Estimating the fractal dimension of images using pixel range calculation technique

Ranganath, Abadhan; Senapati, Manas Ranjan; Sahu, Pradip Kumar

doi:10.1007/s00371-020-01829-1

Cited by 19 publications

(7 citation statements)

References 35 publications

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“…In classification, feature extraction is crucial [4], [27], and [33]. Several types of image features have been examined and evaluated for classification applications.…”

Section: Features Extractionmentioning

confidence: 99%

Improving Breast Cancer Classification using (SMOTE) Technique and Pectoral Muscle Removal in Mammographic Images

Abdulla

Sagheer

Veisi

2021

mendel

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Computer-aided diagnosis methods are being developed to assist radiologists to improve the interpretation of mammograms for the detection and diagnose of breast cancer, reduce the errors and mistakes made by human beings. In addition, it provides a more accurate and reliable classification of benign and malignant abnormalities. In the mammogram diagnosis, the pectoral muscle appears in Mediolateral oblique views (MLO) of the right and left of the breast. Considering that, the pectoral muscle has the same density as the small suspicious masses in the image and can affect/bias the results of image processing methods. This paper presents a diagnosis method to detect an abnormality in mammograms automatically. Before abnormality identification, image-processing techniques are used to correctly segment the suspicious region-of-interest (ROI). The background of the mammograms has been darkened to distinguish the breast area from any blemishes or writings that will be removed. Then the breast area has been extracted after ignoring the empty regions around the breast in mammogram images. After that, the mammogram image is inverted and the inverted image is then subtracted from the initial image. For pectoral muscle removal, a region growing method using the K-means clustering method is used. Afterward, suspicious ROI is segmented utilizing the K-means with thresholding technique. To detect abnormalities in mammograms, shape-based features, moment invariants, and also fractal dimensions are extracted from the segmented ROI. The Mini-MIAS dataset is used to evaluate the proposed method and is predominately composed of benign samples with only a tiny percent of malignant samples. To accomplish far better classifier efficiency, the SMOTE algorithm is used to present new samples from the minority classes to get a balanced dataset. Random forest classifier utilized to classify the segmented region as benign and malignant. The experimental results obtained an accuracy of 97.1%, the sensitivity of 95.1%, and the achieved specificity is 98.5%.

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“…In classification, feature extraction is crucial [4], [27], and [33]. Several types of image features have been examined and evaluated for classification applications.…”

Section: Features Extractionmentioning

confidence: 99%

Improving Breast Cancer Classification using (SMOTE) Technique and Pectoral Muscle Removal in Mammographic Images

Abdulla

Sagheer

Veisi

2021

mendel

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“…Its output is a binary image that inputs a Python code for fractal dimension estimation using the box‐counting method. The readers interested in more details about the Fractal dimension estimation from the image are referred to References 3,40. It is assumed that the level of the fractal mass distribution is equal for all three dimensions, then the average value of

β

obtained from the fractal dimension is considered as

α_{k}

cited in Equation (8) and others (see Table 1).…”

Section: Experimental Testsmentioning

confidence: 99%

The fractal version of the bond‐based peridynamics method

Armandei¹,

Sarzosa²,

Savioli³

2021

Numerical Meth Engineering

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This article presents a new formulation of bond‐based peridynamic theory (PD) encompassing material texture heterogeneity through fractal geometry concepts and transforming all relevant variables from Euclidean space to fractal space. The heterogeneity incorporation is realized through the introduction of a linear transformation fractal coefficient (LTFC). By considering a mean value for all LTFCs of all spatial coordinates. For example, x, y, z, and using the relationship between fractal dimension Df and the Hurst exponent H. We present a fractal version of the bond‐based PD method that the equation of motion is conformed to fractal space. Validation of the formulation is accomplished in two steps: first, the effect of irregular mass distribution is characterized by the fractal dimension of the fracture surface roughness through experimental results of a series of single‐edge notched tensile tests of a ductile material. In the next step, the proposed formulation is calibrated with the fractal dimension obtained experimentally. The results are compared with the classical bond‐based PD formulation and the finite element analysis results. The obtained results in this article indicate that the irregular mass distribution of material can be introduced into particle‐based numerical methods using an adequate fractal exponent. The fractal version provides a good approximation of the fracture behavior of materials.

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“…Fractal data analysis has emerged as a superior option for structural analysis of other types of images for future research, particularly for healthcare applications like medical-based MRI images, based on the notable improvement in results. Pixel range calculation (PRC) method, which is based on a modified version of the box counting method, was used by Ranganath et al (2021) for fractal analysis of digital images to determine their texture, smoothness, or roughness.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach for Object Recognition Using Decision Tree Clustering by Incorporating Multi-Level BPNN Classifiers and Hybrid Texture Features

Kumar

2024

International Journal of Information Retrieval Research

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This work proposes a novel approach to object recognition, particularly for human faces, based on the principle of human cognition. The suggested approach can handle a dataset or problem with a large number of classes for classification more effectively. The model for the facial recognition-based object detection system was constructed using a combination of decision tree clustering based multi-level Backpropagation neural network classifier-TFMLBPNN-DTC and hybrid texture feature (ILMFD+GLCM) and applied on NS and ORL databases. This model produced the classification accuracy (±standard deviation) of 95.37 ±0.951877% and 90.83 ± 1.374369% for single input and 96.58 ±0.5604582% and 91.50 ± 2.850439% for group-based decision for NS and ORL database respectively. The better classification results encourage its application to other object recognition and classification issues. This work's basic idea also makes it easier to improve classification management for a wide range of classes.

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Estimating the fractal dimension of images using pixel range calculation technique

Cited by 19 publications

References 35 publications

Improving Breast Cancer Classification using (SMOTE) Technique and Pectoral Muscle Removal in Mammographic Images

Improving Breast Cancer Classification using (SMOTE) Technique and Pectoral Muscle Removal in Mammographic Images

The fractal version of the bond‐based peridynamics method

A Novel Approach for Object Recognition Using Decision Tree Clustering by Incorporating Multi-Level BPNN Classifiers and Hybrid Texture Features

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