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Rapid image stitching and computer‐aided detection for multipass automated breast ultrasound
Author(s) -
Chang RueyFeng,
ChangChien KuangChe,
Takada Etsuo,
Huang ChiunSheng,
Chou YiHong,
Kuo ChenMing,
Chen JeonHor
Publication year - 2010
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.3377775
Subject(s) - image stitching , computer science , artificial intelligence , computer vision , preprocessor , pentium , breast ultrasound , feature (linguistics) , image quality , image processing , image registration , ultrasound , pattern recognition (psychology) , mammography , image (mathematics) , radiology , medicine , linguistics , philosophy , cancer , parallel computing , breast cancer
Purpose: Breast ultrasound (US) is recently becoming more and more popular for detecting breast lesions. However, screening results in hundreds of US images for each subject. This magnitude of images can lead to fatigue in radiologist, causing failure in the detection of lesions of a subtle nature. In this study, an image stitching technique is proposed for combining multipass images of the whole breast into a series of full‐view images, and a fully automatic screening system that works off these images is also presented. Methods: Using the registration technique based on the simple sum of absolute block‐mean difference (SBMD) measure, three‐pass images were merged into full‐view US images. An automatic screening system was then developed for detecting tumors from these full‐view images. The preprocessing step was used to reduce the tumor detection time of the system and to improve image quality. The gray‐level slicing method was then used to divide images into numerous regions. Finally, seven computerized features—darkness, uniformity, width‐height ratio, area size, nonpersistence, coronal area size, and region continuity—were defined and used to determine whether or not each region was a part of a tumor. Results: In the experiment, there was a total of 25 experimental cases with 26 lesions, and each case was composed of 252 images (three passes, 84 images/pass). The processing time of the proposed stitching procedure for each case was within 30 s with a Pentium IV 2.0 processor, and the detection sensitivity of the proposed CAD system was 92.3% with 1.76 false positives per case. Conclusions: The proposed automatic screening system can be applied to the whole breast images stitched together via SBMD‐based registration in order to detect tumors.