Image enhancement for increased dot-counting efficiency in FISH

The most commonly used molecular cytogenetic technique is fluorescence in situ hybridization (FISH). It has been widely applied in many areas of diagnosis and research, including pre-natal and post-natal screening of chromosomal aberrations, pre-implantation genetic diagnosis, cancer cytogenetics, gene mapping, molecular pathology and developmental molecular biology. The analysis of FISH images consists of detecting fluorescent dots, after which the number of dots per cell can be counted or their relative positions can be measured. A major impediment in the analysis of FISH specimens is signal (dot) quality, which is influenced by the hybridization efficiency and/or the sensitivity of the camera that records the images.

In this paper, we present an approach to improve the efficiency of detecting fluorescent signals in FISH images by recovering the radiance map of the camera. This allows us to generate a high-dynamic-range image wherein an extended range of the sample radiance captured by the camera can be visualized at distinct intensity values. The resulting higher-order numeric complexity of the transformed image is adjusted (or simplified) by examining the intensity distribution in each of the three colour channels (red, green and blue), and remapping the intensity values to generate a high-contrast image with a lower-order (compressed) dynamic range. The remapping is based on a criterion that optimizes the detection of the hybridized signals, allowing attenuation of saturated intensity values while amplifying low-intensity signals.

A simple dot-counting algorithm is used to automatically process 2000 FISH images. The images are taken for lymphocytes from cultured blood specimens for cytogenetic testing. Images are manually analyzed by an expert to obtain ground truth for dot counts. A quantitative analysis is performed by comparing results of automated dot detection on images before and after enhancement with the developed algorithms. In addition, common errors in dot counting due to split dots, dust, poor segmentation and overlapping signals are analyzed and the robustness of the developed approach against these errors evaluated. It is observed that dot-detection efficiency is increased by an average of 9% across all colour channels while reducing errors in missed and false dot counts.

Our proposed method and results demonstrate that dot-counting specificity and sensitivity can be improved by pre-processing and enhancing the image using the radiance curve of the camera and generating a high-contrast, remapped high-dynamic-range image prior to using any algorithm for dot counting.