Encoding patient contours using Fourier descriptors for computer treatment planning.

Area of Science:

• Medical Imaging
• Computational Anatomy
• Radiotherapy Physics

Background:

• Digitizing patient contours from CT scans is crucial for radiotherapy treatment planning.
• High point density in digitized contours necessitates data reduction for efficient use and storage.

Purpose of the Study:

• To introduce an alternative contour encoding method using Fourier descriptors.
• To reduce the number of points representing patient contours while preserving geometric accuracy.

Main Methods:

• Representing contour points (x, y) as complex numbers (x + jy).
• Computing the Discrete Fourier Transform (DFT) of the complex contour data.
• Retaining the 50 lowest frequency components (Fourier descriptors) to represent the contour.
• Reconstructing the contour using the inverse DFT, setting higher frequencies to zero.

Main Results:

• The proposed method effectively encodes complex contours using a reduced set of Fourier descriptors.
• The technique allows for accurate reconstruction of the original contour from the descriptors.
• This approach offers an alternative to traditional interpolation methods for contour simplification.

Conclusions:

• Fourier descriptors provide an efficient and accurate method for encoding and compressing patient contours from CT scans.
• This technique can streamline data handling in radiotherapy treatment planning systems.
• The method has the potential to reduce storage requirements and improve computational efficiency.