We propose novel techniques for microarray image analysis. In particular, we describe an overall pipeline able to solve the most common problems of microarray image analysis. We propose the microarray image rotation algorithm (MIRA) and the statistical gridding pipeline (SGRIP) as two advanced modules devoted to restoring the original microarray grid orientation and to detecting, the correct geometrical information about each spot of input microarray, respectively. Both solutions work by making use of statistical observations, obtaining adaptive and reliable information about each spot property. They improve the performance of the microarray image segmentation pipeline (MISP) we recently developed. MIRA, MISP, and SGRIP modules have been developed as plug-ins for an advanced framework for microarray image analysis. A new quality measure able to effectively evaluate the adaptive segmentation with respect to the fixed (e.g., nonadaptive) circle segmentation of each spot is proposed. Experiments confirm the effectiveness of the proposed techniques in terms of visual and numerical data.
Microarray is a new class of biotechnologies able to help biologist researches to extrapolate new knowledge from biological experiments. Image Analysis is devoted to extrapolate, process and visualize image information. For this reason it has found application also in Microarray, where it is a crucial step of this technology (e.g. segmentation). In this paper we describe MISP (Microarray Image Segmentation Pipeline), a new segmentation pipeline for Microarray Image Analysis. The pipeline uses a recent segmentation algorithm based on statistical analysis coupled with K-Means algorithm. The <i>Spot</i> masks produced by MISP are used to determinate spots information and quality measures. A software prototype system has been developed; it includes visualization, segmentation, information and quality measure extraction. Experiments show the effectiveness of the proposed pipeline both in terms of visual accuracy and measured quality values. Comparisons with existing solutions (e.g. Scanalyze) confirm the improvement with respect to previously published works.
This paper presents a technique to convert surfaces, obtained through a Data Dependent Triangulation, in Bezier Curves by using a Scalable Vector Graphics File format. The method starts from a Data Dependent Triangulation, traces a map of the boundaries present into the triangulation, using the characteristics of the triangles, then the estimated barycenters are connected, and a final conversion of the resulting polylines in curves is performed. After the curves have been estimated and closed the final representation is obtained by sorting the surfaces in a decreasing order. The proposed techniques have been compared with other raster to vector conversions in terms of perceptual quality.
This paper presents a novel raster-to-vector technique for digital images by advanced watershed decomposition coupled with some ad-hoc heuristics devoted to obtain high quality rendering of digital photography. The system is composed by two main steps: first, the image is partitioned into homogeneous and contiguous regions using Watershed decomposition. Then, a Scalable Vector Graphics (SVG) representation of such areas is achieved by <i>ad-hoc</i> chain code building. The final result is an SVG file of the image that can be used for the transmission of pictures through Internet using different display systems (PC, PDA, Cellular Phones). Experimental results and comparisons provide the effectiveness of the proposed method.
The SVG (Scalable Vector Graphics) standard permits to represent complex graphical scenes by a collection of vectorial-based primitives. In this work we are interested in finding some heuristic techniques to cover the gap between the graphical vectorial world and the raster real world typical of digital photography. SVG format could find useful application in the world of mobile imaging devices, where typical camera capabilities should match with limited color/size resolutions displays.
Two different techniques have been applied: Data Dependent Triangulation (DDT) and Wavelet Based Triangulation (WBT). The DDT replaces the input image with a set of triangles according to a specific cost function. The overall perceptual error is then minimized choosing a suitable triangulation. The WBT uses the wavelet multilevel transformation to extract the details from the input image. A triangulation is achieved at the lowest level, introducing large triangles; then the process is iteratively refined, according to the wavelet transformation. That means increasing the quantity of small triangles into the texturized areas and fixing the amount of large triangles into the smooth areas.
Both DDT and WBT are then processed by the polygonalization. The purpose of this function is to merge triangles together reducing the amount of redundancies present into the SVG files.
The proposed technique has been compared with other raster to vector methods showing good performances. Experiments can be found in the SVG UniCT Group page http://svg.dmi.unict.it/.