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

An automated system for chromosome analysis. Volume 1: Goals, system design, and performance

The design, construction, and testing of a complete system to produce karyotypes and chromosome measurement data from human blood samples, and a basis for statistical analysis of quantitative chromosome measurement data is described. The prototype was assembled, tested, and evaluated on clinical material and thoroughly documented

An Automated System for Chromosome Analysis

The design, construction, and testing of a complete system to produce karyotypes and chromosome measurement data from human blood samples, and to provide a basis for statistical analysis of quantitative chromosome measurement data are described

Human Metaphase Chromosome Analysis using Image Processing

Development of an effective human metaphase chromosome analysis algorithm can optimize expert time usage by increasing the efficiency of many clinical diagnosis processes. Although many methods exist in the literature, they are only applicable for limited morphological variations and are specific to the staining method used during cell preparation. They are also highly influenced by irregular chromosome boundaries as well as the presence of artifacts such as premature sister chromatid separation. Therefore an algorithm is proposed in this research which can operate with any morphological variation of the chromosome across images from multiple staining methods. The proposed algorithm is capable of calculating the segmentation outline, the centerline (which gives the chromosome length), partitioning of the telomere regions and the centromere location of a given chromosome. The algorithm also detects and corrects for the sister chromatid separation artifact in metaphase cell images. A metric termed the Candidate Based Centromere Confidence (CBCC) is proposed to accompany each centromere detection result of the proposed method, giving an indication of the confidence the algorithm has on a given localization. The proposed method was first tested for the ability of calculating an accurate width profile against a centerline based method [1] using 226 chromosomes. A statistical analysis of the centromere detection error values proved that the proposed method can accurately locate centromere locations with statistical significance. Furthermore, the proposed method performed more consistently across different staining methods in comparison to the centerline based approach. When tested with a larger data set of 1400 chromosomes collected from a set of DAPI (4\u27,6-diamidino-2-phenylindole) and Giemsa stained cell images, the proposed candidate based centromere detection algorithm was able to accurately localize 1220 centromere locations yielding a detection accuracy of 87%

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

Data fusion by using machine learning and computational intelligence techniques for medical image analysis and classification

Data fusion is the process of integrating information from multiple sources to produce specific, comprehensive, unified data about an entity. Data fusion is categorized as low level, feature level and decision level. This research is focused on both investigating and developing feature- and decision-level data fusion for automated image analysis and classification. The common procedure for solving these problems can be described as: 1) process image for region of interest\u27 detection, 2) extract features from the region of interest and 3) create learning model based on the feature data. Image processing techniques were performed using edge detection, a histogram threshold and a color drop algorithm to determine the region of interest. The extracted features were low-level features, including textual, color and symmetrical features. For image analysis and classification, feature- and decision-level data fusion techniques are investigated for model learning using and integrating computational intelligence and machine learning techniques. These techniques include artificial neural networks, evolutionary algorithms, particle swarm optimization, decision tree, clustering algorithms, fuzzy logic inference, and voting algorithms. This work presents both the investigation and development of data fusion techniques for the application areas of dermoscopy skin lesion discrimination, content-based image retrieval, and graphic image type classification --Abstract, page v

Method for solving nonlinearity in recognising tropical wood species

Classifying tropical wood species pose a considerable economic challenge and failure to classify the wood species accurately can have significant effects on timber industries. Hence, an automatic tropical wood species recognition system was developed at Centre for Artificial Intelligence and Robotics (CAIRO), Universiti Teknologi Malaysia. The system classifies wood species based on texture analysis whereby wood surface images are captured and wood features are extracted from these images which will be used for classification. Previous research on tropical wood species recognition systems considered methods for wood species classification based on linear features. Since wood species are known to exhibit nonlinear features, a Kernel-Genetic Algorithm (Kernel-GA) is proposed in this thesis to perform nonlinear feature selection. This method combines the Kernel Discriminant Analysis (KDA) technique with Genetic Algorithm (GA) to generate nonlinear wood features and also reduce dimension of the wood database. The proposed system achieved classification accuracy of 98.69%, showing marked improvement to the work done previously. Besides, a fuzzy logic-based pre-classifier is also proposed in this thesis to mimic human interpretation on wood pores which have been proven to aid the data acquisition bottleneck and serve as a clustering mechanism for large database simplifying the classification. The fuzzy logic-based pre-classifier managed to reduce the processing time for training and testing by more than 75% and 26% respectively. Finally, the fuzzy pre-classifier is combined with the Kernal-GA algorithm to improve the performance of the tropical wood species recognition system. The experimental results show that the combination of fuzzy preclassifier and nonlinear feature selection improves the performance of the tropical wood species recognition system in terms of memory space, processing time and classification accuracy