A comprehensive review towards segmentation and detection of cancer cell and tumor for dynamic 3D reconstruction

Abstract

Automated cancer cell and tumor segmentation and detection for 3D modeling are still an unsolved research problem in computer vision, image processing and pattern recognition research domains. Human body is complex three-dimensional structure where numerous types of cancer and tumor may exist regardless of shape or position. A three-dimensional (3D) reconstruction of cancer cell and tumor from body parts does not lead to loss of information like 2D shape visualization. Various research methodologies for segmentation and detection for 3D reconstruction of cancer cell and tumor were performed by previous research. However, the pursuit for better methodology for segmentation and detection for 3D reconstruction of cancer cell and tumor are still unsolved research problem due to lack of efficient feature extraction for details surface information, misclassification during training phases and low tissue contrast which causes low detection and precision rate with high computational complexity during detection and segmentation. This research addresses comprehensive and critical review of various segmentation and detection research methodologies for cancer affected cell and tumor in human body in the basis of three-dimensional reconstruction from MRI or CT images. At first, core research background is illustrated highlighting various aspects addressed by this research. After that, various previous methods with advantages and disadvantages followed by various phases used as frameworks exist in the previous research demonstrated by this research. Then, extensive experimental evaluations done by previous research are demonstrated by this research with various performance metrics. At last, this research summarized overall observation on previous research categorized into two aspects, i.e. observation on common research methodologies and recommended area for further research. Overall reviews proposed in this paper have been extensively studied in various research papers which can significantly contribute to computer vision research and can be potential for future development and direction for future research.