Wavelet-Based 3-D Multifractal Spectrum with Applications in Breast MRI Images (original) (raw)
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Automatic prediction of tumour malignancy in breast cancer with fractal dimension
Royal Society open science, 2016
Breast cancer is one of the most prevalent types of cancer today in women. The main avenue of diagnosis is through manual examination of histopathology tissue slides. Such a process is often subjective and error-ridden, suffering from both inter- and intraobserver variability. Our objective is to develop an automatic algorithm for analysing histopathology slides free of human subjectivity. Here, we calculate the fractal dimension of images of numerous breast cancer slides, at magnifications of 40×, 100×, 200× and 400×. Using machine learning, specifically, the support vector machine (SVM) method, the F1 score for classification accuracy of the 40× slides was found to be 0.979. Multiclass classification on the 40× slides yielded an accuracy of 0.556. A reduction of the size and scope of the SVM training set gave an average F1 score of 0.964. Taken together, these results show great promise in the use of fractal dimension to predict tumour malignancy.
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Proceedings of the 8th …, 2008
The purpose of this research was finding differences between medical images in order of their classifications in terms of separation malign tissue from normal and benign tissue. The diagnostics of malign tissue is of the crucial importance in medicine. Therefore, ascertainment of the correlation between multifractals parameters and "chaotic" cells could be of the great appliance. This paper shows the application of multifractal analysis for additional help in cancer diagnosis, as well as diminishing. of the subjective factor and error probability. The results of computer analysis of images have been presented and discussed.
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Over the last years, fractal and multifractal geometries were applied extensively in many medical signal (1D, 2D or 3D) analysis applications like pattern recognition, texture analysis and segmentation. Application of this geometry relies heavily on the estimation of the fractal features. Various methods were proposed to estimate the fractal dimension or multifractal spectral of a signal. This article presents an overview of these algorithms, the way they work, their benefits and their limits. The aim of this review is to explain and to categorize the various algorithms into groups and their application in the field of medical signal analysis.
The application of fractal analysis to mammographic tissue classification
Cancer Letters, 1994
As a first step in determining the effkacy of using computers to assist in diagnosis of medical images, an investigation has been conducted which utilizes the patterns, or textures, in the images. To be of value, any computer scheme must be able to recognize and differentiate the various patterns. An obvious example of this in mammography is the recognition of tumorous tissue and non-malignant abnormal tissue from normal parenchymal tissue. We have developed a pattern recognition technique which uses features derived from the fractal nature of the image. Further. we are able to develop mathematical models which can be used to differentiate and classify the many tissue types. Based on a limited number of cases of digitized mammograms, our computer algorithms have been able to distinguish tumorous from healthy tissue and to distinguish among various parenchymal tissue patterns. These preliminary results indicate that discrimination based on the fractal nature of images may well represent a viable approach to utilizing computers to assist in diagnosis.
Application of Multifractal Analysis on Medical Images
wseas.us, 2008
This paper shows results of computer analysis of images in the purpose of finding differences between medical images in order of their classifications in terms of separation malign tissue from normal and benign tissue. The diagnostics of malign tissue is of the crucial importance in medicine. Therefore, ascertainment of the correlation between multifractals parameters and "chaotic" cells could be of
Applied Sciences
Breast cancer detection using mammogram images at an early stage is an important step in disease diagnostics. We propose a new method for the classification of benign or malignant breast cancer from mammogram images. Hybrid thresholding and the machine learning method are used to derive the region of interest (ROI). The derived ROI is then separated into five different blocks. The wavelet transform is applied to suppress noise from each produced block based on BayesShrink soft thresholding by capturing high and low frequencies within different sub-bands. An improved fractal dimension (FD) approach, called multi-FD (M-FD), is proposed to extract multiple features from each denoised block. The number of features extracted is then reduced by a genetic algorithm. Five classifiers are trained and used with the artificial neural network (ANN) to classify the extracted features from each block. Lastly, the fusion process is performed on the results of five blocks to obtain the final decisi...
Discrete wavelet for multifractal texture classification: application to medical ultrasound imaging
2010
This paper deals with multifractal characterization of skin cancer in ultrasound images. The proposed method establishes a multifractal analysis framework of such images based on a new multiresolution indicator, called the maximum wavelet coefficient, derived from the wavelet leaders. Two main contributions are brought up: first, it proposes a method for the estimation of multifractal features. Second, it reveals the potential of multifractal features to characterize skin melanoma. In order to study the efficiency of our maximum coefficient estimator, we compare its results on a simulated image against wavelet leaders based estimator. We then apply the approach on various samples from different skin images. Results show that the extracted features make a promising quantitative indicator to distinguish between different tissues.
FRACTAL DIMENSION FOR CHARACTERIZATION OF FOCAL BREAST LESIONS
Breast cancer is one of the diseases that cause more deaths around the world. One of the ways to reduce the high rates is the detection of the disease in its early stages. The faster the cancer is diagnosed, the greater the chances of cure without major consequences. Mammography is considered the most effective way to diagnose the lump in the breast. However, due to some factors, it is not always possible to draw conclusive results through the exam. In order to minimize misunderstandings on examinations and to assist the experts, increasingly computational techniques are used during the diagnosis. The similarity between fractals and the nodules suggests that the calculation of fractal dimension can be used as a means of classifying focal breast lesions. Through the results obtained in this work, we conclude that the fractal analysis of the mass outline is an efficient way of classifying mammograms.
Multifractal-Based Features for Medical Images Classification
This paper presents a method to classify colored textural images of skin tissues. Since medical images have highly heterogeneity, the development of reliable skin-cancer detection process is difficult, and a mono fractal dimension is not sufficient to classify images of this nature. A multifractal-based feature vectors are suggested here as an alternative and more effective tool. At the same time multiple color channels are used to get more descriptive features. Two multifractal based set of features are suggested here. The first set measures the local roughness property, while the second set measure the local contrast property.A combination of all the extracted features from the three color models gives a highest classification accuracy with 99.4048% for training and 95.8333% for testing.
Fractal-based brain tumor detection in multimodal MRI
Applied Mathematics and Computation, 2009
In this work, we investigate the effectiveness of fusing two novel texture features along with intensity in multimodal magnetic resonance (MR) images for pediatric brain tumor segmentation and classification. One of the two texture features involves our Piecewise-Triangular-Prism-Surface-Area (PTPSA) algorithm for fractal feature extraction. The other texture feature exploits our novel fractional Brownian motion (fBm) framework that combines both fractal and wavelet analyses for fractalwavelet feature extraction. We exploit three MR image modalities such as T1 (gadolinium-enhanced), T2 and FLuid-Attenuated Inversion-Recovery (FLAIR), respectively. The extracted features from these multimodality MR images are fused using Self-Organizing Map (SOM). For a total of 204 T1 contrast-enhanced, T2 and FLAIR MR images obtained from nine different pediatric patients, our successful tumor segmentation is 100%. Our experimental results suggest that the fusion of fractal, fractalwavelet and intensity features in multimodality MR images offers better tumor segmentation results when compared to that of just fractal and intensity features in single modality MR images. Next, we exploit a multi-layer feedforward neural network with automated Bayesian regularization to classify the tumor regions from non-tumor regions. The Receiver Operating Characteristic (ROC) curves are obtained to evaluate tumor classification performance. The ROC suggests that at a threshold value of 0.7, the True Positive Fraction (TPF) values range from 75% to 100% for different patients, with the average value of 90%.