Saliency-CCE: Exploiting colour contextual extractor and saliency-based biomedical image segmentation. (March 2023)

• Record Type: Journal Article
• Authors: Zhou, Xiaogen; Tong, Tong; Zhong, Zhixiong; Fan, Haoyi; Li, Zuoyong
• Is Part Of: Computers in biology and medicine. Volume 154(2023)
• Publication Date: 2023-03
• Subjects: Salient object detection -- Colour contextual extractor -- Colour activation mapping -- Biomedical image segmentation -- Skin lesion segmentation -- WBC segmentation; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2023.106551
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: 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 . Highlights: A dual-task method is proposed for saliency detection and medical image segmentation. A colour contextual extractor is proposed to tackle an object localization issue. A novel adaptive threshold strategy is adopted for biomedical image segmentation. Experiments show that our method outperforms several methods on three image datasets.