ArticleJournal of pathology informatics2016
A novel method for morphological pleomorphism and heterogeneity quantitative measurement: Named cell feature level co-occurrence matrix.
Article in Journal of pathology informatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed.
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- Prediction of prognosis using artificial intelligence-based histopathological image analysis in patients with soft tissue sarcomas.Cancer medicine · 2024Article
- A multi-class brain tumor grading system based on histopathological images using a hybrid YOLO and RESNET networks.Scientific reports · 2024Article
- Graph 'texture' features as novel metrics that can summarize complex biological graphs.Physics in medicine and biology · 2023Article
- Recurrence prediction in clear cell renal cell carcinoma using machine learning of quantitative nuclear features.Scientific reports · 2023Article
- Development of multiple AI pipelines that predict neoadjuvant chemotherapy response of breast cancer using H&E-stained tissues.The journal of pathology. Clinical research · 2023Article
- Artificial intelligence predicts lymph node metastasis or risk of lymph node metastasis in T1 colorectal cancer.International journal of clinical oncology · 2022Article
- Large-scale extraction of interpretable features provides new insights into kidney histopathology - A proof-of-concept study.Journal of pathology informatics · 2022Article
- Quantitative Evaluation of the Effectiveness of Chemical Peelings in Reducing Acne Lesions Based on Gray-Level Co-Occurrence Matrix (GLCM).Clinical, cosmetic and investigational dermatology · 2022Article
- A Paradigm Shift in Nuclear Chromatin Interpretation: From Qualitative Intuitive Recognition to Quantitative Texture Analysis of Breast Cancer Cell Nuclei.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2021Article
- Protein kinase C zeta suppresses low- or high-grade colorectal cancer (CRC) phenotypes by interphase centrosome anchoring.The Journal of pathology · 2018Article
- Machine Learning Methods for Histopathological Image Analysis.Computational and structural biotechnology journal · 2018Review
- Nuclear features of infiltrating urothelial carcinoma are distinguished from low-grade noninvasive papillary urothelial carcinoma by image analysis.Oncology letters · 2017Article
- Multiscale image analysis reveals structural heterogeneity of the cell microenvironment in homotypic spheroids.Scientific reports · 2017Article
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8 authors.
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Abstract
backgroundRecent developments in molecular pathology and genetic/epigenetic analysis of cancer tissue have resulted in a marked increase in objective and measurable data. In comparison, the traditional morphological analysis approach to pathology diagnosis, which can connect these molecular data and clinical diagnosis, is still mostly subjective. Even though the advent and popularization of digital pathology has provided a boost to computer-aided diagnosis, some important pathological concepts still remain largely non-quantitative and their associated data measurements depend on the pathologist's sense and experience. Such features include pleomorphism and heterogeneity. METHODS AND
resultsIn this paper, we propose a method for the objective measurement of pleomorphism and heterogeneity, using the cell-level co-occurrence matrix. Our method is based on the widely used Gray-level co-occurrence matrix (GLCM), where relations between neighboring pixel intensity levels are captured into a co-occurrence matrix, followed by the application of analysis functions such as Haralick features. In the pathological tissue image, through image processing techniques, each nucleus can be measured and each nucleus has its own measureable features like nucleus size, roundness, contour length, intra-nucleus texture data (GLCM is one of the methods). In GLCM each nucleus in the tissue image corresponds to one pixel. In this approach the most important point is how to define the neighborhood of each nucleus. We define three types of neighborhoods of a nucleus, then create the co-occurrence matrix and apply Haralick feature functions. In each image pleomorphism and heterogeneity are then determined quantitatively. For our method, one pixel corresponds to one nucleus feature, and we therefore named our method Cell Feature Level Co-occurrence Matrix (CFLCM). We tested this method for several nucleus features.
conclusionCFLCM is showed as a useful quantitative method for pleomorphism and heterogeneity on histopathological image analysis.
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