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 Polarity Determination Based on the Total Length and Centromere Location Using Machine Learning Algorithms

In this work we determine chromosome polarity based on three machine learning methods: multilayer perceptron (MLP) neural networks, k-nearest neighbor (k-nn) method and support vector machines (SVM). In all three machine learning methods only two chromosome features, total length of the chromosome and the cetromere location, were used to determine the chromosome polarity.  Classification results obtained are 95.94%, 95.255%, and 95.88% for MLP neural networks, k-nn method and SVM respectively

Denver Groups Classification of Human Chromosomes Using Fuzzy C-Means Clustering

Unbanded human chromosome can be classified into seven Denver Groups (A-G) based their lengths and the ratio of the length of the shorter arm to the whole length of the chromosome, which is called the centromere index (CI). In this article, the fuzzy c-means method will be used to perform the Denver Group classification of a given set of human chromosomes. The objective in clustering is to partition a given human chromosome set into homogeneous clusters; by homogeneous we mean that all points in the same cluster share similar attributes and they do not share similar attributes with points in other clusters. However, the separation of clusters and the meaning of similarity are fuzzy notions and can be described as such. It is found that the clusters iterations converge, highly depend on the initial partition matrix

Integrated Development and Parallelization of Automated Dicentric Chromosome Identification Software to Expedite Biodosimetry Analysis

Manual cytogenetic biodosimetry lacks the ability to handle mass casualty events. We present an automated dicentric chromosome identification (ADCI) software utilizing parallel computing technology. A parallelization strategy combining data and task parallelism, as well as optimization of I/O operations, has been designed, implemented, and incorporated in ADCI. Experiments on an eight-core desktop show that our algorithm can expedite the process of ADCI by at least four folds. Experiments on Symmetric Computing, SHARCNET, Blue Gene/Q multi-processor computers demonstrate the capability of parallelized ADCI to process thousands of samples for cytogenetic biodosimetry in a few hours. This increase in speed underscores the effectiveness of parallelization in accelerating ADCI. Our software will be an important tool to handle the magnitude of mass casualty ionizing radiation events by expediting accurate detection of dicentric chromosomes

Denver Groups Classification of Human Chromosomes Using CANN Teams Supplemented by a Nearest Neighbor Technique CANNT-S

Human chromosomes can be classified into seven Denver groups (A – G) based on the position of the centromere. This classification is an important stage of human chromosome classification, as its output influence the second stage of classification, the correct classification of 24 classes of human chromosomes based on the banding pattern.In this article, the novel artificial neural network committee machines technique (CANNT) developed earlier is supplemented by a nearest neighbor technique, CANNT-S, and the correct classification rate in Denver Groups Classification of Human Chromosomes rose from 96% to a level of 98%

Deep Active Learning for Automatic Mitotic Cell Detection on HEp-2 Specimen Medical Images

Identifying Human Epithelial Type 2 (HEp-2) mitotic cells is a crucial procedure in anti-nuclear antibodies (ANAs) testing, which is the standard protocol for detecting connective tissue diseases (CTD). Due to the low throughput and labor-subjectivity of the ANAs' manual screening test, there is a need to develop a reliable HEp-2 computer-aided diagnosis (CAD) system. The automatic detection of mitotic cells from the microscopic HEp-2 specimen images is an essential step to support the diagnosis process and enhance the throughput of this test. This work proposes a deep active learning (DAL) approach to overcoming the cell labeling challenge. Moreover, deep learning detectors are tailored to automatically identify the mitotic cells directly in the entire microscopic HEp-2 specimen images, avoiding the segmentation step. The proposed framework is validated using the I3A Task-2 dataset over 5-fold cross-validation trials. Using the YOLO predictor, promising mitotic cell prediction results are achieved with an average of 90.011% recall, 88.307% precision, and 81.531% mAP. Whereas, average scores of 86.986% recall, 85.282% precision, and 78.506% mAP are obtained using the Faster R-CNN predictor. Employing the DAL method over four labeling rounds effectively enhances the accuracy of the data annotation, and hence, improves the prediction performance. The proposed framework could be practically applicable to support medical personnel in making rapid and accurate decisions about the mitotic cells' existence