Image　segmentation　is　a　critical　step　in　computer-aided　system　diagnosis.　However,　many　existing　segmentation　methods　are　designed　for　single-task　driven　segmentation,　ignoring　the　potential　benefits　of　incorporating　multi-task　methods,　such　as　salient　object　detection　(SOD)　and　image　segmentation.　In　this　paper,　we　propose　a　novel　dualtask　framework　for　the　detection　and　segmentation　of　white　blood　cells　and　skin　lesions.　Our　method　comprises　three　main　components:　hair　removal　preprocessing　for　skin　lesion　images,　a　novel　color　contextual　extractor　(CCE)　module　for　the　SOD　task,　and　an　improved　adaptive　threshold　(AT)　paradigm　for　the　image　segmentation　task.　We　evaluate　the　effectiveness　of　our　proposed　method　on　three　medical　image　datasets,　demonstrating　superior　performance　compared　to　representative　approaches.