@book{scholars9042,
            year = {2017},
             doi = {10.1201/9781315368351},
            note = {cited By 0},
           pages = {23--58},
         journal = {Optical Imaging for Biomedical and Clinical Applications},
       publisher = {CRC Press},
           title = {Skin image analysis granulation tissue for healing assessment of chronic ulcers},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85054212935&doi=10.1201\%2f9781315368351&partnerID=40&md5=336770b302249aa32587ea2948d6a18f},
        abstract = {Chronic wounds expose patients to risks of infection and limb amputation. Leg ulcers affect 1 of the adult population and 3.6 of people above 65 years. Wounds that fail to heal within an expected period develop into ulcers that cause severe pain and expose patients to limb amputation. Ulcer appearance changes gradually as the ulcer tissues evolve throughout the healing process. Dermatologists assess the progression of ulcer healing based on visual inspection of ulcer tissues, which can be inconsistent and subjective. The ability to measure objectively early stages of ulcer healing is thus important to improve clinical decisions and enhance overall effectiveness of the treatment. As the healing progresses, red granulation tissue containing pigment haemoglobin starts to grow from the base of the ulcer gradually replacing the black necrosis and yellow slough, in effect filling the wound cavity and reducing its volume. An approach based on utilising haemoglobin content as an image marker to detect regions of granulation tissue on ulcers surface using digital colour images of chronic ulcers is investigated in this study. A system to detect regions of granulation tissue on ulcers{\^a}?? surface using digital colour ulcer images has been developed, based on this image marker. The system first employs data transformation using independent component analysis to identify haemoglobin on ulcer surface reflecting regions of granulation tissue. Segmentation based on clustering, using fuzzy c-means technique, is then applied to segment the regions of granulation tissue on extracted haemoglobin images. The system can detect clearly granulation tissue regions in reference to ulcer images that are corrupted with white noise. With ulcer images having signal-to-noise ratios higher than 12 dB, better than 96.0 sensitivity, 99.6 specificity and 99.5 accuracy can be achieved. The system when applied on real ulcer images and compared with dermatologists{\^a}?? assessment shows high correlation of 0.961, which indicates a strong similarity between system detection and dermatologists{\^a}?? assessment of granulation tissue. More importantly, the system can identify granulation tissue regions that otherwise cannot be discerned visually in the early stages of ulcer healing. It is hoped that this work contributes to the development of a new objective and noninvasive scheme to assess the healing progression of chronic ulcers in a more precise and reliable way. {\^A}{\copyright} 2018 by Taylor and Francis Group, LLC.},
          author = {Mohamad Hani, A. F. and Arshad, L.},
            isbn = {9781498750387; 9781498750370}
}