Edge-Inferenced Contrast Enhancement of Image Volumes
Dept. of Neurology, University of Minnesota
Modeling & Analysis
Abstract
Contrast Limited Adaptive Histogram Equalization (CLAHE) is effective for increasing the visual contrast of medical images, but is sometimes criticized for excessively magnifying image noise [1][2]. Traditional adaptive histogram equalization (AHE) maps each pixel to an intensity proportional to the percentile rank of its original intensity in a surrounding window. Thus the slope of the mapping function is proportional to the local intensity histogram. CLAHE avoids undesirable effects by clipping the maximum slope of the derived mapping. The novelty introduced here, Edge-Inferenced Adaptive Histogram Equalization (EIAHE), makes the slope proportional to the mean edge strength at that intensity in a local window. EIAHE aims for strong separation of clustered intensity regions while limiting amplification of noise. Its effectiveness depends on the reliability of localizing edge candidates in intensity space. Our implementation utilizes a relatively simple gradient estimator introduced in [3]. EIAHE conceptually differs from the multi-scale algorithm of [2] because it explicitly localizes gradients to particular regions of intensity space, while our implementation is applicable to 3D volumes.
Methods
The EIAHE transformation method is defined by three operators: CLAHEclassic (standard CLAHE on 3D regions), Grad based on [3], and EIAHEcombo. EIAHEcombo takes two input images, one representing the image to be mapped and the other representing edge strengths, and performs the transformation described above. For computational efficiency in 3D volumes, it is necessary to approximate AHE style computation by interpolating between a grid of local histograms in an analogous manner to the recipe for 2D images introduced in [1]. The overall processing sequence consists of the following steps: 1) CLAHEclassic is applied to the original image to normalize the contrast magnitude at each image intensity; 2) Grad is applied to the output of step1; 3) EIAHEcombo is applied to the original image together with the output of step2.
Results and Discussion
Computation time on a 4MB image is approximately 20 seconds on 1.4MHz Linux PC. The included figures show before/after slices obtained by processing a T1-weighted and T2-weighted MRI volume, along with the output of CLAHEclassic for the T2-weighted volume. Subjective comparison of the EIAHE and CLAHEclassic results indicates that EIAHE produces images that are less grainy, with lower visible noise, but provides weaker contrast in image regions where sufficiently strong edges are not detected.
The significance of the approachs described here lies in the fact that they are fully automatic, efficient, and make few assumptions about the nature of the underlying image. Though now classical in the (2D) Radiology literature, CLAHE itself deserves to be better known in the Neuroimaging community, while EIAHE addresses some previously expressed concerns regarding CLAHE. Either approach may serve as an effective visualization tool, while EIAHE, especially, can be a pre-processing step for other algorithms that are driven by significant edges, such as brain extraction, segmentation, and registration.
References
1. S.M. Pizer et al. "Adaptive Histogram Equalization and Its Variations". CVGIP, 1987.2. Y. Jin, et al. "Contrast Enhancement by Multi-Scale Histogram Equalization," Proc. SPIE, 2001.
3. M. Brejl, M., Sonka. "Directional 3D Edge Detection in Anisotropic Data", CVIU, 2000.