Development and evaluation of automated systems for detection and classification of banded chromosomes: current status and future perspectives

Abstract

Automated detection and classification of banded chromosomes may help clinicians diagnose cancers and other genetic disorders at an early stage more efficiently and accurately. However, developing such an automated system (including both a high-speed microscopic image scanning device and related computer-assisted schemes) is quite a challenging and difficult task. Since the 1980s, great research efforts have been made to develop fast and more reliable methods to assist clinical technicians in performing this important and time-consuming task. A number of computer-assisted methods including classical statistical methods, artificial neural networks and knowledge-based fuzzy logic systems, have been applied and tested. Based on the initial test using limited datasets, encouraging results in algorithm and system development have been demonstrated. Despite the significant research effort and progress made over the last two decades, computer-assisted chromosome detection and classification systems have not been routinely accepted and used in clinical laboratories. Further research and development is needed.

Preprocessing G-banded metaphase: towards the design of automated karyotyping

“…In [23] a neural network approach is proposed for the automated identification of single chromosomes and blob of chromosomes. Significance of all these preprocessing steps in the design of automated karyotyping system is discussed in [24][25][26]…”

Research of Segmentation Algorithms for Overlapping Chromosomes

“…The typical case error rate was approximately 20% [3]. If the chromosomes were touching, overlapping or deformed, the classification error rate was substantially increased [4].…”

Enhancement methods for chromosome images

“…A computer-assisted system usually includes four processing steps: (1) image enhancement, (2) chromosome segmentation (detection) and alignment, (3) feature computation and selection and (4) chromosome classification. Fig.1 shows the block diagram of such a typical system [1]. Chromosome images are acquired by microscopic imaging of metaphase or prophase cells on specimen slides.…”