Segmentation Method for Urinary Sediment Images Using Wavelet Transform and Morphological Operations

<p>This segmentation method for urinary sediment images employs a multi-stage approach beginning with preprocessing using wavelet transform for noise reduction and feature enhancement, combined with morphological operations like dilation and erosion to refine image structures. The processed image then undergoes edge detection algorithms (such as Canny or Sobel) to identify boundaries, followed by adaptive threshold segmentation to isolate distinct components. This technique effectively separates various elements in urinary sediment images, providing detailed information for subsequent analysis and diagnostics. The method's implementation typically involves OpenCV or MATLAB functions like wavedec2 for wavelet decomposition, imopen/imclose for morphological processing, and edge/threshold functions for segmentation. Additionally, this approach demonstrates transferability to other image segmentation domains, showing broad application potential in medical imaging and industrial inspection systems.</p>