Classification of breast tissues using Moran's index and Geary's coefficient as texture signatures and SVM

Bráz, Geraldo; Paiva, Anselmo Cardoso de; Silva, Aristófanes Corrêa; Oliveira, Alexandre César Muniz de

doi:10.1016/j.compbiomed.2009.08.009

Cited by 63 publications

(27 citation statements)

References 21 publications

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“…This technique has performed well when applied to image processing of mammograms, especially to distinguish patterns of the mammogram in mass or normal tissue, as reported in Braz et al (2009), Carvalho et al (2012 and Martins et al (2010). Previously, in Rocha et al (2012), the SVM was used successfully for diagnosing breast regions as benign and malignant.…”

Section: Support Vector Machinementioning

confidence: 96%

“…Because the focus of this research was to characterize the mass textures through diversity indexes and determine their malignant nature, we did not use the complete mammogram image. For the sample selection, we adopted the same approach used by Braz et al (2009). With this For the tests performed in this project, a subset of 300 ROIs was used, all chosen at random, totaling 160 malignant masses and 140 benign masses.…”

Section: Image Acquisitionmentioning

confidence: 99%

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Texture analysis of masses in digitized mammograms using Gleason and Menhinick Diversity Indexes

Rocha

Bráz

Silva

et al. 2014

RBEB

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Introduction: Breast cancer is the second most common type of cancer in the world, being more common among women and representing 22% of all new cancer cases every year. The sooner it is diagnosed, the better the chances of a successful treatment are. Mammography is one way to detect non-palpable tumors that cause breast cancer. However, it is known that the sensitivity of this exam can vary considerably due to factors such as the specialist's experience, the patient's age and the quality of the images obtained in the exam. The use of computational techniques involving artificial intelligence and image processing has contributed more and more to support the specialists in obtaining a more precise diagnosis. Methods: This paper proposes a methodology that exclusively uses texture analysis to describe features of masses in digitized mammograms.To increase the efficiency of texture feature extraction, the diversity index's capability to detect patterns of species co-occurrence is used. For this purpose, the Gleason and Menhinick indexes are used. Finally, the extracted texture is classified using the Support Vector Machine, looking to differentiate the malignant masses from the benign. Results: The best result was obtained using the Gleason index, with 86.66% accuracy, 90% sensitivity, 83.33% specificity and an area under the ROC Curve (Az) of 0.86. Conclusion: Both indexes showed statistically similar performance; however, the Gleason index was slightly superior.

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Section: Support Vector Machinementioning

confidence: 96%

Section: Image Acquisitionmentioning

confidence: 99%

Texture analysis of masses in digitized mammograms using Gleason and Menhinick Diversity Indexes

Rocha

Bráz

Silva

et al. 2014

RBEB

View full text Add to dashboard Cite

show abstract

“…It is known that the best prevention method is the precocious diagnosis, what lessens the mortality and improves the treatment. The aim of the dataset used in this paper is to classify samples of benign and malignant tissues, we used a publicly available database of digitized screen-film mammograms: the digital database for screening mammography (DDSM), we used exactly the 273 malignant and 311 benign images used in [6].…”

Section: Datasetsmentioning

confidence: 99%

Ensemble of Local Phase Quantization Variants with Ternary Encoding

Nanni

Brahnam

Lumini

et al. 2013

Local Binary Patterns: New Variants and Applications

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In this chapter, we present some variants of local phase quantization (LPQ), a novel texture descriptor that has been shown to perform well on a variety of classification tasks. After providing an extensive review of LPQ, we report experiments using several new LPQ derivatives obtained by varying LPQ parameters and by using a ternary rather than the binary encoding scheme. Multiple parameter sets are generated and each set is used to train a standard machine-learning classifier, a stand-alone support vector machine. The ensemble is then combined using the sum rule. Extensive experiments are conducted using six different datasets. Our method is compared along with the best state-of-the-art methods for solving each problem represented by the datasets. In each case, the best result is obtained using an ensemble with LPQ variants and ternary encoding. In this study, we also examine the distribution in the images of the most important bins of the LPQ histograms using Gabor filters. We find that incorporating this information into our best texture descriptor approach produces even better results.

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“…The cause of this disease has remained unknown yet, but early detection is so significant for successful control of this cancer with low costs, and considerable decrease in the death rate [2].…”

Section: Introductionmentioning

confidence: 99%

Classification of Benign and Malignant Breast Masses Based on Shape and Texture Features in Sonography Images

2010

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The purpose of this research was evaluating novel shape and texture feature' efficiency in classification of benign and malignant breast masses in sonography images. First, mass regions were extracted from the region of interest (ROI) sub-image by implementing a new hybrid segmentation approach based on level set algorithms. Then two left and right side areas of the masses are elicited. After that, six features (Eccentricity_feature, Solidity_feature, DeferenceArea_Hull_Rectangular, DeferenceArea_Mass_Rectangular, Cross-correlation-left and Cross-correlation-right) based on shape, texture and region characteristics of the masses were extracted for further classification. Finally a support vector machine (SVM) classifier was utilized to classify breast masses. The leave-one-case-out protocol was utilized on a database of eighty pathologically-proven breast sonographic images of patients (forty-seven benign cases and thirty-three malignant cases) to evaluate our method. The classification results showed an overall accuracy of 95.00%, sensitivity of 90.91%, specificity of 97.87%, positive predictive value of 96.77%, negative predictive value of 93.88%, and Matthew's correlation coefficient of 89.71%. The experimental results declare that our proposed method is actually a beneficial tool for the diagnosis of the breast cancer and can provide a second opinion for a physician's decision or can be used for the medicine training especially when coupled with other modalities.

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Classification of breast tissues using Moran's index and Geary's coefficient as texture signatures and SVM

Cited by 63 publications

References 21 publications

Texture analysis of masses in digitized mammograms using Gleason and Menhinick Diversity Indexes

Texture analysis of masses in digitized mammograms using Gleason and Menhinick Diversity Indexes

Ensemble of Local Phase Quantization Variants with Ternary Encoding

Classification of Benign and Malignant Breast Masses Based on Shape and Texture Features in Sonography Images

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