Medical Image Segmentation and Saliency Detection Through a Novel Color Contextual Extractor

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.