Rapid image stitching and computer-aided detection for multipass automated breast ultrasound

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.