Texture Analysis of Abnormal Cell Images for Predicting the Continuum of Colorectal Cancer.

Ahmad Chaddad,Camel Tanougast

doi:10.1155/2017/8428102

Ahmad Chaddad, Camel Tanougast

Open Access

https://doi.org/10.1155/2017/8428102

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Abstract

Abnormal cell (ABC) is a markedly heterogeneous tissue area and can be categorized into three main types: benign hyperplasia (BH), carcinoma (Ca), and intraepithelial neoplasia (IN) or precursor cancerous lesion. In this study, the goal is to determine and characterize the continuum of colorectal cancer by using a 3D-texture approach. ABC was segmented in preprocessing step using an active contour segmentation technique. Cell types were analyzed based on textural features extracted from the gray level cooccurrence matrices (GLCMs). Significant texture features were selected using an analysis of variance (ANOVA) of ABC with a p value cutoff of p < 0.01. Features selected were reduced with a principal component analysis (PCA), which accounted for 97% of the cumulative variance from significant features. The simulation results identified 158 significant features based on ANOVA from a total of 624 texture features extracted from GLCMs. Performance metrics of ABC discrimination based on significant texture features showed 92.59% classification accuracy, 100% sensitivity, and 94.44% specificity. These findings suggest that texture features extracted from GLCMs are sensitive enough to discriminate between the ABC types and offer the opportunity to predict cell characteristics of colorectal cancer.

Highlights

Colorectal cancer (CRC) represents one of the most frequent cancers affecting people [1]
Images were captured by a charge coupled device (CCD) camera integrated with a liquid crystal tunable filter (LCTF) in the optical microscopy system [20]
According to the experiments in which different groups of texture feature were applied to the Abnormal cell (ABC) discrimination process, the results showed the efficiency of principal components (PCs) features derived from significant texture features extracted using gray level cooccurrence matrices (GLCMs) for histopathology colorectal cancer image analysis

Summary

Introduction

Colorectal cancer (CRC) represents one of the most frequent cancers affecting people [1]. Up to 30% of CRC patients who undergo surgical resection of the primary tumor experience a subsequent relapse within 3 years and with a median time to death of 12 months [3, 4]. Colorectal cells are transformed by CRC into anomalous and heterogeneous shapes [5, 6]. Attempts to quantify heterogeneity have been made using multiple feature functions such as Haralick features [6] Another instance has used the link between the texture of hepatic tissue and its entropy and uniformity to predict survival using computer tomography images [7]. Limited studies have used texture features to assess the continuum of CRC from benign to malignant cells

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