Microcalcifications are considered the first radiographic indicators of breast cancer and remain significant biomarkers for diagnosis and prognosis. Thus, their reliable detection of mammograms is a key challenge in computer-aided breast imaging. Although deep learning has advanced recently, accurate segmentation of microcalcifications remains challenging, as deep models often miss these fine, low-contrast structures in heterogeneous breast tissue. This study proposes RGC-TinyUNet++, a dual-stage approach combining preprocessing with gamma correction and CLAHE enhancement, followed by ROI extraction focused on 32  \(\times \)  32 extremity-centered patches. Initial segmentation is achieved through constraint guided region growth (RGC), providing a preliminary mask, which is then refined through a lightweight Tiny-UNet model. Evaluated on a mammographic dataset, the results show that our dual-stream strategy not only improves sensitivity to small-scale features but also maintains high precision in segmenting complex anatomical backgrounds. The proposed approach offers great potential to enhance CAD systems for a more accurate and earlier breast cancer diagnosis.