Rapid image stitching and computer-aided detection for multipass automated breast ultrasound.
OBJECTIVE
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.
Chang
Ruey-Feng
RF
Department of Computer Science and Information Engineering, Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan 10617.
Chang-Chien
Kuang-Che
KC
Takada
Etsuo
E
Huang
Chiun-Sheng
CS
Chou
Yi-Hong
YH
Kuo
Chen-Ming
CM
Chen
Jeon-Hor
JH
D001331:Automation; D001943:Breast Neoplasms / Q000000981:diagnostic imaging; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005188:False Negative Reactions; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D012372:ROC Curve; D013997:Time Factors; D016217:Ultrasonography, Mammary / Q000379:methods*