Automatic Pattern Extraction and Classification for Chromosome Images

Ming, Delie; Tian, Jinwen

doi:10.1007/s10762-010-9640-1

Cited by 23 publications

(10 citation statements)

References 12 publications

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“…Robust performance started from introducing medial axis transformation (MAT) into chromosome classification [19]. As follows, many MAT-based studies have been conducted [20], [21]. Also, multi-layer perceptron (MLP) was applied to make the predictions [19].…”

Section: B Chromosome Classificationmentioning

confidence: 99%

Statistical Karyotype Analysis Using CNN and Geometric Optimization

Xie

Liu

et al. 2019

IEEE Access

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Karyotype analysis is one of the main techniques of cytogenetics through medical image processing, which has an important role in modern medical treatment and diagnosis. The process of human karyotype analysis contains two key components: Firstly, chromosomes are segmented from metaphase chromosome digital images taken under a microscope. Chromosomes then are analyzed, compared, ordered and classified one by one carefully. Under this procedure, the operation on segmentation and classification is cumbersomely time-consuming, where traditional geometric or statistical methods only have limited effect due to low accuracy. Thus, in most conditions, human effort is still heavily required to monitor the workflow and correct the errors. In this paper, we present an integrated workflow to segment out and classify chromosomes automatically using a combination of multiple input convolutional neural networks (CNN) and geometric optimization, called mCNN_GO. We investigate Mask R-CNN to segment out chromosomes from metaphase chromosome images and train the mCNN_GO to classify the sub-images. To improve the performance of the segmentation network, we adapt a new feature-based approach to synthesize images on the labeled data. Furthermore, we develop a geometric algorithm to straighten the chromosomes before classification to ensure the consistency of the data. Experimental results demonstrate that our approach significantly outperforms the state-of-the-art methods on automatic karyotype analysis. INDEX TERMS Biomedical image processing, image processing, machine learning.

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Section: B Chromosome Classificationmentioning

confidence: 99%

Statistical Karyotype Analysis Using CNN and Geometric Optimization

Xie

Liu

et al. 2019

IEEE Access

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“…However, the instances within the set are not iid, as the parts are spatially linked within the object, and also follow physical motion laws. Other potential applications include karyotyping (labelling the chromosomes in a cell) [9,15] and identifying footballers on the pitch in a live-play video [3].…”

Section: The Multiple-instance Problem This Problem Arises In Complementioning

confidence: 99%

Who Is Missing? A New Pattern Recognition Puzzle

Kuncheva

Jackson

2014

Lecture Notes in Computer Science

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Consider a multi-class classification problem. Given is a set of objects, for which it is known that there is at most one object from each class. The problem is to identify the missing classes. We propose to apply the Hungarian assignment algorithm to the logarithms of the estimated posterior probabilities for the given objects. Each object is thereby assigned to a class. The unassigned classes are returned as the solution. Its quality is measured by a consistency index between the solution and the set of known missing classes. The Hungarian algorithm was found to be better than the rival greedy algorithm on two data sets: the UCI letter data set and a bespoke image data set for recognising scenes with LEGO parts. Both algorithms outperformed a classifier which treats the objects as iid.

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“…Then, intensity-based features and centromeric indexes were fed into an MLP network for classification. Ming et al [10] computed medial axes using a middle point algorithm. They extracted banding patterns by average intensity, gradient, and shape profiles and adopted an MLP classifier.…”

Section: Introductionmentioning

confidence: 99%

Varifocal-Net: A Chromosome Classification Approach Using Deep Convolutional Networks

Qin

Wen

Zheng

et al. 2019

IEEE Trans. Med. Imaging

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Chromosome classification is critical for karyotyping in abnormality diagnosis. To expedite the diagnosis, we present a novel method named Varifocal-Net for simultaneous classification of chromosomes type and polarity using deep convolutional networks. The approach consists of one global-scale network (G-Net) and one localscale network (L-Net). It follows three stages. The first stage is to learn both global and local features. We extract global features and detect finer local regions via the G-Net. By proposing a varifocal mechanism, we zoom into local parts and extract local features via the L-Net. Residual learning and multi-task learning strategies are utilized to promote high-level feature extraction. The detection of discriminative local parts is fulfilled by a localization subnet of the G-Net, whose training process involves both supervised and weakly-supervised learning. The second stage is to build two multi-layer perceptron classifiers that exploit features of both two scales to boost classification performance. The third stage is to introduce a dispatch strategy of assigning each chromosome to a type within each patient case, by utilizing the domain knowledge of karyotyping. Evaluation results from 1909 karyotyping cases showed that the proposed Varifocal-Net achieved the highest accuracy per patient case (%) of 99.2 for both type and polarity tasks. It outperformed state-of-the-art methods, demonstrating the effectiveness of our varifocal mechanism, multi-scale feature ensemble, and dispatch strategy. The proposed method has been applied to assist practical karyotype diagnosis.

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Automatic Pattern Extraction and Classification for Chromosome Images

Cited by 23 publications

References 12 publications

Statistical Karyotype Analysis Using CNN and Geometric Optimization

Statistical Karyotype Analysis Using CNN and Geometric Optimization

Who Is Missing? A New Pattern Recognition Puzzle

Varifocal-Net: A Chromosome Classification Approach Using Deep Convolutional Networks

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