class. This is an acceptable assumption for routine automated chromosome image analysis. However, many genetic abnormalities are directly linked to structural or numerical aberrations of chromosomes within the metaphase spread. Thus, two chromosomes per class cannot be assumed for anomaly analysis. This paper presents the development of image analysis techniques which are extendible to detecting numerical aberrations evolving from structural abnormalities. Specifically, an approach to identifying normal chromosomes from selected class(es) within a metaphase spread is presented. Chromosome assignment to a specific class is initially based on neural networks, followed by banding pattern and centromeric index criteria checking, and concluding with homologue matching. Experimental results are presented comparing neural networks as the sole classifier to the authors\u27 homologue matcher for identifying class 17 within normal and abnormal metaphase spreads

Artificial neural networks : A comparative study of implementations for human chromosome classification

Artificial neural networks are a popular field of artificial intelligence and have commonly been applied to solve many prediction, classification and diagnostic tasks. One such task is the analysis of human chromosomes. This thesis investigates the use of artificial neural networks (ANNs) as automated chromosome classifiers. The investigation involves the thorough analysis of seven different implementation techniques. These include three techniques using artificial neural networks, two techniques using ANN s supported by another method and two techniques not using ANNs. These seven implementations are evaluated according to the classification accuracy achieved and according to their support of important system measures, such as robustness and validity. The results collected show that ANNs perform relatively well in terms of classification accuracy, though other implementations achieved higher results. However, ANNs provide excellent support of essential system measures. This leads to a well-rounded implementation, consisting of a good balance between accuracy and system features, and thus an effective technique for automated human chromosome classification

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