This paper presents a new embeddable method for polyp detections in Wireless Capsule Endoscopic - WCE images. this approach con- sists first of extracting candidate polyps within the image using geo- metric considerations about related shape, and second, in classifying (polyp/non-polyp) obtained candidates by a boosting-based method using texture features. The proposed approach has been designed in accordance with the hardware constraints related to FPGA imple- mentation for integration within WCE imaging device. The classification performance of the method have been evaluated on a large dataset of 300 polyps, and 1200 non-polyps images. Experiments show interesting and promising performance: the boosting-based classification is characterized by a sensitivity of 91%, a specificity of 95% and a false detection rate of 4.8%, the detection rate of the over- all processing chain being of 68%. The performance of the boosting- based classification are in accordance with the most recent reference on this particular topic using the same dataset. Building of a dedicated WCE image database should permit the improvement of the global detection rate.