Color feature extraction of HER2 Score 2+ overexpression on breast cancer using Image Processing

Abstract

Abstract. One of the major challenges in the development of early diagnosis to assess HER2 status is recognized in the form of Gold Standard. The accuracy, validity and refraction of the Gold Standard HER2 methods are widely used in laboratory (Perez, et al., 2014). Method determining the status of HER2 (human epidermal growth factor receptor 2) is affected by reproductive problems and not reliable in predicting the benefit from anti-HER2 therapy (Nuciforo, et al., 2016). We extracted color features by methods adopting Statistics-based segmentation using a continuous-scale naïve Bayes approach. In this study, there were three parts of the main groups, namely image acquisition, image segmentation, and image testing. The stages of image acquisition consisted of image data collection and color deconvolution. The stages of image segmentation consisted of color features, classifier training, classifier prediction, and skeletonization. The stages of image testing were image testing, expert validation, and expert validation results. Area segmentation of the membrane is false positive and false negative. False positive and false negative from area are called the area of system failure. The failure of the system can be validated by experts that the results of segmentation region is not membrane HER2 (noise) and the segmentation of the cytoplasm region. The average from 40 data of HER2 score 2+ membrane images show that 75.13% of the area is successfully recognized by the system.

Immunohistochemical HER2 Recognition and Analysis of Breast Cancer Based on Deep Learning

“…Its value range from 0 to 1. The staining intensity of each IHC score is in a different range, and hence this evaluation index is a good candidate feature for further classification [30][31][32][33].…”

Automatic Extraction of Color Features from Landscape Images Based on Image Processing

“…The varied colors would trigger different emotions among the viewers, exert a positive influence on their psychology, and leave them with a rich impression of the landscapes [1][2][3][4][5][6]. The dominant color features determine the presentation effect and visual experience of landscapes [7][8][9]. Considering the diversity and seasonal variation of the colors of plants and buildings, it is worthwhile to study the color configuration of landscapes, and quantify the features of color composition.…”