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使用可扩展线性 Fisher 判别分析对乳腺 X 线片中的微钙化进行分类。

Classification of micro-calcification in mammograms using scalable linear Fisher discriminant analysis.

机构信息

Aberystwyth University, Aberystwyth, UK.

Norfolk and Norwich University Hospital, Norwich, UK.

出版信息

Med Biol Eng Comput. 2018 Aug;56(8):1475-1485. doi: 10.1007/s11517-017-1774-z. Epub 2018 Jan 25.

DOI:10.1007/s11517-017-1774-z
PMID:29368264
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6061516/
Abstract

Breast cancer is one of the major causes of death in women. Computer Aided Diagnosis (CAD) systems are being developed to assist radiologists in early diagnosis. Micro-calcifications can be an early symptom of breast cancer. Besides detection, classification of micro-calcification as benign or malignant is essential in a complete CAD system. We have developed a novel method for the classification of benign and malignant micro-calcification using an improved Fisher Linear Discriminant Analysis (LDA) approach for the linear transformation of segmented micro-calcification data in combination with a Support Vector Machine (SVM) variant to classify between the two classes. The results indicate an average accuracy equal to 96% which is comparable to state-of-the art methods in the literature. Graphical Abstract Classification of Micro-calcification in Mammograms using Scalable Linear Fisher Discriminant Analysis.

摘要

乳腺癌是女性死亡的主要原因之一。正在开发计算机辅助诊断 (CAD) 系统以协助放射科医生进行早期诊断。微钙化可能是乳腺癌的早期症状。除了检测之外，良性和恶性微钙化的分类对于完整的 CAD 系统至关重要。我们开发了一种使用改进的 Fisher 线性判别分析 (LDA) 方法对分割后的微钙化数据进行线性变换的新方法，结合支持向量机 (SVM) 变体对两种类型进行分类，用于良性和恶性微钙化的分类。结果表明，平均准确率等于 96%，与文献中的最新方法相当。

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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d764/6061516/202f578aafaf/11517_2017_1774_Fig9_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d764/6061516/2cb5089d879b/11517_2017_1774_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d764/6061516/ed01c9fc2e8e/11517_2017_1774_Fig2_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d764/6061516/c025290aab26/11517_2017_1774_Fig6_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d764/6061516/47f60853aa87/11517_2017_1774_Fig8_HTML.jpg
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