TY  - JOUR
JF  - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
VL  - 7066 L
Y1  - 2011///
N1  - cited By 3; Conference of 2nd International Visual Informatics Conference, IVIC 2011 ; Conference Date: 9 November 2011 Through 11 November 2011; Conference Code:87315
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-81255207374&doi=10.1007%2f978-3-642-25191-7_14&partnerID=40&md5=dbff00a2528066b38e9a3b67fc817379
A1  - Hani, A.F.M.
A1  - Arshad, L.
A1  - Malik, A.S.
A1  - Jamil, A.
A1  - Bin, F.Y.B.
SP  - 139
AV  - none
SN  - 03029743
N2  - The ability to measure objectively wound healing is important for an effective wound management. Describing wound tissues in terms of percentages of each tissue colour is an approved clinical method of wound assessment. Wound healing is indicated by the growth of the red granulation tissue, which is rich in small blood capillaries that contain haemoglobin pigment reflecting the red colour of the tissue. A novel approach based on utilizing haemoglobin pigment content in chronic ulcers as an image marker to detect the growth of granulation tissue is investigated in this study. Independent Component Analysis is employed to convert colour images of chronic ulcers into images due to haemoglobin pigment only. K-means clustering is implemented to classify and segment regions of granulation tissue from the extracted haemoglobin images. Results obtained indicate an overall accuracy of 96.88 of the algorithm performance when compared to the manual segmentation. © 2011 Springer-Verlag.
KW  - Chronic wounds; Haemoglobins; Independent components; Ulcers; Visual informatics
KW  -  Color; Diseases; Granulation; Image segmentation; Independent component analysis; Information science; Microcirculation
KW  -  Tissue
IS  - PART 1
TI  - Detection and classification of granulation tissue in chronic ulcers
ID  - scholars1809
EP  - 150
CY  - Selangor
ER  -