Multicolor fluorescence in situ hybridization (M-FISH) is a multichannel imaging technique for rapid detection of chromosomal abnormalities. It is a critical and challenging step to segment chromosomes from M-FISH images toward better chromosome classification. Recently, several fuzzy C-means (FCM) clustering-based methods have been proposed for M-FISH image segmentation or classification, e.g., adaptive fuzzy C-means (AFCM) and improved AFCM (IAFCM), but most of these methods used only one channel imaging information with limited accuracy. To improve the segmentation for better accuracy and more robustness, we proposed an FCM clustering-based method, denoted by spatial- and spectral-FCM. Our method has the following advantages: (1) it is able to exploit information from neighboring pixels (spatial information) to reduce the noise and (2) it can incorporate pixel information across different channels simultaneously (spectral information) into the model. We evaluated the performance of our method by comparing with other FCM-based methods in terms of both accuracy and false-positive detection rate on synthetic, hybrid, and real images. The comparisons on 36 M-FISH images have shown that our proposed method results in higher segmentation accuracy (0.9382±0.0250) and a lower false-positive ratio (0.1042±0.1481) than conventional FCM (accuracy: 0.9210±0.0457, and false-positive ratio: 0.1389±0.1899) and the IAFCM (accuracy: 0.8730±0.2438 and false-positive ratio: 0.2438±0.2438) methods by incorporating both spatial and spectral information from M-FISH images.