Computer-aided Lesion Detection and Robust Diagnosis for Breast Ultrasound
Date Issued
2013
Date
2013
Author(s)
Yang, Min-Chun
Abstract
Breast cancer is the second leading cause of death for female as latest reported from the American Cancer Society (ACS). Since the exact causes of the disease remain unknown, early detection and diagnosis can effectively and efficiently increase successful clinical treatment while reducing unnecessary biopsies of breast masses. Ultrasound imaging is one of widely used non-invasive modality to detect and classify abnormalities of breast lesions. Recently, many researchers are dedicated to develop computer-aided systems (i.e., CADx and CADe) for breast cancer detection and diagnosis to aid radiologists in interpreting breast ultrasound (BUS) images. Compared to traditional heavy and large ultrasonic machines for screening BUS images, portable PC-based breast ultrasound (BUS) imaging systems can be adopted to improve the patient throughput. Herein, we use PC-based ultrasound machine Terason t3000 (Terason Ultrasound, Burlington, MA, USA) with free-hand probe to acquire the source BUS images for later analysis. To effectively address the big data issue while developing clinical applications for whole breast lesion detection, the proposed pixel classification based on naïve Bayes classifier is used to categorize the normal or lesion objects and two-phase lesion selection scheme is adopted to pick out all the suspected lesions with a lower false-positive rate. The proposed system present 93.94% with 4.22 false-positive per hundred slices and is effective for the radiologists to perform the second examination by merely reviewing the detected lesion objects of the proposed system. In order to reduce the biopsies of the breast masses, many researches devote to develop computer-aided diagnostic system using GLCM-based textural features for tumor diagnosis. Nonetheless, these texture analyses did not consider varied parameter setting of different ultrasonic devices might result in large variation of diagnostic performances across the sonographic platforms. Therefore, we aim at developing a robust computer-aided diagnostic system for BUS images based on the invariant gray-scale transform (i.e., ranklet transform). While multi-resolution features are extracted from the ranklet transformed BUS images for texture analysis, the diagnostic performances with the invariant texture features outperform those with state-of-art texture analyses. We conducted experiments in terms of receiver operating characteristic (ROC) analysis, the AUC values derived from the area under the curve for the three databases are 0.918 (95% confidence interval [CI], 0.848 to 0.961), 0.943 (95% CI, 0.906 to 0.968) and 0.934 (95% CI, 0.883 to 0.961), respectively. The experiments reveal the texture analyses using ranklet transform are less sensitive to different ultrasonic devices and properly adopted for designing a robust system for tumor diagnosis.
Subjects
乳癌
可攜式超音波
簡易貝式分類器
腫瘤偵測
可靠式電腦輔助診斷
SDGs
Type
thesis
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