A Robust Approach to Locate HER2 and CEN17 Signals in Varied FISH Images Fluorescence in situ hybridization (FISH) is a wellrecognized technique for evaluating the human epidermal growth factor receptor 2 (HER2) gene status in breast cancer, where HER2 genes are seen as the red stained spots, and the chromosome centromere (CEN17) on which HER2 is located are visible as the green stained spots under a fluorescent microscope. The traditional manual analysis of FISH images is low efficient and has great subjective differences. Many researches focus on automatically calculating the average ratio of redgreen spots in a cell and have achieved good performance. However, there are still a need to improve the segmentation accuracy of cells and HER2CEN17 signals in FISH images with poor quality. In this paper, we propose a robust approach to locate HER2 and CEN17 signals based on the image standardization of varied FISH images. First we propose to use offset operation to reduce the fluorescence scattering, and to use exposure ratio map estimation and brightness transformation to enhance the weak fluorescence. Then we detect redgreen spots by sequentially using tophat filtering, thresholding and postprocessing operations. The experimental results have shown that our proposed method can work well for the clinical FISH images with different quality.

SpotDSQ: A 2DGel Image Analysis Tool for Protein Spot Detection, Segmentation and Quantification The field of proteomics offers powerful methods for studying and analyzing protein expression levels in cells. The TwoDimensional Gel Electrophoresis technique is a wellestablished proteomics technique focusing on protein separation and identification that provides digital images containing thousands of protein spots. 2Dgel images are then segmented into spots and background in order to quantify the expression levels of proteins located on a single gel or of differentially expressed proteins between samples from a series of 2Dgels. Spot detection and segmentation are complex and arduous tasks due to the inherent characteristics of 2Dgel images. Several software packages and experimental methods are available for 2Dgel image analysis, achieving different levels of success, each one having its respective advantages and drawbacks. A common characteristic is their dependency on user intervention in order to achieve optimal results, a process that leads to subjective and usually nonreproducible results. In this work, the authors present SpotDSQ, a software tool for 2Dgel electrophoresis image analysis that incorporates novel algorithms for accurate spot detection, segmentation and quantification. SpotDSQ provides high quality automatic protein spot detection, segmentation and quantification by employing novel algorithms that outperform stateoftheart alternatives. Local automated multithresholding along with a modified version of the growcut segmentation algorithm is utilized in order to detect areas containing spots as well as the spot centers. The segmentation process is then guided by the information gathered during the detection step, utilizing the detected spot centers as seeds for a region growing approach that separates spot areas, while morphological operators are then utilized in order to accurately detect the protein spots boundaries. SpotDSQ offers an easytouse graphical user interface that requires no special training to operate. Results are exported as images and text data in order to facilitate further analysis. The performance of SpotDSQ was evaluated on real as well as synthetic 2Dgel images using well established statistical measures. Spot detection performance was evaluated by means of precision, sensitivity, and the Fmeasure. Volumetric overlap, volumetric error and volumetric overlap error were utilized for evaluating the segmentation performance. The high Fmeasure (94.8%) value, the low volumetric overlap error (8.3%), and the accurate spot boundaries achieved by SpotDSQ indicate its effectiveness compared to alternative methods. Experimental results show that SpotDSQ outperforms stateoftheart software packages as well as methods proposed in the literature, achieving high accuracy and reduced errors. The advantages of SpotDSQ indicate that it has the potential to be a powerful and reliable tool for 2Dgel image analysis in biomedical laboratories.