Radiation therapy is a treatment modality which seeks to deliver radiation energy to a localized site within a patient, in order to destroy a malignant tumor. The nature of radio-therapy results in dual, conflicting treatment goals: (1) the ability to deliver sufficient energy to a site so as to destroy the growth and, (2) sufficient localization of the energy to minimize the damage of surrounding, healthy tissue. One of the most important aspects of radiation dose treatment planning is the accurate localization of tumor masses. In order for a course of radiation therapy to be successful, the treatment volume must encompass the entire malignant process. Accordingly, the treatment volume must include the primary tumor of interest, as well as the direct and indirect course of the cancer's metastasis. Clinical results have demonstrated that a patient's tolerance to a given dose of radiation decreases as the volume exposed is increased. Therefore, improvements in tumor localization will provide the maximum amount of tissue sparing to the patient while encompassing the necessary target volume. An improved methodology is presented for the localization of tumors. This approach focuses on the integration of MRI and CT imaging data towards the generation of a mathematically optimal, tumor boundary. The solution to this problem is formulated within a framework integrating concepts from the fields of deformable modeling, fuzzy logic, and data fusion. Fuzzy edges derived from CT and MR are combined to form an integrated edge map, which subsequently guides the `growth' of a deformable tumor model. The fusion algorithm yields tumor contours which may be employed directly in the radiation therapy treatment planning process. Results are presented for the case of a phantom data set, with a simulated-implanted tumor, as well as for an actual patient.