Clutter noise reduction in B-Mode image through mapping and clustering signal energy for better cyst classification

Improving the ultrasound image contrast ratio (CR) and contrast to noise ratio (CNR) has many clinical advantages. Breast cancer detection is one example. Anechoic cysts which fill with clutter noise can be easily misinterpreted and classified as malignant lesions instead of benign. Beamforming techniques contribute to off-axis side lobes and clutter. These two side effects inherent in beamforming are undesirable since they will degrade the image quality by lowering the image CR and CNR. To overcome this issue a new post-processing technique known as contrast enhanced delay and sum (CEDAS) is proposed. Here the energy of every envelope signals are calculated, mapped, and clustered in order to identify the cyst and clutter location. CEDAS reduce clutter inside the cyst by attenuating it from envelope signals before the new B-Mode image is formed. With CEDAS, the image CR and CNR improved by average 12 dB and 1.1 dB respectively for cysts size 2 mm to 6 mm and imaging depth from 40 mm to 80 mm.