Discretized data pattern in endoscopic gastritis images using Dynamic Window and Pairwise Gini Criterion.

Curent standard clinical procedure for gastritis is via endoscopy by performing an invasive procedure. The procedure takes tissue samples from patient's antrum and diagnoses based on pathological evaluation. Several non-invasive computer-aided visualization studies have been conducted to perform feature extraction from the endoscopic gastritis images.

Computer aided detection of breast density and mass, and visualization of other breast anatomical regions on mammograms using graph cuts.

Breast cancer mostly arises from the glandular (dense) region of the breast. Consequently, breast density has been found to be a strong indicator for breast cancer risk. Therefore, there is a need to develop a system which can segment or classify dense breast areas.

Image Cytometry Data From Breast Lesions Analyzed using Hybrid Networks.

The treatment and therapy to be administered on breast cancer patients are dependent on the stage of the disease at time of diagnosis. It is therefore crucial to determine the stage at the earliest time possible. Tumor dissemination to axillary lymph nodes has been regarded as an indication of tumor aggression, thus the stage of the disease.

Analysis of image cytometry data of fine needle aspirated cells of breast cancer patients: a comparison between logistic regression and artificial neural networks.

Image flow cytometry data of aspirated tumour cells from 102 patients with breast cancer were analysed and used as prognostic markers in an attempt to predict involvement of axillary lymph nodes and histological grade using logistic regression. Prediction was 70% for both nodal status and histological analyses. The outcome of this study is compared to an earlier study using the same cytological information to obtain prediction using a neural approach.