Colon Cancer Detection in Biopsy Images for Indian Population at Different Magnification Factors Using Texture Features

Babu, Tina; Singh, Tripty; Gupta, Deepa; Hameed, Shahin

doi:10.1109/icoac.2017.8441173

Cited by 15 publications

(3 citation statements)

References 21 publications

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“…The concept, GRLM, is based on the reality that many neighboring pixels with the same gray level are characterized by coarse texture features [ 42 , 44 , 45 , 47 ]. For a given image, GLRLM P ( i , j ) is calculated by representing the total runs of pixels having gray level i and run length j in a particular direction.…”

Section: Methodsmentioning

confidence: 99%

“…. GLCM is one of the most widespread techniques of texture analysis that quantitatively measured the frequency of different combinations of pixel brightness values (gray levels) which occur in an image, and it has been used in a number of applications, e.g., [42][43][44][45][46][47][48]. In this step, texture features that contain information about the image are computed by GLCM to extract second-order statistic texture features (Table 1).…”

Section: Gray Level Cooccurrence Matrix (Glcm)mentioning

confidence: 99%

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A Novel Intelligent System for Brain Tumor Diagnosis Based on a Composite Neutrosophic-Slantlet Transform Domain for Statistical Texture Feature Extraction

Wady

Yousif

Hasan

2020

BioMed Research International

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Discrete wavelet transform (DWT) is often implemented by an iterative filter bank; hence, a lake of optimization of a discrete time basis is observed with respect to time localization for a constant number of zero moments. This paper discusses and presents an improved form of DWT for feature extraction, called Slantlet transform (SLT) along with neutrosophy, a generalization of fuzzy logic, which is a relatively new logic. Thus, a novel composite NS-SLT model has been suggested as a source to derive statistical texture features that used to identify the malignancy of brain tumor. The MR images in the neutrosophic domain are defined using three membership sets, true (T), false (F), and indeterminate (I); then, SLT was applied to each membership set. Three statistical measurement-based methods are used to extract texture features from images of brain MRI. One-way ANOVA has been applied as a method of reducing the number of extracted features for the classifiers; then, the extracted features are subsequently provided to the four neural network classification techniques, Support Vector Machine Neural Network (SVM-NN), Decision Tree Neural Network (DT-NN), K-Nearest Neighbor Neural Network (KNN-NN), and Naive Bayes Neural Networks (NB-NN), to predict the type of the brain tumor. Meanwhile, the performance of the proposed model is assessed by calculating average accuracy, precision, sensitivity, specificity, and Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve. The experimental results demonstrate that the proposed approach is quite accurate and efficient for diagnosing brain tumors when the Gray Level Run Length Matrix (GLRLM) features derived from the composite NS-SLT technique is used.

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Section: Methodsmentioning

confidence: 99%

Section: Gray Level Cooccurrence Matrix (Glcm)mentioning

confidence: 99%

A Novel Intelligent System for Brain Tumor Diagnosis Based on a Composite Neutrosophic-Slantlet Transform Domain for Statistical Texture Feature Extraction

Wady

Yousif

Hasan

2020

BioMed Research International

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“…Furthermore, Rathore et al [11] optimized the segmentation parameters for each magnification (4X, 5X, 10X, and 40X) for ellipse fitting algorithm using genetic algorithm and extracted gray-level co-occurrence matrix (GLCM)-based as well as gray-level histogram moment features from the segmented ROI to classify colon biopsy images through an SVM classifier, thereby attaining 92.33% average accuracy. Across various magnified colon images (10X, 20X, 40X), for cancer detection, texture, shape, and wavelet features were analyzed and classified using multi-classifier models in [12][13][14][15]. Abdulhay et al [16] suggested a strategy for the segmentation of blood leukocytes using static microscopes to classify 100 unique magnified microscopic pictures (72-abnormal, 38-normal) by using SVM for the tuned segmentation and filtering of the non-ROI image using local binary patterns and texture characteristics with a 95.3% accuracy.…”

Section: Introductionmentioning

confidence: 99%

Robust Magnification Independent Colon Biopsy Grading System over Multiple Data Sources

Babu¹,

Gupta²,

Singh³

et al. 2021

Computers, Materials &Amp; Continua

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Automated grading of colon biopsy images across all magnifications is challenging because of tailored segmentation and dependent features on each magnification. This work presents a novel approach of robust magnification-independent colon cancer grading framework to distinguish colon biopsy images into four classes: normal, well, moderate, and poor. The contribution of this research is to develop a magnification invariant hybrid feature set comprising cartoon feature, Gabor wavelet, wavelet moments, HSV histogram, color auto-correlogram, color moments, and morphological features that can be used to characterize different grades. Besides, the classifier is modeled as a multiclass structure with six binary class Bayesian optimized random forest (BO-RF) classifiers. This study uses four datasets (two collected from Indian hospitals-Ishita Pathology Center (IPC) of 4X, 10X, and 40X and Aster Medcity (AMC) of 10X, 20X, and 40X-two benchmark datasets-gland segmentation (GlaS) of 20X and IMEDIATREAT of 10X) comprising multiple microscope magnifications. Experimental results demonstrate that the proposed method outperforms the other methods used for colon cancer grading in terms of accuracy (97.25%-IPC, 94.40%-AMC, 97.58%-GlaS, 99.16%-Imediatreat), sensitivity (0.9725-IPC, 0.9440-AMC, 0.9807-GlaS, 0.9923-Imediatreat), specificity (0.9908-IPC, 0.9813-AMC, 0.9907-GlaS, 0.9971-Imediatreat) and F-score (0.9725-IPC, 0.9441-AMC, 0.9780-GlaS, 0.9923-Imediatreat). The generalizability of the model to any magnified input image is validated by training in one dataset and testing in another dataset, highlighting strong concordance in multiclass classification and evidencing its effective use in the first level of automatic biopsy grading and second opinion.

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A Survey of Text Mining Approaches, Techniques, and Tools on Discharge Summaries

Nair

Gupta

Devi

2020

Advances in Intelligent Systems and Computing

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Colon Cancer Detection in Biopsy Images for Indian Population at Different Magnification Factors Using Texture Features

Cited by 15 publications

References 21 publications

A Novel Intelligent System for Brain Tumor Diagnosis Based on a Composite Neutrosophic-Slantlet Transform Domain for Statistical Texture Feature Extraction

A Novel Intelligent System for Brain Tumor Diagnosis Based on a Composite Neutrosophic-Slantlet Transform Domain for Statistical Texture Feature Extraction

Robust Magnification Independent Colon Biopsy Grading System over Multiple Data Sources

A Survey of Text Mining Approaches, Techniques, and Tools on Discharge Summaries

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