Prewitt Edge Detection for Mammogram Analysis Using MATLAB

Application of Prewitt Edge Detection in Mammogram Image Processing

Edge detection in mammogram X-ray images is a crucial step in medical image analysis, assisting physicians in identifying potential lesion areas. The Prewitt operator, as a classical edge detection method, enhances edge features by calculating image gradients in both horizontal and vertical directions.

Implementing Prewitt edge detection for mammograms in MATLAB typically involves several key steps. The process begins with reading mammogram image data using functions like imread(). Since medical images often have high dynamic range, preprocessing steps such as contrast enhancement with imadjust() or histeq() may be necessary. The core implementation applies two convolutional kernels: horizontal Prewitt operator [-1 0 1; -1 0 1; -1 0 1] and vertical Prewitt operator [-1 -1 -1; 0 0 0; 1 1 1] using imfilter() or conv2() functions. Gradient magnitude is then computed by combining both directional gradients using the formula sqrt(Gx² + Gy²), followed by normalization for visualization.

In mammogram processing, Prewitt operator offers advantages over other edge detection methods due to its computational simplicity and moderate noise resistance. However, since tissue edges in mammograms are typically模糊, post-processing techniques like threshold segmentation with graythresh() or morphological operations with imopen()/imclose() may be required to optimize edge extraction. Practical implementation must also consider factors such as image resolution and noise levels, which can be addressed using medfilt2() for noise reduction before edge detection.