1. Purpose and Focus: Scikit-image is primarily focused on image processing tasks such as image segmentation, filtering, and transformation, while scikit-learn is focused on machine learning algorithms for tasks like classification, regression, and clustering.
2. Data Type: Scikit-image works with image data represented as multidimensional arrays of pixel values, whereas scikit-learn works with tabular data in the form of feature matrices and target vectors.
3. Algorithms: Scikit-image provides algorithms specific to image processing, such as edge detection, morphology, and feature extraction, while scikit-learn offers a wide range of machine learning algorithms like random forests, support vector machines, and k-nearest neighbors.
4. Application: Scikit-image is commonly used in computer vision applications for tasks like object recognition and image enhancement, while scikit-learn is used for building predictive models in various domains such as healthcare, finance, and marketing.
5. Community and Support: Scikit-learn has a larger community and extensive documentation compared to scikit-image, which may result in more readily available resources and support for users.
6. Ease of Use: Due to its focus on specific image processing tasks, scikit-image may have a steeper learning curve for users who are not familiar with image processing concepts, while scikit-learn's more general machine learning focus may be easier for beginners to grasp.

In Summary, scikit-image specializes in image processing tasks using multidimensional arrays, while scikit-learn focuses on machine learning algorithms for tabular data, with differences in algorithms, applications, community support, and ease of use.