Mammographic masses characterization based on localized texture and dataset fractal analysis using linear, neural and support vector machine classifiers

We describe a decision support system to distinguish among hematology cases directly from microscopic specimens. The system uses an image database containing digitized specimens from normal and four different hematologic malignancies. Initially, the nuclei and cytoplasmic components of the specimens are segmented using a robust color gradient vector flow active contour model. Using a few cell images from each class, the basic texture elements (textons) for the nuclei and cytoplasm are learned, and the cells are represented through texton histograms. We propose to use support vector machines on the texton histogram based cell representation and achieve major improvement over the commonly used classification methods in texture research. Experiments with 3,691 cell images from 105 patients which originated from four different hospitals indicate more than 84% classification performance for individual cells and 89% for case based classification for the five class problem.

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“…It has been successfully applied in breast cancer [32,33,42], liver cancer [23] and obstructive lung disease [2].…”

Section: Introductionmentioning

confidence: 99%

Classification of hematologic malignancies using texton signatures

Tuzel

Yang

Meer

et al. 2007

Pattern Anal Applic

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“…There are a few reports about the clinical use of SVMs in the diagnosis of neuromuscular disorders, the management of patients with gastrointestinal bleeding, and mammographic mass characterization [46][47][48]. In our study, imagebased clinical decision support systems, such as SVM or ANN, could provide probability of having a prostate cancer with inputs of various clinical data, thus aiding the clinician in the decision of whether or not to pursue a prostate biopsy.…”

Section: Discussionmentioning

confidence: 90%

Image-based clinical decision support for transrectal ultrasound in the diagnosis of prostate cancer: comparison of multiple logistic regression, artificial neural network, and support vector machine

et al. 2009

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“…Mavroforakis et al [15] established a quantitative approach of mammographic masses texture classification, supported by fractal analysis of the dataset of the extracted textural features. A set of textural feature functions was applied on mammograms, in multiple configurations and scales, constructing "signatures" for benign and malignant cases of tumors.…”

Section: Mammography and Fractalsmentioning

confidence: 99%

New Algorithm for Fractal Dimension Estimation based on Texture Measurements: Application on Breast Tissue Characterization

Khemis¹,

Lazzouni²,

Messadi³

et al. 2016

IJIGSP

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Abstract-Fractal analysis is currently in full swing in particular in the medical field because of the fractal nature of natural phenomena (vascular system, nervous system, bones, breast tissue ...). For this, many algorithms for estimating the fractal dimension have emerged. Most of them are based on the principle of box counting. In this work we propose a new method for calculating fractal attributes based on contrast homogeneity and energy that have been extracted from gray level co-occurrence matrix. As application we are investigated in the characterization and classification of mammographic images with SuportVectorMachine classifier. We considered in particular images with tumor masses and architectural disorder to compare with normal ones. We calculate, for comparison the fractal dimension obtained by a reference method (triangular prism) and perform a classification similar to the previous. Results obtained with new algorithm are better than reference method (classification rate is 0.91 vs 0.65). Hence new fractal attributes are relevant.

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Mammographic masses characterization based on localized texture and dataset fractal analysis using linear, neural and support vector machine classifiers

Cited by 98 publications

References 37 publications

Classification of hematologic malignancies using texton signatures

Classification of hematologic malignancies using texton signatures

Image-based clinical decision support for transrectal ultrasound in the diagnosis of prostate cancer: comparison of multiple logistic regression, artificial neural network, and support vector machine

New Algorithm for Fractal Dimension Estimation based on Texture Measurements: Application on Breast Tissue Characterization

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