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.

A 2D gel electrophoresis DNA image analysis algorithm with automatic thresholding Polymerase chain reaction (PCR) and gel electrophoresis are two widely used techniques for genetic studies that require the bench scientist to perform many tedious manual steps. Advances in automation are making these techniques more accessible, but detection and image analysis still remain laborintensive. Although several commercial software packages are now available, DNA image analysis still requires some intervention by the user, and thus a certain level of image processing expertise. To allow researchers to speed up their analyses and obtain more repeatable results, we present a fully automated image analysis system for DNA or protein studies with high accuracy. The proposed system is based mainly on four steps: automatic thresholding, shifting, filtering, and processing. The automatic thresholding that is used to equalize the gray values of the gel electrophoreses image background is one of the key and novel operations in this algorithm. An enhancement is also used to improve poor quality images that have faint DNA bands. Experimental results show that the proposed method eliminates defects due to noise for good and average quality gel electrophoresis images, while it also improves the appearance of poor quality images.