# Special Issue Texture and Color in Image Analysis
## 3 Applications  
Biomedical image analysis received much attention in this Special Issue with a total of four papers accepted. In [5], [PERSON] et al. addressed segmentation of mammograms as an optimization problem and considered three metaheuristic approaches: simulated annealing, genetic algorithms, and a bat algorithm. They used Dunn index as the fitness function to evaluate segmented regions, which were characterized by clinical data, intensity, texture, and shape descriptors. Then, for the diagnosis of breast cancer lesions, they proposed a new artificial immune system (AIS). The performance of the metaheuristic algorithms was compared to intensity-based segmentations obtained using the Otsu method, and the outcomes of the AIS were evaluated on six datasets. [PERSON] et al. [6] investigated automated grading of prostate cancer from histology images. Their method is based on four steps: (1) segmentation of the input images via \(k\)-means; (2) separation of the touching cells through watershed transform; (3) extraction of morphological features; (4) SVM-based classification into four Gleason grade groups--grade 3, grade 4, grade 5, and benign. [PERSON] and [PERSON] [7] explored assessing a person's condition from capacitive images of their hair and skin. Concretely, they attempted to determine whether a capacitive imaging sensor in combination with image processing algorithms such as gradient-based segmentation, gray level co-occurrence matrix, and normalized cross-correlation could be used in different hair and skin analysis tasks that are of great interest to the cosmetic and pharmaceu industries, namely, the detection of skin polygons, the estimation of the bounding wrinkles length, and the observation of hair water sorption capabilities. The experimental results indicate that the proposed approach can be successful for detecting and tracking skin artifacts (e.g., wrinkles, moles, or scars) as well as skin age classification. Evidence indicates that capacitive imaging can also be applied to hair water loss studies. In [8], [PERSON] et al. examined whether additional digital intraoral radiography (DIR) image preprocessing based on texture analysis improves the recognition and differentiation of periapical lesions. They applied several texture models such as co-occurrences, first-order features, run-length matrices, gray-tone difference matrices, and local binary patterns to transform DIR images into feature maps. To improve the recognition of osteolytic and sclerotic lesions, the feature maps were further processed through \(k\)-means clustering. The ability of the proposed approach to yield information about the shape of a structure, its pattern, and adequate contrast was validated by two radiologists independently. The experimental results showed that the application of feature mapping to radiographic dental images constitutes a promising tool for the refinement and possible differentiation of periapical lesions. Three papers investigated industrial applications. [PERSON] et al. [9] presented a computer vision system for counting small metal parts produced by electrodeposition. This manufacturing procedure is common in the fashion field and, since the raw materials are usually gold and silver, it is of paramount importance to reduce the amount of waste. The devised method employs a combination of image thresholding and morphological operations. [PERSON] et al. [10] investigated online defect detection in the production of steel plates. This is a fairly common problem in the industry, and requires both speed and high recognition accuracy. The proposed solution relies on Multiblock Local Binary Patterns (MB-LBP), which the authors found to be superior to other methods such as the Gray-Level Co-occurrence Matrix (GLCM), the Scale-Invariant Feature Transform (SIFT), and the speeded up robust feature (SURF). [PERSON] et al. 's work [11] is concerned with the problem of measuring the period length and the skew angle patterns of textile cutting pieces.