A Multiple Classifier System for Classification of Breast Lesions Using Dynamic and Morphological Features in DCE-MRI

被引：0

作者：

Fusco, Roberta ^{[1
]}

Sansone, Mario ^{[1
]}

Petrillo, Antonella ^{[2
]}

Sansone, Carlo ^{[3
]}

机构：

[1] Univ Naples Federico II, Dept Biomed Elect & Telecommun Engn, Naples, Italy

[2] Natl Canc Inst Naples, Fondazione Pascale, Dept Diagnost Imaging, I-80131 Naples, Italy

[3] Univ Naples Federico II, Dept Comp & Syst Engn, Naples, Italy

来源：

STRUCTURAL, SYNTACTIC, AND STATISTICAL PATTERN RECOGNITION | 2012年 / 7626卷

关键词：

breast DCE-MRI; multiple classification system; morphological and dynamic features; IMAGES; DIAGNOSIS; CRITERIA; CANCER; BENIGN;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper we propose a Multiple Classifier System (MCS) for classifying breast lesions in Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI). The proposed MCS combines the results of two classifiers trained with dynamic and morphological features respectively. Twenty-one malignant and seventeen benign breast lesions, histologically proven, were analyzed. Volumes of Interest (VOIs) have been automatically extracted via a segmentation procedure assessed in a previous study. The performance of the MCS have been compared with histological classification. Results indicated that with automatic segmented VOIs 90% of test-set lesions were correctly classified.

引用

页码：684 / 692

页数：9

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