Introduction. The ability to reliably differentiate neoplastic from nonneoplastic specimen and ascertain the tumour grade of diffusely infiltrating gliomas (DIGs) is often challenging. Aims and Objective. To evaluate utility of image morphometry in identifying DIG areas and to predict tumour grade. Materials and Methods. Image morphometry was used to analyze the following nuclear features of 30 DIGs and 10 controls (CG): major axis of nucleus (MAJX), minor axis of nucleus (MINX), nuclear area (NA), nuclear perimeter (NP), nuclear roundness (NR), nuclear density (ND), and percentage of total nuclear area (%TNA). Results. Statistically significant differences in all parameters, except NR, were observed between all groups, with strong positive correlation with tumour grade ( ). The mean values were maximum for HGG and minimum for CG. For NR, the difference between CG/HGG was statistically significant, unlike CG/LGG and LGG/HGG. It was observed that NA distributions for CG were nearly Gaussian type with smaller range, while gliomas displayed erratic pattern with larger range. NA and NP exhibited strong positive correlation with ND. Conclusion. Image morphometry has immense potential in being a powerful tool to distinguish normal from neoplastic tissue and also to differentiate LGG from HGG cases, especially in tiny stereotactic biopsies. 1. Introduction The global annual incidence of primary malignant central nervous system (CNS) tumors is around 37 per million for male and 26？per？million for female [1, 2], with gliomas constituting the majority. In the past several years, it has been well established that quite a few clinical and histopathological parameters are helpful in predicting the clinical outcome of cancer patients. Currently, well-established techniques like morphometry, stereology, static image, and flow cytometry are routinely used in diagnostic quantitative pathology. The potential significance of these techniques includes the objective distinction between benign, borderline, and malignant lesions; the objective grading of invasive tumors; and the prediction of prognosis and therapeutic response. Computer-assisted image analysis is a new powerful tool for high-precision measurement of different facets of tumor cells to achieve similar goals [3–7]. To date only few studies have utilized nuclear morphometric measurements, like mean major axis (MAJX), minor axis (MINX), nuclear area (NA), nuclear perimeter (NP), and roundness of nucleus (NR), to determine the nuclear size and shape profiles in neoplastic tissues in CNS tumors [6–12]. The initial
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