INTRODUCTION: Analysis of nuclear texture features as a measure of nuclear chromatin changes has been proven to be useful when measured on thin (5-6 microm) tissue sections using conventional 2D bright field microscopy. The drawback of this approach is that most nuclei are not intact because of those thin sections. Confocal laser scanning microscopy (CLSM) allows measurements of texture in 3D reconstructed nuclei. The aim of this study was to develop 3D texture features that quantitatively describe changes in chromatin architecture associated with malignancy using CLSM images. METHODS: Thirty-five features thoughtfully chosen from 4 categories of 3D texture features (discrete texture features, Markovian features, fractal features, grey value distribution features) were selected and tested for invariance properties (rotation and scaling) using artificial images with a known grey value distribution. The discriminative power of the 3D texture features was tested on artificially constructed benign and malignant 3D nuclei with increasing nucleolar size and advancing chromatin margination towards the periphery of the nucleus. As a clinical proof of principle, the discriminative power of the texture features was assessed on 10 benign and 10 malignant human prostate nuclei, evaluating also whether there was more texture information in 3D whole nuclei compared to a single 2D plane from the middle of the nucleus. RESULTS: All texture features showed the expected invariance properties. Almost all features were sensitive to variations in the nucleolar size and to the degree of margination of chromatin. Fourteen texture features from different categories had high discriminative power for separating the benign and malignant nuclei. The discrete texture features performed less than expected. There was more information on nuclear texture in 3D than in 2D. CONCLUSION: A set of 35 3D nuclear texture features was used successfully to assess nuclear chromatin patterns in 3D images obtained by confocal laser scanning microscopy, and as a proof of principle we showed that these features may be clinically useful for analysis of prostate neoplasia.