Curvelet Based Offline Analysis of SEM Images

Abstract

Manual offline analysis, of a scanning electron microscopy (SEM) image, is a time consuming process and requires continuous human intervention and efforts. This paper presents an image processing based method for automated offline analyses of SEM images. To this end, our strategy relies on a two-stage process, viz. texture analysis and quantification. The method involves a preprocessing step, aimed at the noise removal, in order to avoid false edges. For texture analysis, the proposed method employs a state of the art Curvelet transform followed by segmentation through a combination of entropy filtering, thresholding and mathematical morphology (MM). The quantification is carried out by the application of a box-counting algorithm, for fractal dimension (FD) calculations, with the ultimate goal of measuring the parameters, like surface area and perimeter. The perimeter is estimated indirectly by counting the boundary boxes of the filled shapes. The proposed method, when applied to a representative set of SEM images, not only showed better results in image segmentation but also exhibited a good accuracy in the calculation of surface area and perimeter. The proposed method outperforms the well-known Watershed segmentation algorithm.

Accurate Microscopic Red Blood Cell Image Enhancement and Segmentation

“…It is the process of partitioning an image to objects, shapes and regions. To elaborate more, image segmentation not only focuses on the discrimination between objects and their background but also on the separation between different regions [4]. The image segmentation techniques can be classified as region based and contour based.…”

A Comparative Study of State-of-the-Art Deep Learning Models for Semantic Segmentation of Pores in Scanning Electron Microscope Images of Activated Carbon

“…Traditional image processing approaches such as the watershed method and its variations [20], histogrambased methods [21], graph-cut approaches [22], and active contour methods [23], have the benefit of being effective, but to use them effectively on a variety of images, it is necessary to perform significant parameter tuning. In most cases, image preprocessing techniques like resizing, filtering, thresholding, enhancement, morphological operations, color correction, and others have to be implemented before any of these approaches [24], [25]. All of the available tools and image processing approaches require substantial parameter tuning, often lack automation, are error-prone, require domain expertise, and are lowly accurate, making them nongeneric and time-consuming [24]- [27].…”

“…In most cases, image preprocessing techniques like resizing, filtering, thresholding, enhancement, morphological operations, color correction, and others have to be implemented before any of these approaches [24], [25]. All of the available tools and image processing approaches require substantial parameter tuning, often lack automation, are error-prone, require domain expertise, and are lowly accurate, making them nongeneric and time-consuming [24]- [27]. Manually analyzing SEM images using tools and traditional approaches is inefficient, with its challenges.…”

An improved contrast equalization technique for contrast enhancement in scanning electron microscopy images

“…Recently, microscopic technology has thrived and led to the delivery of an unprecedented view of microscopic items that cannot be seen by the naked eye (Cocks, Taggart, Rind, & White, ). Electron microscopes are the most powerful yet flexible devices available for the depiction of microstructures of different materials (Shirazi, Haq, Hayat, Naz, & Haque, ). In particular, scanning electron microscopy (SEM) has produced outstanding images for extremely small items, providing highly beneficial images for a myriad of scientific fields (Trampert et al, ).…”