Besides diagnosis of fatty liver disease(FLD) using multiple medical imaging techniques in clinic,accurate fat quantification of liver tissue slice, especially the fat droplets measurement, is still a critical indicator in related pathological researches. Stained by hematoxylin-eosin(HE), different tissue components with different colors need to be identified and measured manually in conventional approaches. Automated liver fat quantification of HE stained images remains challenging because forms and distributions of fat are extremely irregular with no clear boundaries, especially in conducting high-throughput analysis which demands quick processing and higher accuracy for the reference of pathologists. To solve this problem, we propose an automated liver fat quantifications pipeline of HE stained images based on pixel-wise clustering, which firstly extracts high-relevant pixel-level features with color mode transformation, then locates boundaries between nuclei, fat and other components by clustering image pixels in an unsupervised mode, and finally identifies indicative fat droplets based on a set of morphological criteria. The pipeline was verified in analysis of multifold fatty liver treatment assays, with experimental results showing high accuracy and adaptability in fat droplets quantification despite data variance. Quantitative indicators provide a reliable evidence for relevant pathological researches or therapy selection, in which number and average area of indicative fat droplets increased sharply in severe and moderate-grade FLD respectively. Those indicators might be utilized as surrogate biomarkers for further researches. Another diagnosis of fatty liver disease (FLD) using multiple medical imaging techniques in clinic, accurate fat quantification of liver tissue slices, especially the fat droplets measurement, is still a critical indicator in related pathological researches. Stained by hematoxylin-eosin (HE), different tissue components with different colors need to be identified and measured manually in conventional approaches. Automated liver fat quantification of HE stained images of persistent challenging because forms and distributions of fat are extremely irregular with no clear boundaries, especially in conducting high-throughput analysis of demands quick processing and higher accuracy for the reference of pathologists. To solve this problem, we propose an automated liver fat quantifications pipeline of HE stained images based on pixel-wise clustering, which has extracts high-relevant pixel-level features with color mode transformation, then locates boundaries between nuclei, fat and other componen ts by clustering image pixels in an unsupervised mode, and finally may indicate fat droplets based on a set of morphological criteria. The pipeline was verified in analysis of multifold fatty liver treatment assays, with experimental results showing high accuracy and adaptability in fat droplets quantification data variance. Quantitative indicators provide a reliable evidence for relevant pathological researches or therapy selection, in which number and average area of ​​indicative fat droplets increased sharply in severe and moderate-grade FLD respectively. Those indicators might be utilized as surrogate biomarkers for further researches.