Feature Selection for the Automated Detection of Metaphase Chromosomes: Performance Comparison Using a Receiver Operating Characteristic Method

Abstract

Background. The purpose of this study is to identify a set of features for optimizing the performance of metaphase chromosome detection under high throughput scanning microscopy. In the development of computer-aided detection (CAD) scheme, feature selection is critically important, as it directly determines the accuracy of the scheme. Although many features have been examined previously, selecting optimal features is often application oriented. Methods. In this experiment, 200 bone marrow cells were first acquired by a high throughput scanning microscope. Then 9 different features were applied individually to group captured images into the clinically analyzable and unanalyzable classes. The performance of these different methods was assessed by a receiving operating characteristic (ROC) method. Results. The results show that using the number of labeled regions on each acquired image is suitable for the first on-line CAD scheme. For the second off-line CAD scheme, it would be suggested to combine four feature extraction methods including the number of labeled regions, average regions area, average region pixel value, and the standard deviation of either region distance or circularity. Conclusion. This study demonstrates an effective method of feature selection and comparison to facilitate the optimization of the CAD schemes for high throughput scanning microscope in the future.

A new approach to develop computer-aided diagnosis scheme of breast mass classification using deep learning technology

“…From the previous CAD studies reported in the literature, we found that although a large number of image features [29, 30] could be computed, many of them are redundant and only a small set of features was finally selected to build a classifier [31]. The small number of features can typically improve robustness of the classifier with a limited training and testing dataset.…”

Machine Learning Classifiers Evaluation for Automatic Karyogram Generation from G-Banded Metaphase Images

“…Manual karyogram construction is a complex task demanding time and expertise. Nowadays, several efforts have been done to create automatic systems for dealing with computer-based karyotyping [2,[6][7][8][9][10][11][12][13]. Several studies implement techniques from machine learning such as Support Vector Machines [7,8,[14][15][16], Nearest Neighbor Algorithms [17,18], Wavelets [19], Bayesian techniques [20,21] and mainly, Artificial Neural Networks [19,[22][23][24][25].…”

“…It is important to define the optimal chromosome features to obtain a good accuracy. In the literature, shape description, length, centromere position, and the banding pattern have been used as chromosomes descriptors [12,18,30,31,[34][35][36]. In Reference [8,33], the authors annotate that these characteristics are useful to determine if the chromosome was correctly segmented, but they can not be used as the only descriptors of the chromosome.…”

Radiation Dose Estimation by Completely Automated Interpretation of the Dicentric Chromosome Assay

“…We have implemented a computational approach which corrects FPs at the chromosome-level by analyzing individual positive detections and reduces FPs at the image-level by selecting optimal images in samples for analysis (4) . Previous approaches for metaphase cell selection have applied fixed thresholds to features extracted from objects present in these images for binary classification as either suitable or unanalyzable (5,6) . Rule-based criteria for metaphase assessment have also extracted morphological features of objects enclosed by rectangles to partition chromosomes (7) or images (8) into small numbers of generally analyzable classes, with the exception of poor quality samples with high levels of debris or low mitotic indices (8) .…”

“…Features used for image selection include object count and shape (used in ref. 5,6 ), contour finite difference, a novel measure of texture coarseness (used in ref. 10 ), as well as likely centromeres (2) .…”