Segmentation and Texture Analysis for Efficient Classification of Breast Tumors from Sonograms

Author(s): Ezhilarasu Palani, Krishnaraj Nagappan, Basim Alhadidi.

Journal Name: Current Signal Transduction Therapy

Volume 11 , Issue 2 , 2016

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Abstract:

Background: Mammographies are a significant technology utilized in the effective detection of breast cancers prior to them becoming palpable during selfexaminations. The primary aim of the study was the determination of screening precision of mammographies as well as ultrasounds in local populations.

Method: In the current study, Minimum Spanning Tree (MST) segmentations are suggested for the selection of minute weights amongst all spanning trees. In the suggested method, Fuzzy Local Binary Patterns (FLBPs) are images comprising micro-patterns. LBPs are first-order circular derivatives of patterns which are created through the concatenation of binary gradient directions. It includes fuzzy logic in LBPs through sets of fuzzy rules. Support Vector Machines (SVMs) are utilized for the classification of chosen attributes.

Results: Breast cancers in ultrasounds are utilized for the valuation of KNN, SVM-0.1, Naïve Bayes, SVM-0.5 as well as SVM-0.3 approaches in terms of classification precision, sensitivities, specificities, Positive Predictive Values (PPVs) as well as Negative Predictive Values (NPVs). The suggested that SVM-0.3 showed the most optimal performance in all factors.

Conclusion: Breast imaging utilizing mammographies as well as sonographies among women who display local or diffused breast pains are of considerable importance, for the assurance of patients as well as clinicians. But if imaging discoveries are symptomatic of pathologies, biopsies ought not to be put off for long.

Keywords: Breast cancer, mammography, ultrasound, Computer-Aided Detection (CAD), texture features, Minimum Spanning Tree (MST), Fuzzy Local Binary Pattern (FLBP), Support Vector Machine (SVM).

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Article Details

VOLUME: 11
ISSUE: 2
Year: 2016
Page: [84 - 90]
Pages: 7
DOI: 10.2174/1574362411666160627101628
Price: $58

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