This paper presents a new method of segmenting and classifying protein crystallization trial images that were collected using trace fluorescent labeling. Trace fluorescent labeling typically involves fluorescence dye that can re-emit the illumination light at other wavelengths around the principal wavelength. The captured image has a primary color channel with respect to illumination light and fluorescence dye. Crystals will have higher intensity than non-crystal areas. But there might be bright regions that may not be crystals, thereby making inaccurate and not robust trial images classification. In this paper, we utilize the subordinate color channel besides the primary color in the image of trace fluorescently labeled protein solution. This new method extracts proper features and successfully builds a high accuracy classifier with a low rate of misclassification of crystals as non-crystals. We also present a framework that could optimize both image segmentation and classification. In our experiments, we achieved around 94% accuracy with 0.6% misclassification of crystals as non-crystal.