This paper presents a new image segmentation method based on the combination of texture and color informations. The method first computes the morphological color and texture gradients. The color gradient is analyzed taking into account the different color spaces. The texture gradient is computed using the luminance component of the HSL color space. The texture gradient procedure is achieved using a morphological filter and a granulometric and local energy analysis. To overcome the limitations of a linear/barycentric combination, the two morphological gradients are then mixed using a gradient component fusion strategy (to fuse the three components of the color gradient and the unique component of the texture gradient) and an adaptive technique to choose the weighting coefficients. The segmentation process is finally performed by applying the watershed technique using different type of germ images. The segmentation method is evaluated in different object classification applications using the k-means algorithm. The obtained results are compared with other known segmentation methods. The evaluation analysis shows that the proposed method gives better results, especially with hard image acquisition conditions.