Ultrasonography　is　a　valuable　diagnosis　method　for　thyroid　nodules.　Automatically　discriminating　benign　and　malignant　nodules　in　the　ultrasound　images　can　provide　aided　diagnosis　suggestions,　or　increase　the　diagnosis　accuracy　when　lack　of　experts.　The　core　problem　in　this　issue　is　how　to　capture　appropriate　features　for　this　specific　task.　Here,　we　propose　a　feature　extraction　method　for　ultrasound　images　based　on　the　convolution　neural　networks　(CNNs),　try　to　introduce　more　meaningful　and　specific　features　to　the　classification.　A　CNN　model　trained　with　ImageNet　data　is　transferred　to　the　ultrasound　image　domain,　to　generate　semantic　deep　features　under　small　sample　condition.　Then,　we　combine　those　deep　features　with　conventional　features　such　as　Histogram　of　Oriented　Gradient　(HOG)　and　Scale　Invariant　Feature　Transform　(SIFT)　together　to　form　a　hybrid　feature　space.　Furthermore,　to　make　the　general　deep　features　more　pertinent　to　our　problem,　a　feature　subset　selection　process　is　employed　for　the　hybrid　nodule　classification,　followed　by　a　detailed　discussion　on　the　influence　of　feature　number　and　feature　composition　method.　Experimental　results　on　1037　images　show　that　the　accuracy　of　our　proposed　method　is　0.929,　which　outperforms　other　relative　methods　by　over　10%.