Medical image segmentation is a challenging process due to possible image over-segmentation and under-segmentation (leaking). The CALM medical image segmentation system is constructed with an innovative scheme that cascades threshold level-set and region-growing segmentation algorithms using Union and Intersection set operators. These set operators help to balance the over-segmentation rate and under-segmentation rate of the system respectively. While adjusting the curvature scalar parameter in the threshold level-set algorithm, we observe that the abrupt change in the size of the segmented areas coincides with the occurrences of possible leaking. Instead of randomly choose a value or use the system default curvature scalar values, this observation prompts us to use the following formula in CALM to automatically decide the optimal curvature values γ to prevent the occurrence of leaking : ∂2S/∂γ2 >= M, where S is the size of the segmented area and M is a large positive number. Motivated for potential applications in organ transplant and analysis, the CALM system is tested on the segmentation of the kidney regions from the Magnetic Resonance images taken from the National University Hospital of Singapore. Due to the nature of MR imaging, low-contrast, weak edges and overlapping regions of adjacent organs at kidney boundaries are frequently seen in the datasets and hence kidney segmentation is prone to leaking. The kidney segmentation accuracy rate achieved by CALM is 22% better compared with those achieved by the component algorithms or the system without leaking detection mechanism. CALM is easy-to-implement and can be applied to many applications besides kidney segmentation.