Breast Ultrasound Images Enhancement Using Gray Level Co-Occurrence Matrices Quantizing Technique

This article demonstrates a simple and robust enhancement method for breast ultrasound images based on quantizing the gray level intensities. The quantizing is performed using gray level co-occurrence matrices; that calculates the neighbor intensity interrelation according to the number of gray intensities per level. In this research we divide the gray scale to 15 levels. Gaussian and median filtrations were implemented and iterated 7 times, at each level, using a kernel size of 11x11. Finally each filtered level is translated back to its original location. This quantization technique significantly smoothes the breast ultrasound image while preserving edges. The performance of the algorithm has been compared with the standard filtering technique and evaluated using second order statistical methods. Test and synthesized images with induced speckle noise were used for technique verification and automatic edge detection. The proposed method demonstrates high filtration quality performance and edge preservation compared to the standard overall image filtration method. The textures were preserved with slight blurring. The proposed method introduces a new enhancing technique based on second order dependency matrices quantizing technique.