Superpixel algorithms oversegment an image by grouping pixels with similar local features such as spatial position, gray level intensity, color, and texture. Superpixels provide visually significant regions and avoid a large number of redundant information to reduce dimensionality and complexity for subsequent image processing tasks. However, superpixel algorithms decrease performance in images with high-frequency contrast variations in regions of uniform texture. Moreover, most state-of-the-art methods use only basic pixel information -spatial and color-, getting superpixels with low regularity, boundary smoothness and adherence. The proposed algorithm adds texture information to the common superpixel representation. This information is obtained with the Hermite Transform, which extracts local texture features in terms of Gaussian derivatives. A local iterative clustering with adaptive feature weights generates superpixels preserving boundary adherence, smoothness, regularity, and compactness. A feature adjustment stage is applied to improve algorithm performance. We tested our algorithm on Berkeley Segmentation Dataset and evaluated it with standard superpixel metrics. We also demonstrate the usefulness and adaptability of our proposal in medical image application.