Feature vector (All) = LBP + Contrast LBP + LTP + LTrP + HSV histogram matrix (1)
Likewise LTP, LTrP statistical features and color histogram matrix values are calculated and tabulated for all the 200 dermoscopic images in the database.
For each feature vector set from different descriptors like LBP, LTP, LTrP, HSV colour histogram and for concatenated feature vector set (all), each classifier is trained and the estimates are tabulated and compared.
Feature extraction of dermoscopic images is performed, in which the texture features are extracted by Local Binary Patterns (LBP) and its extensions like LTP, LTrP. As dermoscopic images are color images, color features are also extracted using HSV color histogram matrix.
First, the LTrP and CLBP descriptors consistently perform much better than LBP and global features in terms of top features ranked in the feature selection process by stepwise discriminant analysis (SDA).
Hence, local binary pattern (LBP), local tetra pattern (LTrP), and completed local binary pattern (CLBP) are employed in order to find out which local texture patterns are better for IHC image description and multilabel human protein subcellular localization classification.
Local tetra pattern (LTrP) descriptor was proposed by Murala et al.
Take the direction of center pixel [I.sup.1.sub.Dir] ([g.sub.c]) to be equal to "1" in (7) as an example, then LTrP binary coding can be defined by setting 2, 3, and 4 to "1''respectively, and the rest bits are set to "0"; see Figure 3.
Finally computes the histogram of decimal coding to generate LTrP feature vectors based on some specified pattern mappings, and for more details about LTrP, for example, high-order tetra patterns, readers are suggested to read .
Two global features (i.e., Haralick texture and DNA distribution features) and three local pattern features (i.e., LBP, CLBP, and LTrP features) were employed to describe IHC images in this study.
Hence, besides the widely used global features, that is, Haralick texture feature and DNA-protein overlap feature, both CLBP and LTrP are investigated to describe IHC images for the first time and applied to AIPSLP.