Chronic wounds (CWs) detection and diagnosis are deemed as significant social and economic problems in society, especially regarding elderly persons and bedridden. These problems and challenges due to their unpredictive healing procedure at an expected time. The cost of the CW diagnosis and treatment is very high as compared with other types of diseases. This paper presents a healing assessment computer-aided system (CAD) for CW. The proposed CAD system is based on extracting various significant features to help in detecting different tissue types from various CW categories. The proposed system extracted different color and texture features and then returned with the most significant features by applying the non-negative matrix factorization (NMF) technique. The resulting features are fused and supplied to the gradient boosted trees (GBT) technique to distinguish different types of tissues. After that, the healing percentage from each type of CW tissues are calculated. Finally, the proposed CAD system assesses the healing status of the CW. We trained and tested the proposed CAD system on 341 images from the Medetec CW dataset. The proposed CAD system fulfilled on average 94% accuracy. The experimental results are higher than all tested state-of-the-art techniques, which indicate promising results.