Ultrasound imaging is a noninvasive technique well-suited for detecting abnormalities like cysts, lesions and blood clots. In order to use 3D ultrasound to visualize the size and shape of such abnormalities, effective boundary detection methods are needed. A robust boundary detection technique using a nearest neighbor map (NNM) and applicable to multi-object cases has been developed. The algorithm contains three modules: pre-processor, main processor and boundary constructor. The pre-processor detects the object(s) and obtains geometrical as well as statistical information for each object, whereas the main processor uses that information to perform the final processing of the image. These first two modules perform image normalization, thresholding, filtering using median, wavelet, Wiener and morphological operation, estimation and boundary detection of object(s) using NNM, and calculation of object size and their location. The boundary constructor module implements an active contour model that uses information from previous modules to obtain seed-point(s). The algorithm has been found to offer high boundary detection accuracy of 96.4% for single scan plane (SSP) and 97.9 % for multiple scan plane (MSP) images. The algorithm was compared with Stick's algorithm and Gibbs Joint Probability Function based algorithm and was found to offer shorter execution time with higher accuracy than either of them. SSP numerically modeled ultrasound images, SSP real ultrasound images, MSP phantom images and MSP numerically modeled ultrasound images were processed.