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Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification ADCI and Dose Estimation

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Correlation-based feature selection and classification via regression of segmented chromosomes using geometric

Tanvi Arora¹, Renu Dhir²

¹Department of Computer Science and Engineering, Dr. B.R Ambedkar National Institute of Technology, Jalandhar, Punjab, India. tanviverma@rediffmail.com.

Medical & Biological Engineering & Computing

|July 31, 2016

Summary

This study introduces a hybrid method to classify and preprocess chromosomes, improving genetic defect detection. The technique accurately categorizes chromosomes, aiding in the correction of their orientation for better analysis.

Keywords:

Chromosomes Classification Feature extraction Feature selection Genetic defects

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Area of Science:

Genetics
Biomedical Imaging
Computational Biology

Background:

Chromosomes carry genetic information but their varied orientations and defects complicate analysis.
Accurate chromosome classification and preprocessing are crucial for identifying genetic abnormalities.

Purpose of the Study:

To develop an efficient hybrid technique for classifying segmented chromosomes into five categories: straight, overlapping, bent, touching, or noise.
To enable subsequent preprocessing steps for correcting chromosome orientation.

Main Methods:

A hybrid classification technique combining correlation-based feature selection and a classification via regression approach was developed.
The method was evaluated on 1592 segmented chromosomes from the Advance Digital Imaging Research dataset.

Main Results:

The proposed hybrid classifier achieved an overall accuracy of 94.78% for the five-class problem.
Performance was benchmarked against Bayes Net, Naïve Bayes, Radial Bias Feed Forward Network, and k-nearest-neighbor classifiers.

Conclusions:

The developed hybrid classification technique effectively categorizes chromosomes, facilitating improved preprocessing for genetic defect analysis.
This approach offers a robust solution for handling complex chromosome imaging data.