A Novel Framework for Ultrasound Image Enhancement to assist segmentation in Computer-Aided Detection of Breast Lesion

The low quality of diagnostic Ultrasound (US) images makes structural identification challenging. It is crucial to enhance these images before application in Computer-Aided Detection (CAD). This study aims to ease breast lesion identification in US images by introducing a novel echo-texture-based technique. We used the basic notion that the unique acoustic impedance of distinct structures results in a textural discrepancy in the US image. Initially, an Acoustic Impedance Index was calculated using the local grayscale variation in the image. Then, we used this parameter to design an adaptive filter that generates an Enhanced Texture Image. The resultant images had a significant perception difference between the lesion and its surrounding. Compared to other enhancement techniques, the No Reference distortion metric had the best values for the proposed method. The enhanced image segmented using Active contour and superpixel-based techniques gives the most accurate lesion boundary. The Dice, Jaccard, and BF score were used to quantify the similarity between the masked image generated after segmentation and outlined by the expert. With the exceptionally reduced probability of false alarm, the proposed technique promises to improve the CAD of breast lesions.