A system for detection of cervical precancerous in field emission scanning electron microscope images using texture features

Abstract

This study develops a novel cervical precancerous detection system by using texture analysis of¯eld emission scanning electron microscopy (FE-SEM) images. The processing scheme adopted in the proposed system focused on two steps. The¯rst step was to enhance cervical cell FE-SEM images in order to show the precancerous characterization indicator. A problem arises from the question of how to extract features which characterize cervical precancerous cells. For the¯rst step, a preprocessing technique called intensity transformation and morphological operation (ITMO) algorithm used to enhance the quality of images was proposed. The algorithm consisted of contrast stretching and morphological opening operations. The second step was to characterize the cervical cells to three classes, namely normal, low grade intra-epithelial squamous lesion (LSIL), and high grade intra-epithelial squamous lesion (HSIL). To di®eren-tiate between normal and precancerous cells of the cervical cell FE-SEM images, human papillomavirus (HPV) contained in the surface of cells were used as indicators. In this paper, we investigated the use of texture as a tool in determining precancerous cell images based on the observation that cell images have a distinct visual texture. Gray level co-occurrences matrix (GLCM) technique was used to extract the texture features. To con¯rm the system's performance, the system was tested using 150 cervical cell FE-SEM images. The results showed that the accuracy, sensitivity and speci¯city of the proposed system are 95.7%, 95.7% and 95.8%, respectively.

Modeling observer happiness from facial hyperspectral sensor

“…Some findings in the literature claim that the features from segmented local textures of a facial image region can provide more information than those directly extracted from the whole facial region (Andrearczyk and Whelan, 2018; Hernández et al , 2007; Jusman et al , 2017). Many studies also propose a block-based method to deal with specific dynamic events, which takes into account an extension of facial texture, producing dynamic texture patterns of the spatiotemporal domain (Huang et al , 2016; Xiaohua et al , 2017; Zhao and Pietikainen, 2007).…”

The Classification for Steel Corroded Reinforced Concrete Beams Using Linear Discriminant Analysis

“…Also, it naturally handles problems with two classes and more. It can estimate the probability for each of the candidate classes [28].…”

“…The The LDA computes a linear transformation matrix W ∈ Rd×(C−1), and usually d ≫ C. The transformation matrix projects data from the original high-dimensional space into a lowdimensional space, maximizing the between-class distance while minimizing the within-class distance. Traditional LDA finds the optimal transformation matrix WLDA by solving the optimization problem [28].…”

Guidelines for a Morphometric Analysis of Prokaryotic and Eukaryotic Cells by Scanning Electron Microscopy

“…Since SEM enables to capture fine shapes of biological objects, this technique has been routinely used to collect images suitable for the analysis of textural descriptors [ 99 , 100 , 101 , 102 ]. Still, it should be borne in mind that the processing method could affect the texture of a sample.…”