The paper presents the designed software CAMI (Computerized Analysis of Microscopic Images) for a digital reconstruction of the diversiform glands seen in chronic inflammatory gastric mucosa, and for automated recognition and quantization of the immunoreactive neuroendocrine (NE) cells appearing within mucosal glands. Digital reconstruction of the individual gastric gland is difficult due to variable shapes of the glandular cross-sections. Fifteen gastric biopsy specimens representing chronic gastritis were stained routinely with H+E and immunohistochemically with 3 NE markers: Chromogranin A, Somatostatin and Serotonin. Two expert pathologists counted manually the NE cells with the light microscope in 4 types of glandular cross-sections: round, short- oblique, long- oblique and longitudinal. The automated counting of the NE cells was performed on the digital images presenting the same microscopic areas which were selected for the manual reading. The first step of image analysis was concerned to the cell extraction and recognition of the cytoplasmic immunoreactivity. The unstained nuclei of the NE cells were spotted by the sequential thresholding algorithm combined with the artificial neural network of Support\Vector Machine (SVM) type. The second step of image analysis comprised reconstruction of the glands. The presumed shape of each gastric gland was defined by the cellular lining of viewed glandular cross-section. The designed algorithm for gland reconstruction was based on the cell masks. The third step of analysis dealt the cell counting. Every recognized gland with the face cells was used for the NE cell evaluation. The results of the automated quantization compared with manual counting results for the number of NE cells showed high concordance in 3 types of glandular cross-sections: round, short- and long- oblique. A difference noticed in the results of the longitudinal glands should be verified in the extended study. The designed software CAMI is more adequate for the gland recognition with an discontinuous gland face seen in the immunohistochemical digital images, which appear to be a difficult problem for the accurate automated analysis of the cellular component of glands.