We used a noise-masking paradigm to investigate the number and properties of chromatic mechanisms involved in image segmentation. Observers were presented with a pattern of dynamic random squares, each independently modulated along a certain direction in DKL color space, either in the isoluminant plane or in the L - M luminance plane. A signal consisting of a rectangular region of squares, oriented horizontally or vertically, was added to the noise. The signal squares were spatially and temporally aligned to the noise squares, excluding the possibility of phase offsets to mediate segmentation performance. Noise and signal color directions were independently varied, and the signal contrast was measured at which an observer could reliably indicate the orientation of the signal. In a second set of experiments, the noise was simultaneously varying in two directions, symmetrically arranged around the signal direction. Masking was generally highest when signal and noise were modulated along the same direction and minimal for orthogonal noise. No difference was found between signals modulated along cardinal directions or intermediate directions. However, measured tuning widths critically depended on the type of noise: Noise modulated along one direction results in narrow tuning, whereas two-sided noise results in broad tuning. A chromatic detection model with multiple broadly tuned mechanisms successfully accounts for the experimental findings, both for narrow and broad tuning curves. Models with four broadly tuned cardinal mechanisms or multiple narrowly tuned mechanisms failed to reproduce the data. Our results suggest an important role for multiple, broadly tuned mechanisms in image segmentation.