Computer-Aided Detection of Architectural Distortion in Prior Mammograms of Interval Cancer (Paperback)

Abstract Architectural distortion is an important and early sign of breast cancer, but because of its subtlety, it is a common cause of false-negative findings on screening mammograms. Screening mammograms obtained prior to the detection of cancer could contain subtle signs of early stages of breast cancer, in particular, architectural distortion. This book presents image processing and pattern recognition techniques to detect architectural distortion in prior mammograms of interval-cancer cases. The methods are based upon Gabor filters, phase portrait analysis, procedures for the analysis of the angular spread of power, fractal analysis, Laws' texture energy measures derived from geometrically transformed regions of interest (ROIs), and Haralick's texture features. With Gabor filters and phase-portrait analysis, 4,224 ROIs were automatically obtained from 106 prior mammograms of 56 interval-cancer cases, including 301 true-positive ROIs related to architectural distortion, and from 52 mammograms of 13 normal cases. For each ROI, the fractal dimension, the entropy of the angular spread of power, 10 Laws' texture energy measures, and Haralick's 14 texture features were computed. The areas under the receiver operating characteristic (ROC) curves obtained using the features selected by stepwise logistic regression and the leave-one-image-out method are 0.77 with the Bayesian classifier, 0.76 with Fisher linear discriminant analysis, and 0.79 with a neural network classifier. Free-response ROC analysis indicated sensitivities of 0.80 and 0.90 at 5.7 and 8.8 false positives (FPs) per image, respectively, with the Bayesian classifier and the leave-one-image-out method. The present study has demonstrated the ability to detect early signs of breast cancer 15 months ahead of the time of clinical diagnosis, on the average, for interval-cancer cases, with a sensitivity of 0.8 at 5.7 FP/image. The presented computer-aided detection techniques, dedicated to accurate detection and localization of architectural distortion, could lead to efficient detection of early and subtle signs of breast cancer at pre-mass-formation stages. Table of Contents: Introduction / Detection of Early Signs of Breast Cancer / Detection and Analysis of Oriented Patterns / Detection of Potential Sites of Architectural Distortion / Experimental Set Up and Datasets / Feature Selection and Pattern Classification / Analysis of Oriented Patterns Related to Architectural Distortion / Detection of Architectural Distortion in Prior Mammograms / Concluding Remarks

摘要 建築扭曲是乳癌的重要且早期徵兆，但由於其微妙性，它是篩檢乳房X光檢查中偽陰性結果的常見原因。在發現癌症之前進行的篩檢乳房X光檢查可能包含早期乳癌的微妙徵兆，特別是建築扭曲。本書介紹了圖像處理和模式識別技術，用於檢測間隔癌症病例的先前乳房X光檢查中的建築扭曲。這些方法基於Gabor濾波器、相位圖分析、用於分析功率角度分佈的程序、分形分析、從幾何變換的感興趣區域（ROI）中獲得的Laws紋理能量測量以及Haralick紋理特徵。使用Gabor濾波器和相位圖分析，從56個間隔癌症病例的106個先前乳房X光檢查中自動獲得了4,224個ROI，其中包括與建築扭曲相關的301個真陽性ROI，以及來自13個正常病例的52個乳房X光檢查。對於每個ROI，計算了分形維度、功率角度分佈的熵、10個Laws紋理能量測量和Haralick的14個紋理特徵。使用逐步邏輯回歸選擇的特徵和留一圖像法獲得的接收器操作特性（ROC）曲線下面積分別為0.77（貝葉斯分類器）、0.76（Fisher線性判別分析）和0.79（神經網絡分類器）。自由響應ROC分析顯示，使用貝葉斯分類器和留一圖像法時，偽陽性（FP）每張圖像5.7和8.8個的靈敏度分別為0.80和0.90。本研究證明了在臨床診斷時間之前15個月，對於間隔癌症病例，能夠以0.8的靈敏度在每張圖像5.7個FP的情況下檢測到乳癌的早期徵兆。所提出的計算機輔助檢測技術，專門用於準確檢測和定位建築扭曲，可以有效檢測乳癌的早期和微妙徵兆，即在形成腫塊之前。目錄：引言/檢測乳癌的早期徵兆/檢測和分析定向模式/檢測建築扭曲的潛在位置/實驗設置和數據集/特徵選擇和模式分類/與建築扭曲相關的定向模式分析/在先前乳房X光檢查中檢測建築扭曲/結論