Saliency-CCE: Exploiting colour contextual extractor and saliency-based biomedical image segmentation

Biomedical image segmentation is one critical component in computer-aided system diagnosis. However, various non-automatic segmentation methods are usually designed to segment target objects with single-task driven, ignoring the potential contribution of multi-task, such as the salient object detection (SOD) task and the image segmentation task. In this paper, we propose a novel dual-task framework for white blood cell (WBC) and skin lesion (SL) saliency detection and segmentation in biomedical images, called Saliency-CCE. Saliency-CCE consists of a preprocessing of hair removal for skin lesions images, a novel colour contextual extractor (CCE) module for the SOD task and an improved adaptive threshold (AT) paradigm for the image segmentation task. In the SOD task, we perform the CCE module to extract hand-crafted features through a novel colour channel volume (CCV) block and a novel colour activation mapping (CAM) block. We first exploit the CCV block to generate a target object's region of interest (ROI). After that, we employ the CAM block to yield a refined salient map as the final salient map from the extracted ROI. We propose a novel adaptive threshold (AT) strategy in the segmentation task to automatically segment the WBC and SL from the final salient map. We evaluate our proposed Saliency-CCE on the ISIC-2016, the ISIC-2017, and the SCISC datasets, which outperform representative state-of-the-art SOD and biomedical image segmentation approaches. Our code is available at https://github.com/zxg3017/Saliency-CCE.