The appearance of bone metastasis in patients with breast or prostate cancer makes the skeleton most affected by metastatic cancer. It is estimated that these two cancers lead in 80% of the cases to the appearance of bone metastasis, which is considered the main cause of death. 99mTc-methylene diphosphonate (99mTc-MDP) bone scintigraphy is the most commonly used radionuclide imaging technique for the detection and prognosis of bone carcinoma. With this work, it was intended to develop a new computational solution to extract from 99mTc-MDP bone scintigraphy images quantitative measurements of the affected regions in relation to the non-pathological regions. Hence, the uptake indexes computed from a new imaging exam are compared with the indexes computed from a previous exam of the same patient. Using active shape models, it is possible to segment the regions of the skeleton more prone to be affected by the bone carcinoma. On the other hand, the metastasis is segmented using the region-growing algorithm. Then, the uptake rate is calculated from the relation between the maximum intensity pixel of the metastatic region in relation to the maximum intensity pixel of the skeletal region where the metastasis was located. We evaluated the developed solution using scintigraphic images of 15 patients (7 females and 8 males) with bone carcinoma in two distinct time exams. The bone scans were obtained approximately 3 h after the injection of 740 MBq of 99mTc-MDP. The obtained indexes were compared against the evaluations in the clinical reports of the patients. It was possible to verify that the indexes obtained are according to the clinical evaluations of the 30 exams analyzed. However, there were 2 cases where the clinical evaluation was unclear as to the progression or regression of the disease, and when comparing the indexes, it is suggested the progression of the disease in one case and the regression in the other one. Based on the obtained results, it is possible to conclude that the computed indexes allow a quantitative analysis to evaluate the response to the prescribed therapy. Thus, the developed solution is promising to be used as a tool to help the technicians at the time of clinical evaluation.