In the field of medical imaging, fully automated processing of medical images has become the need of the hour. This demand has led to the development of several techniques to detect abnormalities in digital images. This research paper aims to develop an automatic system for detecting lesion in digital cervical Colposcopic images. Through research works reviewed significantly, available cervical cancer detection schemes employ Discrete Wavelet Transform (DWT) as the major feature extraction technique and active contour technique as a lesion detection technique. As discrete wavelet transform is a frequency domain analysis technique, features are extracted and stored in the database which is required for lesion detection. Further, the affected area in the cervical image is detected by active contour method. Simulation results are obtained by using K Nearest Neighbor (KNN) classifier which provides visual information about lesion in the cervical image with increased accuracy, precision, sensitivity, specificity in comparison to the results obtained by using statistical features, laws and wavelets.