Development of a computer-aided chromosome analysis system to assist cancer diagnosis

Metaphase-finding and karyotyping are standard and fundamental procedures, which are routinely performed in genetic laboratories for clinicians to detect cancers and other genetic diseases. However, visual search, manual identification, and classification of analyzable metaphase chromosomes using optical microscopes are tedious and time-consuming tasks. Hence, an integrated computer-aided detection (CAD) system, which includes both an automated metaphase finding module and a karyotyping chromosome module, may significantly expedite and improve the diagnostic efficiency and performance in a hectic clinical practice. Despite of previous efforts, several challenges have limited the development of automated metaphase-finding and karyotyping schemes: (1) most current automated metaphase finding schemes use low-resolution images to search for the location of potentially analyzable cells and they still needs human intervention; (2) automatic metaphase finding results obtained in low magnification requires technicians to switch to high magnification lens to double check whether it is useful for later karyotyping; (3) most automated karyotyping schemes use a single classifier to classify 24 different types of chromosomes; (4) how to select effective features for classification; and (5) how to find the optimal classifier and thus reduce the complexity of karyotyping. In order to solve these problems, this thesis reports the results of the research in developing a new chromosome analysis system for cancer diagnosis. The integrated chromosome analysis system in this study includes three basic modules. The first module detects whether a microscopic digital image depicts a metaphase chromosome cell in high magnification. A computerized scheme has been developed and tested. It can automatically identify chromosomes in metaphase stage and classify them into analyzable and un-analyzable groups in high magnification. Through computing a set of features for each individual chromosome as well as for each identified metaphase cell, two machine learning classifiers including a decision tree (DT) and an artificial neural network (ANN) are optimized and tested to classify between analyzable and un-analyzable cells. These two classifiers utilize features of individual chromosomes and metaphase cells. The on-line metaphase finding processes less than 350 milliseconds for an eight-bit chromosome image with size 4096*2048. Second, a multi-stage rule-based scheme has been developed to automatically detect centomeres and determine polarities for both abnormal and normal metaphase chromosomes. Automatic centromere identification and polarity assignment are two key factors in the automatic karyotyping of human chromosomes. The scheme implements a modified thinning algorithm to identify the medial axis of a chromosome and extracts four related feature profiles, which include pixel distribution, shape, density, and idealized banding profile. According to a set of pre-optimized classification rules, the scheme adaptively identifies the centromeres, assigns corresponding polarities, and extracts related features of chromosomes. The experimental results demonstrate that the scheme developed in this study can be successfully applied to diverse chromosomes, which include those severely bent and abnormal chromosomes extracted from cancer cells. Third, though analyzing four feature profiles and calculating specific features by weighted density distribution functions, a vector of 31 features is obtained and used to classify chromosomes by a two-layer ANN-based classifier. This two-layer classifier with eight ANNs is optimized by a genetic algorithm. In the first layer, a testing chromosome is classified into one of the seven groups by the ANN. Another ANN is then automatically selected from the seven ANNs in the second layer (one for each group) to further classify this chromosome into one of 24 types. The scheme was evaluated using a "training-testing-validation" method. To assess the performance and robustness of the chromosome analysis system introduced in this thesis, 200 digital microscopic chromosome images are used in this chapter. The automatic metaphase-finding module detects analyzable metaphase cells using a feature-based ANN. The ANN-generated outputs are analyzed by an ROC method and an area under the ROC curve is 0.966. In the extracting feature module, the overall accuracy is 89% for centromere identification and 90.7% for polarity assignment. In karyotyping module, a two-layer DT-based classifier with eight ANNs established in its connection nodes is optimized by a GA. Chromosomes are classified into seven groups by the ANN in the first layer. The chromosomes in these groups are then separately classified by seven ANNs into 24 types in the second layer. The classification accuracy is 94.5% in the first layer. Six ANNs achieved the accuracy above 95% and only one had lessened performance (80.6%) in the second layer. The results demonstrate that the developed automated scheme can achieve high and robust performance in the identification and classification of metaphase chromosomes. Besides assisting cancer detection, this analysis system can help doctors evaluate the prognosis of specific cancer. By analyzing the numerical change of chromosomes and computing the DNA index, the correlated results obtained in this thesis can help doctors to analyze the prognosis of childhood acute lymphoblastic leukemia.