Wavelet Compression/Reconstruction and Visualization of Pulmonary X-Ray Images for Achieving of Asbestosis Infected Patients Data - Visual Computing: Scientific Visualization and Imaging Systems - page 121

Fig. 6.3 Algorithm for
visualization of the
reconstructed data

Input saved coefficients

Linear interpolation
of wav.coefficients

1D-IDWT

Desired level?

2D-IDWT

Reconstructed image

Histogram analysis with
colour renormalisation

Emphasis of relevant area

Screen: image visualization

Step 3. Nonlinear interpolation of coefficients at places of zeros is performed.
B-spline wavelets were not used, but rather spline interpolation of wavelet
coefficients in the vector. This step takes the most processor time if
programming technique is weak. Recently, several programming tech-
niques were developed to accelerate these operations. Therefore, older
versions of Matlab operate slower than newest.
Step 4. Since vector is 1D type of data, 1D IDWT is performed. Notice that
reconstruction cannot be always performed without resizing vector

introducing zero at the end or deleting last zero.
Step 5. Repetition of Steps 2 to 4 while desired level is reached.
Step 6. 2D-IDWT is performed.
Step 7. Reconstructed image is obtained, which can be used to compare with
original. If the diagnoses from reconstructed image and from the original