GLCM and GLRLM based Feature Extraction Technique in Mammogram Images

Preetha, K; Jayanthi, S.

doi:10.14419/ijet.v7i2.21.12378

Cited by 18 publications

(7 citation statements)

References 2 publications

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“…GLCM textures as originally described in 1973 by Haralick and others. The GLCM functions characterize the textures of an image by calculating how often a pair of the pixel with gray-level or value i occur either horizontally, vertically, or diagonally to adjacent pixels with the value j, where i values and j values represent gray level values in the image (Preetha & Jayanthi, 2018). The texture features used are Contrast, Correlation, Energy and Homogeneity with the following feature equations:  Contrast returns a measure of the intensity contrast between a pixel and its neighbor over an image (Preetha & Jayanthi, 2018).…”

Section: Proposed Methodsmentioning

confidence: 99%

“… Correlation is the measure of how correlated a pixel is to its neighbor in an image (Preetha & Jayanthi, 2018). The following if correlation formula:…”

Section: Proposed Methodsmentioning

confidence: 99%

“… Energy also known as uniformity or Angular Second Moment (ASM) is the sum of squared elements in the GLCM (Preetha & Jayanthi, 2018). The following if energy formula:…”

Section: Proposed Methodsmentioning

confidence: 99%

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Classification of Dermoscopic Image of Skin Cancer Using the GLCM Method and Multi-SVM Algorithm

Hasanah

Riana

2021

Rekayasa

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The development of abnormal skin pigment cells can cause a skin cancer called melanoma. Melanoma can be cured if diagnosed and treated in its early stages. Various studies using various technologies have been developed to conduct early detection of melanoma. This research was conducted to diagnose melanoma skin cancer with digital image processing techniques on the dermoscopic image of skin cancer. The diagnosis is made by classifying dermoscopic images based on the types of Common Nevus, Atypical Nevus or Melanoma. Pre-processing is done by changing the RGB image to grayscale (grayscaling), smoothing image using median filtering, and image segmentation based on binary images of skin lesions. The value of Contrast, Correlation, Energy and Homogeneity obtained from the texture feature extraction of the GLCM method is used in the next step, which is the classification process with the Multi-SVM algorithm. The proposed research method shows high accuracy results in diagnosing skin cancer

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Section: Proposed Methodsmentioning

confidence: 99%

“… Correlation is the measure of how correlated a pixel is to its neighbor in an image (Preetha & Jayanthi, 2018). The following if correlation formula:…”

Section: Proposed Methodsmentioning

confidence: 99%

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Classification of Dermoscopic Image of Skin Cancer Using the GLCM Method and Multi-SVM Algorithm

Hasanah

Riana

2021

Rekayasa

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“…GLCM is referred to as a second-order statistics method which considers the spatial relationship between a couple of pixels. GLRLM helps in obtaining higher-order statistical features consisting of a set of continuous pixels having similar gray levels [63]. GLDM extracts the features by computing a gray-level absolute difference method between two pixels separated by specific displacement [64].…”

Section: Related Workmentioning

confidence: 99%

Hybrid bio-inspired algorithm and convolutional neural network for automatic lung tumor detection

Vijh¹,

Gaurav

Pandey

2020

Neural Comput & Applic

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In this paper, we have proposed a hybrid bio-inspired algorithm which takes the merits of whale optimization algorithm (WOA) and adaptive particle swarm optimization (APSO). The proposed algorithm is referred as the hybrid WOA_APSO algorithm. We utilize a convolutional neural network (CNN) for classification purposes. Extensive experiments are performed to evaluate the performance of the proposed model. Here, pre-processing and segmentation are performed on 120 lung CT images for obtaining the segmented tumored and non-tumored region nodule. The statistical, texture, geometrical and structural features are extracted from the processed image using different techniques. The optimized feature selection plays a crucial role in determining the accuracy of the classification algorithm. The novel variant of whale optimization algorithm and adaptive particle swarm optimization, hybrid bio-inspired WOA_APSO, is proposed for selecting optimized features. The feature selection grouping is applied by embedding linear discriminant analysis which helps in determining the reduced dimensions of subsets. Twofold performance comparisons are done. First, we compare the performance against the different classification techniques such as support vector machine, artificial neural network (ANN) and CNN. Second, the computational cost of the hybrid WOA_APSO is compared with the standard WOA and APSO algorithms. The experimental result reveals that the proposed algorithm is capable of automatic lung tumor detection and it outperforms the other state-of-the-art methods on standard quality measures such as accuracy (97.18%), sensitivity (97%) and specificity (98.66%). The results reported in this paper are encouraging; hence, these results will motivate other researchers to explore more in this direction.

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“…Various inquiries have been done on the textural examination of images by proposing distinctive feature extraction methods. The extracted features similar histogram, DWT, GLCM, and GLRLM are analyzed independently [10]. Textural features can likewise be extracted from the spectral domains, for example, the frequency domain, the wavelet domain, and the Gabor domain [11].…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction Techniques in Associate with Opposition Based Whale Optimization Algorithm

HimaBindu¹,

Anuradha²,

Murty³

2019

ISI

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Near-duplicate video (NDV) detection is an important issue of copyright protection. However, the traditional detection methods are very imprecise and complex. To solve the problem, this paper introduces the opposition-based solution generation strategy into the conventional whale optimization algorithm (OWOA), creating a novel NDV detection method called the OWOA. The author detailed how to use the hybrid method to extract different types of features, ranging from color, shape to texture, and compared the OWOA with traditional feature extraction methods through experiments. The comparison shows that the OWOA achieved the optimal performance in DNV detection. The research findings can greatly assist regulatory authorities in monitoring and detecting edited contents.

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GLCM and GLRLM based Feature Extraction Technique in Mammogram Images

Cited by 18 publications

References 2 publications

Classification of Dermoscopic Image of Skin Cancer Using the GLCM Method and Multi-SVM Algorithm

Classification of Dermoscopic Image of Skin Cancer Using the GLCM Method and Multi-SVM Algorithm

Hybrid bio-inspired algorithm and convolutional neural network for automatic lung tumor detection

Feature Extraction Techniques in Associate with Opposition Based Whale Optimization Algorithm

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