<p>Tubule, a structure that exhibits a clear central lumen surrounded by neoplastic cells, is regarded as one of the fundamental factors underpinning the overall grade of breast cancer in accordance with the Nottingham Histopathology Grading (NHG) system. Despite the availability of many promising artificial intelligence-oriented frameworks, the adoption of such frameworks in real-world clinical settings is very limited, however. Mainly because the proposed methods do not align with the World Health Organization (WHO) clinical protocols for tubule scoring, hindering widespread clinical adoption. The integration of quantitative measurement could be a potential solution. In this study, the tubule and non-tubule are explored in accordance with the standard procedures, such that the tubule measurement method is approached as a textural analysis problem. The proposed measurement method requires no precise segmentation of nuclei and central lumen. It relies mainly on the texture information surrounding the tubule and non-tubule. The proposed measurement method comprises five stages: color normalization, lumen candidate segmentation and nuclei centroid marking, tubule candidate detection, features extraction and selection, and quantitative measurement. Testing and evaluation were performed on 20 breast histopathology slides, comprising 150 tubules and 150 non-tubules, yielding values of 0.9879 and 0.9706 in accuracy and F1-score, respectively. The findings here are found to be comparative, if not, better than some of the existing methods.</p>