||The objectives of this thesis are to provide insight of fundamental mechanisms of acetowhitening effect, upon which the colposcopic diagnosis of human cervical cancer is based and to develop novel quantitative optical imaging technologies supplementing colposcopy to improve its performance in detecting early cancer. Firstly, the temporal characteristics of acetowhitening process are studied on monolayer cell cultures. It is found that the dynamic acetowhitening processes in normal and cancerous cells are significantly different. Secondly, the changes in light scattering induced by acetic acid in intact cells and isolated cellular fractions are investigated by using confocal microscopy and light scattering spectroscopy. The results provide evidence that the small-sized components in the cytoplasm are the major contributors to the acetowhitening effect. Thirdly, a unified Mie and fractal model is proposed to interpret light scattering by biological cells. It is found that light scattering in forward directions is dominated by Mie scattering by bare cells and nuclei, whereas light scattering at large angles is determined by fractal scattering by subcellular structures. Fourthly, an optical imaging system based on active stereo vision and motion tracking is built to measure the 3-D surface topology of cervix and track the motion of patient. The information of motion tracking is used to register the time-sequenced images of cervix recorded during colposcopic examination. The imaging system is evaluated by tracking the movements of cervix models. The results demonstrate that the imaging technique holds the promise to enable the quantitative mapping of the acetowhitening kinetics over cervical surface for more accurate diagnosis of cervical cancer. At last, a calibrated autofluorescence imaging system is instrumented for detecting neoplasia in vivo. It is found that the calibrated autofluorescence signals from neoplasia are generally lower than signals from normal cervical tissue. The imaging system can be used to quantitatively detect abnormal tissue.