By Ahmad Fadzil Mohamad Hani
In accordance with medical institution scientific trials reading using sign and picture processing recommendations, floor Imaging for Biomedical purposes bridges the space among engineers and clinicians. this article bargains an intensive research of biomedical floor imaging to clinical practitioners because it pertains to the analysis, detection, and tracking of pores and skin stipulations and illness. Written from an engineer's standpoint, the book discusses picture acquisition tools, picture processing, and trend reputation suggestions. It makes a speciality of a number of thoughts utilized in contemporary years for image. Read more...
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Additional info for Surface Imaging for Biomedical Applications
The notation 1/G is usually used to relate values that are linearly proportional to the surface roughness. A total of 72 3D surfaces are scanned for each grade. Since there are five grades evaluated in this study, the acquisition gives a total of 280 surfaces. A surface roughness algorithm is applied to the collected 3D surfaces. 1 lists the surface roughness and its standard deviation for each abrasive grade. The standard deviation represents the measurement precision. As listed in this table, the surface roughness increases proportionally to 1/G.
To prove that the algorithm is invariant to the rotation of the measured surface, the repeatability of the successive scans with rotation angles is evaluated. The definition of repeatability is a closeness between independent results that are obtained through the same method on an identical object, same operator, same conditions, and performed in a short time interval . The absolute differences between the two measurements are used to determine the system repeatability. The repeatability itself can be accepted if 95% of the absolute measurement differences are less than two standard deviations of the measurement difference (2σDiff), as suggested by Bland and Altman .
40] applied FCM clustering to the lip from face skin. Color and shape information are used to classify lip from non-lip regions. Chahir and Elmoataz  applied FCM clustering to differentiate and segment skin regions. Color information is selected as the classification feature. The algorithm is useful for data mining purposes. Zhou et al.  proposed an improved FCM, anisotropic mean shift based fuzzy c-means (AMSFCM). It is used to find the lesion boundary of malignant melanoma in dermoscopy images.